app.py

import os

import gradio as gr
import pandas as pd
from gradio_image_annotation import image_annotator

from tools.auth import authenticate_user
from tools.aws_functions import download_file_from_s3, upload_log_file_to_s3
from tools.config import (
    ACCESS_LOG_DYNAMODB_TABLE_NAME,
    ACCESS_LOGS_FOLDER,
    ALLOW_LIST_PATH,
    AWS_ACCESS_KEY,
    AWS_REGION,
    AWS_SECRET_KEY,
    CHOSEN_COMPREHEND_ENTITIES,
    CHOSEN_LOCAL_OCR_MODEL,
    CHOSEN_REDACT_ENTITIES,
    COGNITO_AUTH,
    COMPRESS_REDACTED_PDF,
    CONFIG_FOLDER,
    COST_CODES_PATH,
    CSV_ACCESS_LOG_HEADERS,
    CSV_FEEDBACK_LOG_HEADERS,
    CSV_USAGE_LOG_HEADERS,
    DEFAULT_COMBINE_PAGES,
    DEFAULT_CONCURRENCY_LIMIT,
    DEFAULT_COST_CODE,
    DEFAULT_DUPLICATE_DETECTION_THRESHOLD,
    DEFAULT_EXCEL_SHEETS,
    DEFAULT_FUZZY_SPELLING_MISTAKES_NUM,
    DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX,
    DEFAULT_LANGUAGE,
    DEFAULT_LANGUAGE_FULL_NAME,
    DEFAULT_MIN_CONSECUTIVE_PAGES,
    DEFAULT_MIN_WORD_COUNT,
    DEFAULT_PAGE_MAX,
    DEFAULT_PAGE_MIN,
    DEFAULT_PII_DETECTION_MODEL,
    DEFAULT_SEARCH_QUERY,
    DEFAULT_TABULAR_ANONYMISATION_STRATEGY,
    DEFAULT_TEXT_COLUMNS,
    DEFAULT_TEXT_EXTRACTION_MODEL,
    DENY_LIST_PATH,
    DIRECT_MODE_DEFAULT_USER,
    DIRECT_MODE_DUPLICATE_TYPE,
    DIRECT_MODE_INPUT_FILE,
    DIRECT_MODE_OUTPUT_DIR,
    DIRECT_MODE_TASK,
    DISPLAY_FILE_NAMES_IN_LOGS,
    DO_INITIAL_TABULAR_DATA_CLEAN,
    DOCUMENT_REDACTION_BUCKET,
    DYNAMODB_ACCESS_LOG_HEADERS,
    DYNAMODB_FEEDBACK_LOG_HEADERS,
    DYNAMODB_USAGE_LOG_HEADERS,
    ENFORCE_COST_CODES,
    FEEDBACK_LOG_DYNAMODB_TABLE_NAME,
    FEEDBACK_LOG_FILE_NAME,
    FEEDBACK_LOGS_FOLDER,
    FILE_INPUT_HEIGHT,
    FULL_COMPREHEND_ENTITY_LIST,
    FULL_ENTITY_LIST,
    GET_COST_CODES,
    GET_DEFAULT_ALLOW_LIST,
    GRADIO_SERVER_PORT,
    GRADIO_TEMP_DIR,
    HANDWRITE_SIGNATURE_TEXTBOX_FULL_OPTIONS,
    HOST_NAME,
    IMAGES_DPI,
    INPUT_FOLDER,
    LOAD_PREVIOUS_TEXTRACT_JOBS_S3,
    LOCAL_PII_OPTION,
    LOG_FILE_NAME,
    MAX_FILE_SIZE,
    MAX_OPEN_TEXT_CHARACTERS,
    MAX_QUEUE_SIZE,
    MPLCONFIGDIR,
    NO_REDACTION_PII_OPTION,
    OUTPUT_COST_CODES_PATH,
    OUTPUT_FOLDER,
    PII_DETECTION_MODELS,
    PREPROCESS_LOCAL_OCR_IMAGES,
    REMOVE_DUPLICATE_ROWS,
    RETURN_PDF_END_OF_REDACTION,
    ROOT_PATH,
    RUN_AWS_FUNCTIONS,
    RUN_DIRECT_MODE,
    S3_ACCESS_LOGS_FOLDER,
    S3_ALLOW_LIST_PATH,
    S3_COST_CODES_PATH,
    S3_FEEDBACK_LOGS_FOLDER,
    S3_USAGE_LOGS_FOLDER,
    SAVE_LOGS_TO_CSV,
    SAVE_LOGS_TO_DYNAMODB,
    SESSION_OUTPUT_FOLDER,
    SHOW_COSTS,
    SHOW_EXAMPLES,
    SHOW_LANGUAGE_SELECTION,
    SHOW_WHOLE_DOCUMENT_TEXTRACT_CALL_OPTIONS,
    TABULAR_PII_DETECTION_MODELS,
    TESSERACT_TEXT_EXTRACT_OPTION,
    TEXT_EXTRACTION_MODELS,
    TEXTRACT_JOBS_LOCAL_LOC,
    TEXTRACT_JOBS_S3_INPUT_LOC,
    TEXTRACT_JOBS_S3_LOC,
    TEXTRACT_TEXT_EXTRACT_OPTION,
    TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_BUCKET,
    TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_INPUT_SUBFOLDER,
    TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_OUTPUT_SUBFOLDER,
    USAGE_LOG_DYNAMODB_TABLE_NAME,
    USAGE_LOG_FILE_NAME,
    USAGE_LOGS_FOLDER,
    USE_GREEDY_DUPLICATE_DETECTION,
    WHOLE_PAGE_REDACTION_LIST_PATH,
)
from tools.custom_csvlogger import CSVLogger_custom
from tools.data_anonymise import anonymise_files_with_open_text
from tools.file_conversion import get_input_file_names, prepare_image_or_pdf
from tools.file_redaction import choose_and_run_redactor
from tools.find_duplicate_pages import (
    apply_whole_page_redactions_from_list,
    create_annotation_objects_from_duplicates,
    exclude_match,
    handle_selection_and_preview,
    run_duplicate_analysis,
    run_full_search_and_analysis,
)
from tools.find_duplicate_tabular import (
    clean_tabular_duplicates,
    handle_tabular_row_selection,
    run_tabular_duplicate_detection,
)
from tools.helper_functions import (
    LANGUAGE_CHOICES,
    MAPPED_LANGUAGE_CHOICES,
    _get_env_list,
    calculate_aws_costs,
    calculate_time_taken,
    check_for_existing_textract_file,
    check_for_relevant_ocr_output_with_words,
    custom_regex_load,
    enforce_cost_codes,
    ensure_folder_exists,
    get_connection_params,
    load_all_output_files,
    load_in_default_allow_list,
    load_in_default_cost_codes,
    merge_csv_files,
    put_columns_in_df,
    reset_aws_call_vars,
    reset_base_dataframe,
    reset_data_vars,
    reset_ocr_base_dataframe,
    reset_ocr_with_words_base_dataframe,
    reset_review_vars,
    reset_state_vars,
    reveal_feedback_buttons,
    update_cost_code_dataframe_from_dropdown_select,
    update_dataframe,
    update_language_dropdown,
)
from tools.load_spacy_model_custom_recognisers import custom_entities
from tools.redaction_review import (
    apply_redactions_to_review_df_and_files,
    convert_df_to_xfdf,
    convert_xfdf_to_dataframe,
    create_annotation_objects_from_filtered_ocr_results_with_words,
    decrease_page,
    df_select_callback_cost,
    df_select_callback_dataframe_row,
    df_select_callback_dataframe_row_ocr_with_words,
    df_select_callback_ocr,
    df_select_callback_textract_api,
    exclude_selected_items_from_redaction,
    get_all_rows_with_same_text,
    get_all_rows_with_same_text_redact,
    get_and_merge_current_page_annotations,
    increase_bottom_page_count_based_on_top,
    increase_page,
    reset_dropdowns,
    undo_last_removal,
    update_all_entity_df_dropdowns,
    update_all_page_annotation_object_based_on_previous_page,
    update_annotator_object_and_filter_df,
    update_annotator_page_from_review_df,
    update_entities_df_page,
    update_entities_df_recogniser_entities,
    update_entities_df_text,
    update_other_annotator_number_from_current,
    update_redact_choice_df_from_page_dropdown,
    update_selected_review_df_row_colour,
)
from tools.textract_batch_call import (
    analyse_document_with_textract_api,
    check_for_provided_job_id,
    check_textract_outputs_exist,
    load_in_textract_job_details,
    poll_whole_document_textract_analysis_progress_and_download,
    replace_existing_pdf_input_for_whole_document_outputs,
)

# Suppress downcasting warnings
pd.set_option("future.no_silent_downcasting", True)

# Ensure that output folders exist
ensure_folder_exists(CONFIG_FOLDER)
ensure_folder_exists(OUTPUT_FOLDER)
ensure_folder_exists(INPUT_FOLDER)
if GRADIO_TEMP_DIR:
    ensure_folder_exists(GRADIO_TEMP_DIR)
if MPLCONFIGDIR:
    ensure_folder_exists(MPLCONFIGDIR)

ensure_folder_exists(FEEDBACK_LOGS_FOLDER)
ensure_folder_exists(ACCESS_LOGS_FOLDER)
ensure_folder_exists(USAGE_LOGS_FOLDER)

# Convert string environment variables to string or list
if SAVE_LOGS_TO_CSV == "True":
    SAVE_LOGS_TO_CSV = True
else:
    SAVE_LOGS_TO_CSV = False
if SAVE_LOGS_TO_DYNAMODB == "True":
    SAVE_LOGS_TO_DYNAMODB = True
else:
    SAVE_LOGS_TO_DYNAMODB = False
if SHOW_LANGUAGE_SELECTION == "True":
    SHOW_LANGUAGE_SELECTION = True
else:
    SHOW_LANGUAGE_SELECTION = False
if DISPLAY_FILE_NAMES_IN_LOGS == "True":
    DISPLAY_FILE_NAMES_IN_LOGS = True
else:
    DISPLAY_FILE_NAMES_IN_LOGS = False
if DO_INITIAL_TABULAR_DATA_CLEAN == "True":
    DO_INITIAL_TABULAR_DATA_CLEAN = True
else:
    DO_INITIAL_TABULAR_DATA_CLEAN = False
if COMPRESS_REDACTED_PDF == "True":
    COMPRESS_REDACTED_PDF = True
else:
    COMPRESS_REDACTED_PDF = False
if RETURN_PDF_END_OF_REDACTION == "True":
    RETURN_PDF_END_OF_REDACTION = True
else:
    RETURN_PDF_END_OF_REDACTION = False
if USE_GREEDY_DUPLICATE_DETECTION == "True":
    USE_GREEDY_DUPLICATE_DETECTION = True
else:
    USE_GREEDY_DUPLICATE_DETECTION = False
if DEFAULT_COMBINE_PAGES == "True":
    DEFAULT_COMBINE_PAGES = True
else:
    DEFAULT_COMBINE_PAGES = False
if REMOVE_DUPLICATE_ROWS == "True":
    REMOVE_DUPLICATE_ROWS = True
else:
    REMOVE_DUPLICATE_ROWS = False

if CSV_ACCESS_LOG_HEADERS:
    CSV_ACCESS_LOG_HEADERS = _get_env_list(CSV_ACCESS_LOG_HEADERS)
if CSV_FEEDBACK_LOG_HEADERS:
    CSV_FEEDBACK_LOG_HEADERS = _get_env_list(CSV_FEEDBACK_LOG_HEADERS)
if CSV_USAGE_LOG_HEADERS:
    CSV_USAGE_LOG_HEADERS = _get_env_list(CSV_USAGE_LOG_HEADERS)

if DYNAMODB_ACCESS_LOG_HEADERS:
    DYNAMODB_ACCESS_LOG_HEADERS = _get_env_list(DYNAMODB_ACCESS_LOG_HEADERS)
if DYNAMODB_FEEDBACK_LOG_HEADERS:
    DYNAMODB_FEEDBACK_LOG_HEADERS = _get_env_list(DYNAMODB_FEEDBACK_LOG_HEADERS)
if DYNAMODB_USAGE_LOG_HEADERS:
    DYNAMODB_USAGE_LOG_HEADERS = _get_env_list(DYNAMODB_USAGE_LOG_HEADERS)

if CHOSEN_COMPREHEND_ENTITIES:
    CHOSEN_COMPREHEND_ENTITIES = _get_env_list(CHOSEN_COMPREHEND_ENTITIES)
if FULL_COMPREHEND_ENTITY_LIST:
    FULL_COMPREHEND_ENTITY_LIST = _get_env_list(FULL_COMPREHEND_ENTITY_LIST)
if CHOSEN_REDACT_ENTITIES:
    CHOSEN_REDACT_ENTITIES = _get_env_list(CHOSEN_REDACT_ENTITIES)
if FULL_ENTITY_LIST:
    FULL_ENTITY_LIST = _get_env_list(FULL_ENTITY_LIST)

if DEFAULT_TEXT_COLUMNS:
    DEFAULT_TEXT_COLUMNS = _get_env_list(DEFAULT_TEXT_COLUMNS)
if DEFAULT_EXCEL_SHEETS:
    DEFAULT_EXCEL_SHEETS = _get_env_list(DEFAULT_EXCEL_SHEETS)

if DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX:
    DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX = _get_env_list(
        DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX
    )

# Add custom spacy recognisers to the Comprehend list, so that local Spacy model can be used to pick up e.g. titles, streetnames, UK postcodes that are sometimes missed by comprehend
CHOSEN_COMPREHEND_ENTITIES.extend(custom_entities)
FULL_COMPREHEND_ENTITY_LIST.extend(custom_entities)

# Load some components outside of blocks context that are used for examples
## Redaction examples
in_doc_files = gr.File(
    label="Choose a PDF document or image file (PDF, JPG, PNG)",
    file_count="multiple",
    file_types=[".pdf", ".jpg", ".png", ".json", ".zip"],
    height=FILE_INPUT_HEIGHT,
)

text_extract_method_radio = gr.Radio(
    label="""Choose text extraction method. Local options are lower quality but cost nothing - they may be worth a try if you are willing to spend some time reviewing outputs. AWS Textract has a cost per page - £2.66 ($3.50) per 1,000 pages with signature detection (default), £1.14 ($1.50) without. Change the settings in the tab below (AWS Textract signature detection) to change this.""",
    value=DEFAULT_TEXT_EXTRACTION_MODEL,
    choices=TEXT_EXTRACTION_MODELS,
)

pii_identification_method_drop = gr.Radio(
    label="""Choose personal information detection method. The local model is lower quality but costs nothing - it may be worth a try if you are willing to spend some time reviewing outputs, or if you are only interested in searching for custom search terms (see Redaction settings - custom deny list). AWS Comprehend has a cost of around £0.0075 ($0.01) per 10,000 characters.""",
    value=DEFAULT_PII_DETECTION_MODEL,
    choices=PII_DETECTION_MODELS,
)

handwrite_signature_checkbox = gr.CheckboxGroup(
    label="AWS Textract extraction settings",
    choices=HANDWRITE_SIGNATURE_TEXTBOX_FULL_OPTIONS,
    value=DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX,
)

in_redact_entities = gr.Dropdown(
    value=CHOSEN_REDACT_ENTITIES,
    choices=FULL_ENTITY_LIST,
    multiselect=True,
    label="Local PII identification model (click empty space in box for full list)",
)
in_redact_comprehend_entities = gr.Dropdown(
    value=CHOSEN_COMPREHEND_ENTITIES,
    choices=FULL_COMPREHEND_ENTITY_LIST,
    multiselect=True,
    label="AWS Comprehend PII identification model (click empty space in box for full list)",
)

## Deduplication examples
in_duplicate_pages = gr.File(
    label="Upload one or multiple 'ocr_output.csv' files to find duplicate pages and subdocuments",
    file_count="multiple",
    height=FILE_INPUT_HEIGHT,
    file_types=[".csv"],
)

duplicate_threshold_input = gr.Number(
    value=DEFAULT_DUPLICATE_DETECTION_THRESHOLD,
    label="Similarity threshold",
    info="Score (0-1) to consider pages a match.",
)

min_word_count_input = gr.Number(
    value=DEFAULT_MIN_WORD_COUNT,
    label="Minimum word count",
    info="Pages with fewer words than this value are ignored.",
)

combine_page_text_for_duplicates_bool = gr.Checkbox(
    value=True,
    label="Analyse duplicate text by page (off for by line)",
)

## Tabular examples
in_data_files = gr.File(
    label="Choose Excel or csv files",
    file_count="multiple",
    file_types=[".xlsx", ".xls", ".csv", ".parquet", ".docx"],
    height=FILE_INPUT_HEIGHT,
)

in_colnames = gr.Dropdown(
    choices=["Choose columns to anonymise"],
    multiselect=True,
    allow_custom_value=True,
    label="Select columns that you want to anonymise (showing columns present across all files).",
)

pii_identification_method_drop_tabular = gr.Radio(
    label="Choose PII detection method. AWS Comprehend has a cost of approximately $0.01 per 10,000 characters.",
    value=DEFAULT_PII_DETECTION_MODEL,
    choices=TABULAR_PII_DETECTION_MODELS,
)

anon_strategy = gr.Radio(
    choices=[
        "replace with 'REDACTED'",
        "replace with <ENTITY_NAME>",
        "redact completely",
        "hash",
        "mask",
    ],
    label="Select an anonymisation method.",
    value=DEFAULT_TABULAR_ANONYMISATION_STRATEGY,
)  # , "encrypt", "fake_first_name" are also available, but are not currently included as not that useful in current form

in_tabular_duplicate_files = gr.File(
    label="Upload CSV, Excel, or Parquet files to find duplicate cells/rows. Note that the app will remove duplicates from later cells/files that are found in earlier cells/files and not vice versa.",
    file_count="multiple",
    file_types=[".csv", ".xlsx", ".xls", ".parquet"],
    height=FILE_INPUT_HEIGHT,
)

tabular_text_columns = gr.Dropdown(
    label="Choose columns to deduplicate",
    multiselect=True,
    allow_custom_value=True,
)

# Create the gradio interface
app = gr.Blocks(
    theme=gr.themes.Default(primary_hue="blue"), fill_width=True
)  # gr.themes.Base()

with app:

    ###
    # STATE VARIABLES
    ###

    # Pymupdf doc needs to be stored as State objects as they do not have a standard Gradio component equivalent
    pdf_doc_state = gr.State(list())
    all_image_annotations_state = gr.Dropdown(
        "", label="all_image_annotations_state", allow_custom_value=True, visible=False
    )

    all_decision_process_table_state = gr.Dataframe(
        value=pd.DataFrame(),
        headers=None,
        col_count=0,
        row_count=(0, "dynamic"),
        label="all_decision_process_table",
        visible=False,
        type="pandas",
        wrap=True,
    )

    all_page_line_level_ocr_results = gr.Dropdown(
        "",
        label="all_page_line_level_ocr_results",
        allow_custom_value=True,
        visible=False,
    )
    all_page_line_level_ocr_results_with_words = gr.Dropdown(
        "",
        label="all_page_line_level_ocr_results_with_words",
        allow_custom_value=True,
        visible=False,
    )

    chosen_local_model_textbox = gr.Textbox(
        CHOSEN_LOCAL_OCR_MODEL, label="chosen_local_model_textbox", visible=False
    )

    session_hash_state = gr.Textbox(label="session_hash_state", value="", visible=False)
    host_name_textbox = gr.Textbox(
        label="host_name_textbox", value=HOST_NAME, visible=False
    )
    s3_output_folder_state = gr.Textbox(
        label="s3_output_folder_state", value="", visible=False
    )
    session_output_folder_textbox = gr.Textbox(
        value=SESSION_OUTPUT_FOLDER,
        label="session_output_folder_textbox",
        visible=False,
    )
    output_folder_textbox = gr.Textbox(
        value=OUTPUT_FOLDER, label="output_folder_textbox", visible=False
    )
    input_folder_textbox = gr.Textbox(
        value=INPUT_FOLDER, label="input_folder_textbox", visible=False
    )

    first_loop_state = gr.Checkbox(label="first_loop_state", value=True, visible=False)
    second_loop_state = gr.Checkbox(
        label="second_loop_state", value=False, visible=False
    )
    do_not_save_pdf_state = gr.Checkbox(
        label="do_not_save_pdf_state", value=False, visible=False
    )
    save_pdf_state = gr.Checkbox(label="save_pdf_state", value=True, visible=False)

    prepared_pdf_state = gr.Dropdown(
        label="prepared_pdf_list", value="", allow_custom_value=True, visible=False
    )
    document_cropboxes = gr.Dropdown(
        label="document_cropboxes", value="", allow_custom_value=True, visible=False
    )
    page_sizes = gr.Dropdown(
        label="page_sizes", value="", allow_custom_value=True, visible=False
    )
    images_pdf_state = gr.Dropdown(
        label="images_pdf_list", value="", allow_custom_value=True, visible=False
    )
    all_img_details_state = gr.Dropdown(
        label="all_img_details_state", value="", allow_custom_value=True, visible=False
    )

    output_image_files_state = gr.Dropdown(
        label="output_image_files_list",
        value="",
        allow_custom_value=True,
        visible=False,
    )
    output_file_list_state = gr.Dropdown(
        label="output_file_list", value="", allow_custom_value=True, visible=False
    )
    text_output_file_list_state = gr.Dropdown(
        label="text_output_file_list", value="", allow_custom_value=True, visible=False
    )
    log_files_output_list_state = gr.Dropdown(
        label="log_files_output_list", value="", allow_custom_value=True, visible=False
    )
    duplication_file_path_outputs_list_state = gr.Dropdown(
        label="duplication_file_path_outputs_list",
        value=list(),
        multiselect=True,
        allow_custom_value=True,
        visible=False,
    )

    # Backup versions of these objects in case you make a mistake
    backup_review_state = gr.State(pd.DataFrame())
    backup_image_annotations_state = gr.State(list())
    backup_recogniser_entity_dataframe_base = gr.State(pd.DataFrame())
    backup_all_page_line_level_ocr_results_with_words_df_base = gr.State(pd.DataFrame())

    # Logging variables
    access_logs_state = gr.Textbox(
        label="access_logs_state",
        value=ACCESS_LOGS_FOLDER + LOG_FILE_NAME,
        visible=False,
    )
    access_s3_logs_loc_state = gr.Textbox(
        label="access_s3_logs_loc_state", value=S3_ACCESS_LOGS_FOLDER, visible=False
    )
    feedback_logs_state = gr.Textbox(
        label="feedback_logs_state",
        value=FEEDBACK_LOGS_FOLDER + FEEDBACK_LOG_FILE_NAME,
        visible=False,
    )
    feedback_s3_logs_loc_state = gr.Textbox(
        label="feedback_s3_logs_loc_state", value=S3_FEEDBACK_LOGS_FOLDER, visible=False
    )
    usage_logs_state = gr.Textbox(
        label="usage_logs_state",
        value=USAGE_LOGS_FOLDER + USAGE_LOG_FILE_NAME,
        visible=False,
    )
    usage_s3_logs_loc_state = gr.Textbox(
        label="usage_s3_logs_loc_state", value=S3_USAGE_LOGS_FOLDER, visible=False
    )

    session_hash_textbox = gr.Textbox(
        label="session_hash_textbox", value="", visible=False
    )
    textract_metadata_textbox = gr.Textbox(
        label="textract_metadata_textbox", value="", visible=False
    )
    comprehend_query_number = gr.Number(
        label="comprehend_query_number", value=0, visible=False
    )
    textract_query_number = gr.Number(
        label="textract_query_number", value=0, visible=False
    )

    doc_full_file_name_textbox = gr.Textbox(
        label="doc_full_file_name_textbox", value="", visible=False
    )
    doc_file_name_no_extension_textbox = gr.Textbox(
        label="doc_full_file_name_textbox", value="", visible=False
    )
    blank_doc_file_name_no_extension_textbox_for_logs = gr.Textbox(
        label="doc_full_file_name_textbox", value="", visible=False
    )
    blank_data_file_name_no_extension_textbox_for_logs = gr.Textbox(
        label="data_full_file_name_textbox", value="", visible=False
    )
    placeholder_doc_file_name_no_extension_textbox_for_logs = gr.Textbox(
        label="doc_full_file_name_textbox", value="document", visible=False
    )
    placeholder_data_file_name_no_extension_textbox_for_logs = gr.Textbox(
        label="data_full_file_name_textbox", value="data_file", visible=False
    )

    # Left blank for when user does not want to report file names
    doc_file_name_with_extension_textbox = gr.Textbox(
        label="doc_file_name_with_extension_textbox", value="", visible=False
    )
    doc_file_name_textbox_list = gr.Dropdown(
        label="doc_file_name_textbox_list",
        value="",
        allow_custom_value=True,
        visible=False,
    )
    latest_review_file_path = gr.Textbox(
        label="latest_review_file_path", value="", visible=False
    )  # Latest review file path output from redaction
    latest_ocr_file_path = gr.Textbox(
        label="latest_ocr_file_path", value="", visible=False
    )  # Latest ocr file path output from text extraction

    data_full_file_name_textbox = gr.Textbox(
        label="data_full_file_name_textbox", value="", visible=False
    )
    data_file_name_no_extension_textbox = gr.Textbox(
        label="data_full_file_name_textbox", value="", visible=False
    )
    data_file_name_with_extension_textbox = gr.Textbox(
        label="data_file_name_with_extension_textbox", value="", visible=False
    )
    data_file_name_textbox_list = gr.Dropdown(
        label="data_file_name_textbox_list",
        value="",
        allow_custom_value=True,
        visible=False,
    )

    # Constants just to use with the review dropdowns for filtering by various columns
    label_name_const = gr.Textbox(
        label="label_name_const", value="label", visible=False
    )
    text_name_const = gr.Textbox(label="text_name_const", value="text", visible=False)
    page_name_const = gr.Textbox(label="page_name_const", value="page", visible=False)

    actual_time_taken_number = gr.Number(
        label="actual_time_taken_number", value=0.0, precision=1, visible=False
    )  # This keeps track of the time taken to redact files for logging purposes.
    annotate_previous_page = gr.Number(
        value=0, label="Previous page", precision=0, visible=False
    )  # Keeps track of the last page that the annotator was on
    s3_logs_output_textbox = gr.Textbox(label="Feedback submission logs", visible=False)

    ## Annotator zoom value
    annotator_zoom_number = gr.Number(
        label="Current annotator zoom level", value=100, precision=0, visible=False
    )
    zoom_true_bool = gr.Checkbox(label="zoom_true_bool", value=True, visible=False)
    zoom_false_bool = gr.Checkbox(label="zoom_false_bool", value=False, visible=False)

    clear_all_page_redactions = gr.Checkbox(
        label="clear_all_page_redactions", value=True, visible=False
    )
    prepare_for_review_bool = gr.Checkbox(
        label="prepare_for_review_bool", value=True, visible=False
    )
    prepare_for_review_bool_false = gr.Checkbox(
        label="prepare_for_review_bool_false", value=False, visible=False
    )
    prepare_images_bool_false = gr.Checkbox(
        label="prepare_images_bool_false", value=False, visible=False
    )

    ## Settings page variables
    default_deny_list_file_name = "default_deny_list.csv"
    default_deny_list_loc = OUTPUT_FOLDER + "/" + default_deny_list_file_name
    in_deny_list_text_in = gr.Textbox(value="deny_list", visible=False)

    fully_redacted_list_file_name = "default_fully_redacted_list.csv"
    fully_redacted_list_loc = OUTPUT_FOLDER + "/" + fully_redacted_list_file_name
    in_fully_redacted_text_in = gr.Textbox(
        value="fully_redacted_pages_list", visible=False
    )

    # S3 settings for default allow list load
    s3_default_bucket = gr.Textbox(
        label="Default S3 bucket", value=DOCUMENT_REDACTION_BUCKET, visible=False
    )
    s3_default_allow_list_file = gr.Textbox(
        label="Default allow list file", value=S3_ALLOW_LIST_PATH, visible=False
    )
    default_allow_list_output_folder_location = gr.Textbox(
        label="Output default allow list location", value=ALLOW_LIST_PATH, visible=False
    )

    s3_whole_document_textract_default_bucket = gr.Textbox(
        label="Default Textract whole_document S3 bucket",
        value=TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_BUCKET,
        visible=False,
    )
    s3_whole_document_textract_input_subfolder = gr.Textbox(
        label="Default Textract whole_document S3 input folder",
        value=TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_INPUT_SUBFOLDER,
        visible=False,
    )
    s3_whole_document_textract_output_subfolder = gr.Textbox(
        label="Default Textract whole_document S3 output folder",
        value=TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_OUTPUT_SUBFOLDER,
        visible=False,
    )
    successful_textract_api_call_number = gr.Number(precision=0, value=0, visible=False)
    no_redaction_method_drop = gr.Radio(
        label="""Placeholder for no redaction method after downloading Textract outputs""",
        value=NO_REDACTION_PII_OPTION,
        choices=[NO_REDACTION_PII_OPTION],
        visible=False,
    )
    textract_only_method_drop = gr.Radio(
        label="""Placeholder for Textract method after downloading Textract outputs""",
        value=TEXTRACT_TEXT_EXTRACT_OPTION,
        choices=[TEXTRACT_TEXT_EXTRACT_OPTION],
        visible=False,
    )

    load_s3_whole_document_textract_logs_bool = gr.Textbox(
        label="Load Textract logs or not",
        value=LOAD_PREVIOUS_TEXTRACT_JOBS_S3,
        visible=False,
    )
    s3_whole_document_textract_logs_subfolder = gr.Textbox(
        label="Default Textract whole_document S3 input folder",
        value=TEXTRACT_JOBS_S3_LOC,
        visible=False,
    )
    local_whole_document_textract_logs_subfolder = gr.Textbox(
        label="Default Textract whole_document S3 output folder",
        value=TEXTRACT_JOBS_LOCAL_LOC,
        visible=False,
    )

    s3_default_cost_codes_file = gr.Textbox(
        label="Default cost centre file", value=S3_COST_CODES_PATH, visible=False
    )
    default_cost_codes_output_folder_location = gr.Textbox(
        label="Output default cost centre location",
        value=OUTPUT_COST_CODES_PATH,
        visible=False,
    )
    enforce_cost_code_textbox = gr.Textbox(
        label="Enforce cost code textbox", value=ENFORCE_COST_CODES, visible=False
    )
    default_cost_code_textbox = gr.Textbox(
        label="Default cost code textbox", value=DEFAULT_COST_CODE, visible=False
    )

    # Base tables that are not modified subsequent to load
    recogniser_entity_dataframe_base = gr.State(
        pd.DataFrame(columns=["page", "label", "text", "id"])
    )
    all_page_line_level_ocr_results_df_base = gr.State(
        pd.DataFrame(columns=["page", "text", "left", "top", "width", "height", "line"])
    )
    all_line_level_ocr_results_df_placeholder = gr.State(
        pd.DataFrame(columns=["page", "text", "left", "top", "width", "height", "line"])
    )

    # Placeholder for selected entity dataframe row
    selected_entity_id = gr.Textbox(value="", label="selected_entity_id", visible=False)
    selected_entity_colour = gr.Textbox(
        value="", label="selected_entity_colour", visible=False
    )
    selected_entity_dataframe_row_text = gr.Textbox(
        value="", label="selected_entity_dataframe_row_text", visible=False
    )
    selected_entity_dataframe_row_text_redact = gr.Textbox(
        value="", label="selected_entity_dataframe_row_text_redact", visible=False
    )

    # This is an invisible dataframe that holds all items from the redaction outputs that have the same text as the selected row
    recogniser_entity_dataframe_same_text = gr.Dataframe(
        pd.DataFrame(
            data={"page": list(), "label": list(), "text": list(), "id": list()}
        ),
        col_count=(4, "fixed"),
        type="pandas",
        label="Table rows with same text",
        headers=["page", "label", "text", "id"],
        show_fullscreen_button=True,
        wrap=True,
        max_height=400,
        static_columns=[0, 1, 2, 3],
        visible=False,
    )

    to_redact_dataframe_same_text = gr.Dataframe(
        pd.DataFrame(
            data={
                "page": list(),
                "line": list(),
                "word_text": list(),
                "word_x0": list(),
                "word_y0": list(),
                "word_x1": list(),
                "word_y1": list(),
                "index": list(),
            }
        ),
        type="pandas",
        headers=[
            "page",
            "line",
            "word_text",
            "word_x0",
            "word_y0",
            "word_x1",
            "word_y1",
            "index",
        ],
        wrap=False,
        visible=False,
    )

    # Duplicate page detection
    in_duplicate_pages_text = gr.Textbox(label="in_duplicate_pages_text", visible=False)
    duplicate_pages_df = gr.Dataframe(
        value=pd.DataFrame(),
        headers=None,
        col_count=0,
        row_count=(0, "dynamic"),
        label="duplicate_pages_df",
        visible=False,
        type="pandas",
        wrap=True,
    )
    full_duplicated_data_df = gr.Dataframe(
        value=pd.DataFrame(),
        headers=None,
        col_count=0,
        row_count=(0, "dynamic"),
        label="full_duplicated_data_df",
        visible=False,
        type="pandas",
        wrap=True,
    )
    selected_duplicate_data_row_index = gr.Number(
        value=None, label="selected_duplicate_data_row_index", visible=False
    )
    full_duplicate_data_by_file = (
        gr.State()
    )  # A dictionary of the full duplicate data indexed by file

    # Tracking variables for current page (not visible)
    current_loop_page_number = gr.Number(
        value=0,
        precision=0,
        interactive=False,
        label="Last redacted page in document",
        visible=False,
    )
    page_break_return = gr.Checkbox(
        value=False, label="Page break reached", visible=False
    )

    # Placeholders for elements that may be made visible later below depending on environment variables
    cost_code_dataframe_base = gr.Dataframe(
        value=pd.DataFrame(),
        row_count=(0, "dynamic"),
        label="Cost codes",
        type="pandas",
        interactive=True,
        show_fullscreen_button=True,
        show_copy_button=True,
        show_search="filter",
        wrap=True,
        max_height=200,
        visible=False,
    )
    cost_code_dataframe = gr.Dataframe(
        value=pd.DataFrame(), type="pandas", visible=False, wrap=True
    )
    cost_code_choice_drop = gr.Dropdown(
        value=DEFAULT_COST_CODE,
        label="Choose cost code for analysis. Please contact Finance if you can't find your cost code in the given list.",
        choices=[DEFAULT_COST_CODE],
        allow_custom_value=False,
        visible=False,
    )

    textract_output_found_checkbox = gr.Checkbox(
        value=False,
        label="Existing Textract output file found",
        interactive=False,
        visible=False,
    )
    relevant_ocr_output_with_words_found_checkbox = gr.Checkbox(
        value=False,
        label="Existing local OCR output file found",
        interactive=False,
        visible=False,
    )
    total_pdf_page_count = gr.Number(label="Total page count", value=0, visible=False)
    estimated_aws_costs_number = gr.Number(
        label="Approximate AWS Textract and/or Comprehend cost ($)",
        value=0,
        visible=False,
        precision=2,
    )
    estimated_time_taken_number = gr.Number(
        label="Approximate time taken to extract text/redact (minutes)",
        value=0,
        visible=False,
        precision=2,
    )

    only_extract_text_radio = gr.Checkbox(
        value=False, label="Only extract text (no redaction)", visible=False
    )

    # Textract API call placeholders in case option not selected in config

    job_name_textbox = gr.Textbox(
        value="", label="whole_document Textract call", visible=False
    )
    send_document_to_textract_api_btn = gr.Button(
        "Analyse document with AWS Textract", variant="primary", visible=False
    )

    job_id_textbox = gr.Textbox(
        label="Latest job ID for whole_document document analysis",
        value="",
        visible=False,
    )
    check_state_of_textract_api_call_btn = gr.Button(
        "Check state of Textract document job and download",
        variant="secondary",
        visible=False,
    )
    job_current_status = gr.Textbox(
        value="", label="Analysis job current status", visible=False
    )
    job_type_dropdown = gr.Dropdown(
        value="document_text_detection",
        choices=["document_text_detection", "document_analysis"],
        label="Job type of Textract analysis job",
        allow_custom_value=False,
        visible=False,
    )
    textract_job_detail_df = gr.Dataframe(
        pd.DataFrame(
            columns=[
                "job_id",
                "file_name",
                "job_type",
                "signature_extraction",
                "job_date_time",
            ]
        ),
        label="Previous job details",
        visible=False,
        type="pandas",
        wrap=True,
    )
    selected_job_id_row = gr.Dataframe(
        pd.DataFrame(
            columns=[
                "job_id",
                "file_name",
                "job_type",
                "signature_extraction",
                "job_date_time",
            ]
        ),
        label="Selected job id row",
        visible=False,
        type="pandas",
        wrap=True,
    )
    is_a_textract_api_call = gr.Checkbox(
        value=False, label="is_this_a_textract_api_call", visible=False
    )
    task_textbox = gr.Textbox(
        value="redact", label="task", visible=False
    )  # Track the task being performed
    job_output_textbox = gr.Textbox(
        value="", label="Textract call outputs", visible=False
    )
    job_input_textbox = gr.Textbox(
        value=TEXTRACT_JOBS_S3_INPUT_LOC, label="Textract call outputs", visible=False
    )

    textract_job_output_file = gr.File(
        label="Textract job output files", height=FILE_INPUT_HEIGHT, visible=False
    )
    convert_textract_outputs_to_ocr_results = gr.Button(
        "Placeholder - Convert Textract job outputs to OCR results (needs relevant document file uploaded above)",
        variant="secondary",
        visible=False,
    )

    ## Duplicate search object
    new_duplicate_search_annotation_object = gr.Dropdown(
        value=None,
        label="new_duplicate_search_annotation_object",
        allow_custom_value=True,
        visible=False,
    )

    # Spacy analyser state
    updated_nlp_analyser_state = gr.State(list())
    tesseract_lang_data_file_path = gr.Textbox("", visible=False)

    ###
    # UI DESIGN
    ###

    gr.Markdown(
        """# Document redaction

    Redact personally identifiable information (PII) from documents (PDF, images), Word files (.docx), or tabular data (XLSX/CSV/Parquet). Please see the [User Guide](https://github.com/seanpedrick-case/doc_redaction/blob/main/README.md) for a walkthrough on how to use the app. Below is a very brief overview.
    
    To identify text in documents, the 'Local' text/OCR image analysis uses spaCy/Tesseract, and works well only for documents with typed text. If available, choose 'AWS Textract' to redact more complex elements e.g. signatures or handwriting. Then, choose a method for PII identification. 'Local' is quick and gives good results if you are primarily looking for a custom list of terms to redact (see Redaction settings). If available, AWS Comprehend gives better results at a small cost.
    
    After redaction, review suggested redactions on the 'Review redactions' tab. The original pdf can be uploaded here alongside a '...review_file.csv' to continue a previous redaction/review task. See the 'Redaction settings' tab to choose which pages to redact, the type of information to redact (e.g. people, places), or custom terms to always include/ exclude from redaction.

    NOTE: The app is not 100% accurate, and it will miss some personal information. It is essential that all outputs are reviewed **by a human** before using the final outputs."""
    )

    ###
    # REDACTION PDF/IMAGES TABLE
    ###
    with gr.Tab("Redact PDFs/images"):

        # Examples for PDF/image redaction
        # Examples for PDF/image redaction
        if SHOW_EXAMPLES == "True":
            gr.Markdown(
                "### Try an example - Click on an example below and then the 'Extract text and redact document' button:"
            )
            
            # Check which example files exist and create examples only for available files
            example_files = [
                "example_data/example_of_emails_sent_to_a_professor_before_applying.pdf",
                "example_data/example_complaint_letter.jpg", 
                "example_data/graduate-job-example-cover-letter.pdf",
                "example_data/Partnership-Agreement-Toolkit_0_0.pdf"
            ]
            
            available_examples = []
            example_labels = []
            
            # Check each example file and add to examples if it exists
            if os.path.exists(example_files[0]):
                available_examples.append([
                    [example_files[0]],
                    "Local model - selectable text",
                    "Local",
                    [],
                    CHOSEN_REDACT_ENTITIES,
                    CHOSEN_COMPREHEND_ENTITIES,
                    [example_files[0]],
                ])
                example_labels.append("PDF with selectable text redaction")
            
            if os.path.exists(example_files[1]):
                available_examples.append([
                    [example_files[1]],
                    "Local OCR model - PDFs without selectable text",
                    "Local",
                    [],
                    CHOSEN_REDACT_ENTITIES,
                    CHOSEN_COMPREHEND_ENTITIES,
                    [example_files[1]],
                ])
                example_labels.append("Image redaction with local OCR")
            
            if os.path.exists(example_files[2]):
                available_examples.append([
                    [example_files[2]],
                    "Local OCR model - PDFs without selectable text",
                    "Local",
                    [],
                    ["TITLES", "PERSON", "DATE_TIME"],
                    CHOSEN_COMPREHEND_ENTITIES,
                    [example_files[2]],
                ])
                example_labels.append("PDF redaction with custom entities (TITLES, PERSON, DATE_TIME)")
            
            if os.path.exists(example_files[3]):
                available_examples.append([
                    [example_files[3]],
                    "AWS Textract service - all PDF types",
                    "AWS Comprehend",
                    ["Extract handwriting", "Extract signatures"],
                    CHOSEN_REDACT_ENTITIES,
                    CHOSEN_COMPREHEND_ENTITIES,
                    [example_files[3]],
                ])
                example_labels.append("PDF redaction with AWS services and signature detection")
            
            # Only create examples if we have available files
            if available_examples:
                redaction_examples = gr.Examples(
                    examples=available_examples,
                    inputs=[
                        in_doc_files,
                        text_extract_method_radio,
                        pii_identification_method_drop,
                        handwrite_signature_checkbox,
                        in_redact_entities,
                        in_redact_comprehend_entities,
                        prepared_pdf_state,
                    ],
                    example_labels=example_labels,
                )

        with gr.Accordion("Redact document", open=True):
            # in_doc_files = gr.File(
            #     label="Choose a PDF document or image file (PDF, JPG, PNG)",
            #     file_count="multiple",
            #     file_types=[".pdf", ".jpg", ".png", ".json", ".zip"],
            #     height=FILE_INPUT_HEIGHT,
            # )
            in_doc_files.render()

            # text_extract_method_radio = gr.Radio(
            #     label="""Choose text extraction method. Local options are lower quality but cost nothing - they may be worth a try if you are willing to spend some time reviewing outputs. AWS Textract has a cost per page - £2.66 ($3.50) per 1,000 pages with signature detection (default), £1.14 ($1.50) without. Change the settings in the tab below (AWS Textract signature detection) to change this.""",
            #     value=DEFAULT_TEXT_EXTRACTION_MODEL,
            #     choices=TEXT_EXTRACTION_MODELS,
            # )
            text_extract_method_radio.render()

            with gr.Accordion(
                "Enable AWS Textract signature detection (default is off)", open=False
            ):
                # handwrite_signature_checkbox = gr.CheckboxGroup(
                #     label="AWS Textract extraction settings",
                #     choices=HANDWRITE_SIGNATURE_TEXTBOX_FULL_OPTIONS,
                #     value=DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX,
                # )
                handwrite_signature_checkbox.render()

            with gr.Row(equal_height=True):
                # pii_identification_method_drop = gr.Radio(
                #     label="""Choose personal information detection method. The local model is lower quality but costs nothing - it may be worth a try if you are willing to spend some time reviewing outputs, or if you are only interested in searching for custom search terms (see Redaction settings - custom deny list). AWS Comprehend has a cost of around £0.0075 ($0.01) per 10,000 characters.""",
                #     value=DEFAULT_PII_DETECTION_MODEL,
                #     choices=PII_DETECTION_MODELS,
                # )
                pii_identification_method_drop.render()

            if SHOW_COSTS == "True":
                with gr.Accordion(
                    "Estimated costs and time taken. Note that costs shown only include direct usage of AWS services and do not include other running costs (e.g. storage, run-time costs)",
                    open=True,
                    visible=True,
                ):
                    with gr.Row(equal_height=True):
                        with gr.Column(scale=1):
                            textract_output_found_checkbox = gr.Checkbox(
                                value=False,
                                label="Existing Textract output file found",
                                interactive=False,
                                visible=True,
                            )
                            relevant_ocr_output_with_words_found_checkbox = gr.Checkbox(
                                value=False,
                                label="Existing local OCR output file found",
                                interactive=False,
                                visible=True,
                            )
                        with gr.Column(scale=4):
                            with gr.Row(equal_height=True):
                                total_pdf_page_count = gr.Number(
                                    label="Total page count",
                                    value=0,
                                    visible=True,
                                    interactive=False,
                                )
                                estimated_aws_costs_number = gr.Number(
                                    label="Approximate AWS Textract and/or Comprehend cost (£)",
                                    value=0.00,
                                    precision=2,
                                    visible=True,
                                    interactive=False,
                                )
                                estimated_time_taken_number = gr.Number(
                                    label="Approximate time taken to extract text/redact (minutes)",
                                    value=0,
                                    visible=True,
                                    precision=2,
                                    interactive=False,
                                )

            if GET_COST_CODES == "True" or ENFORCE_COST_CODES == "True":
                with gr.Accordion("Assign task to cost code", open=True, visible=True):
                    gr.Markdown(
                        "Please ensure that you have approval from your budget holder before using this app for redaction tasks that incur a cost."
                    )
                    with gr.Row():
                        cost_code_dataframe = gr.Dataframe(
                            value=pd.DataFrame(),
                            row_count=(0, "dynamic"),
                            label="Existing cost codes",
                            type="pandas",
                            interactive=True,
                            show_fullscreen_button=True,
                            show_copy_button=True,
                            show_search="filter",
                            visible=True,
                            wrap=True,
                            max_height=200,
                        )
                        with gr.Column():
                            reset_cost_code_dataframe_button = gr.Button(
                                value="Reset code code table filter"
                            )
                            cost_code_choice_drop = gr.Dropdown(
                                value=DEFAULT_COST_CODE,
                                label="Choose cost code for analysis",
                                choices=[DEFAULT_COST_CODE],
                                allow_custom_value=False,
                                visible=True,
                            )

            if SHOW_WHOLE_DOCUMENT_TEXTRACT_CALL_OPTIONS == "True":
                with gr.Accordion(
                    "Submit whole document to AWS Textract API (quickest text extraction for large documents)",
                    open=False,
                    visible=True,
                ):
                    with gr.Row(equal_height=True):
                        gr.Markdown(
                            """Document will be submitted to AWS Textract API service to extract all text in the document. Processing will take place on (secure) AWS servers, and outputs will be stored on S3 for up to 7 days. To download the results, click 'Check status' below and they will be downloaded if ready."""
                        )
                    with gr.Row(equal_height=True):
                        send_document_to_textract_api_btn = gr.Button(
                            "Analyse document with AWS Textract API call",
                            variant="primary",
                            visible=True,
                        )
                    with gr.Row(equal_height=False):
                        with gr.Column(scale=2):
                            textract_job_detail_df = gr.Dataframe(
                                label="Previous job details",
                                visible=True,
                                type="pandas",
                                wrap=True,
                                interactive=True,
                                row_count=(0, "fixed"),
                                col_count=(5, "fixed"),
                                static_columns=[0, 1, 2, 3, 4],
                                max_height=400,
                            )
                        with gr.Column(scale=1):
                            job_id_textbox = gr.Textbox(
                                label="Job ID to check status",
                                value="",
                                visible=True,
                                lines=2,
                            )
                            check_state_of_textract_api_call_btn = gr.Button(
                                "Check status of Textract job and download",
                                variant="secondary",
                                visible=True,
                            )
                    with gr.Row():
                        with gr.Column():
                            textract_job_output_file = gr.File(
                                label="Textract job output files",
                                height=100,
                                visible=True,
                            )
                        with gr.Column():
                            job_current_status = gr.Textbox(
                                value="",
                                label="Analysis job current status",
                                visible=True,
                            )
                            convert_textract_outputs_to_ocr_results = gr.Button(
                                "Convert Textract job outputs to OCR results",
                                variant="secondary",
                                visible=True,
                            )

            gr.Markdown(
                """If you only want to redact certain pages, or certain entities (e.g. just email addresses, or a custom list of terms), please go to the Redaction Settings tab."""
            )
            document_redact_btn = gr.Button(
                "Extract text and redact document", variant="primary", scale=4
            )

        with gr.Row():
            redaction_output_summary_textbox = gr.Textbox(
                label="Output summary", scale=1, lines=4
            )
            output_file = gr.File(
                label="Output files", scale=2
            )  # , height=FILE_INPUT_HEIGHT)
            latest_file_completed_num = gr.Number(
                value=0,
                label="Number of documents redacted",
                interactive=False,
                visible=False,
            )

        # Feedback elements are invisible until revealed by redaction action
        pdf_feedback_title = gr.Markdown(value="## Please give feedback", visible=False)
        pdf_feedback_radio = gr.Radio(
            label="Quality of results",
            choices=["The results were good", "The results were not good"],
            visible=False,
        )
        pdf_further_details_text = gr.Textbox(
            label="Please give more detailed feedback about the results:", visible=False
        )
        pdf_submit_feedback_btn = gr.Button(value="Submit feedback", visible=False)

    ###
    # REVIEW REDACTIONS TAB
    ###
    with gr.Tab("Review redactions", id="tab_object_annotation"):

        all_page_line_level_ocr_results_with_words_df_base = gr.Dataframe(
            type="pandas",
            label="all_page_line_level_ocr_results_with_words_df_base",
            show_fullscreen_button=True,
            wrap=False,
            show_search="filter",
            visible=False,
        )

        with gr.Accordion(label="Review PDF redactions", open=True):
            with gr.Row(equal_height=True):
                with gr.Column(scale=2):
                    input_pdf_for_review = gr.File(
                        label="Upload original PDF to begin review process.",
                        file_count="multiple",
                        height=FILE_INPUT_HEIGHT,
                    )
                    upload_pdf_for_review_btn = gr.Button(
                        "1. Upload original PDF", variant="secondary"
                    )
                with gr.Column(scale=1):
                    input_review_files = gr.File(
                        label="Upload review files here to review suggested redactions. 'review_file' csv The 'ocr_results with words' file can also be provided for searching text and making new redactions.",
                        file_count="multiple",
                        height=FILE_INPUT_HEIGHT,
                    )
                    upload_review_files_btn = gr.Button(
                        "2. Upload review or OCR csv files", variant="secondary"
                    )
        with gr.Row():
            annotate_zoom_in = gr.Button("Zoom in", visible=False)
            annotate_zoom_out = gr.Button("Zoom out", visible=False)
        with gr.Row():
            clear_all_redactions_on_page_btn = gr.Button(
                "Clear all redactions on page", visible=False
            )

        with gr.Accordion(label="View and edit review file data", open=False):
            review_file_df = gr.Dataframe(
                value=pd.DataFrame(),
                headers=[
                    "image",
                    "page",
                    "label",
                    "color",
                    "xmin",
                    "ymin",
                    "xmax",
                    "ymax",
                    "text",
                    "id",
                ],
                row_count=(0, "dynamic"),
                label="Review file data",
                visible=True,
                type="pandas",
                wrap=True,
                show_search=True,
                show_fullscreen_button=True,
                show_copy_button=True,
            )

        with gr.Row():
            with gr.Column(scale=2):
                with gr.Row(equal_height=True):
                    annotation_last_page_button = gr.Button("Previous page", scale=4)
                    annotate_current_page = gr.Number(
                        value=1,
                        label="Current page",
                        precision=0,
                        scale=2,
                        min_width=50,
                        minimum=1,
                    )
                    annotate_max_pages = gr.Number(
                        value=1,
                        label="Total pages",
                        precision=0,
                        interactive=False,
                        scale=2,
                        min_width=50,
                        minimum=1,
                    )
                    annotation_next_page_button = gr.Button("Next page", scale=4)

                zoom_str = str(annotator_zoom_number) + "%"

                annotator = image_annotator(
                    label="Modify redaction boxes",
                    label_list=["Redaction"],
                    label_colors=[(0, 0, 0)],
                    show_label=False,
                    height=zoom_str,
                    width=zoom_str,
                    box_min_size=1,
                    box_selected_thickness=2,
                    handle_size=4,
                    sources=None,  # ["upload"],
                    show_clear_button=False,
                    show_share_button=False,
                    show_remove_button=False,
                    handles_cursor=True,
                    interactive=False,
                )

                with gr.Row(equal_height=True):
                    annotation_last_page_button_bottom = gr.Button(
                        "Previous page", scale=4
                    )
                    annotate_current_page_bottom = gr.Number(
                        value=1,
                        label="Current page",
                        precision=0,
                        interactive=True,
                        scale=2,
                        min_width=50,
                        minimum=1,
                    )
                    annotate_max_pages_bottom = gr.Number(
                        value=1,
                        label="Total pages",
                        precision=0,
                        interactive=False,
                        scale=2,
                        min_width=50,
                        minimum=1,
                    )
                    annotation_next_page_button_bottom = gr.Button("Next page", scale=4)

            with gr.Column(scale=1):
                annotation_button_apply = gr.Button(
                    "Apply revised redactions to PDF", variant="primary"
                )
                update_current_page_redactions_btn = gr.Button(
                    value="Save changes on current page to file", variant="primary"
                )

                with gr.Tab("Modify existing redactions"):
                    with gr.Accordion("Search suggested redactions", open=True):
                        with gr.Row(equal_height=True):
                            recogniser_entity_dropdown = gr.Dropdown(
                                label="Redaction category",
                                value="ALL",
                                allow_custom_value=True,
                            )
                            page_entity_dropdown = gr.Dropdown(
                                label="Page", value="ALL", allow_custom_value=True
                            )
                        text_entity_dropdown = gr.Dropdown(
                            label="Text", value="ALL", allow_custom_value=True
                        )
                        reset_dropdowns_btn = gr.Button(value="Reset filters")
                        recogniser_entity_dataframe = gr.Dataframe(
                            pd.DataFrame(
                                data={
                                    "page": list(),
                                    "label": list(),
                                    "text": list(),
                                    "id": list(),
                                }
                            ),
                            col_count=(4, "fixed"),
                            type="pandas",
                            label="Click table row to select and go to page",
                            headers=["page", "label", "text", "id"],
                            show_fullscreen_button=True,
                            wrap=True,
                            max_height=400,
                            static_columns=[0, 1, 2, 3],
                        )

                        with gr.Row(equal_height=True):
                            exclude_selected_btn = gr.Button(
                                value="Exclude all redactions in table"
                            )

                        with gr.Accordion("Selected redaction row", open=True):
                            selected_entity_dataframe_row = gr.Dataframe(
                                pd.DataFrame(
                                    data={
                                        "page": list(),
                                        "label": list(),
                                        "text": list(),
                                        "id": list(),
                                    }
                                ),
                                col_count=4,
                                type="pandas",
                                visible=True,
                                headers=["page", "label", "text", "id"],
                                wrap=True,
                            )
                            exclude_selected_row_btn = gr.Button(
                                value="Exclude specific redaction row"
                            )
                            exclude_text_with_same_as_selected_row_btn = gr.Button(
                                value="Exclude all redactions with same text as selected row"
                            )

                        undo_last_removal_btn = gr.Button(
                            value="Undo last element removal", variant="primary"
                        )

                with gr.Tab("Search text to make new redactions"):
                    with gr.Accordion("Search text", open=True):
                        with gr.Row(equal_height=True):
                            page_entity_dropdown_redaction = gr.Dropdown(
                                label="Page",
                                value="1",
                                allow_custom_value=True,
                                scale=4,
                            )
                            reset_dropdowns_btn_new = gr.Button(
                                value="Reset page filter", scale=1
                            )

                        with gr.Row(equal_height=True):
                            multi_word_search_text = gr.Textbox(
                                label="Multi-word text search", value="", scale=4
                            )
                            multi_word_search_text_btn = gr.Button(
                                value="Search", scale=1
                            )

                        with gr.Accordion("Search options", open=False):
                            similarity_search_score_minimum = gr.Number(
                                value=1.0,
                                minimum=0.4,
                                maximum=1.0,
                                label="Minimum similarity score for match (max=1)",
                                visible=False,
                            )  # Not used anymore for this exact search
                            new_redaction_text_label = gr.Textbox(
                                label="Label for new redactions", value="Redaction"
                            )
                            colour_label = gr.Textbox(
                                label="Colour for labels (three number RGB format, max 255 with brackes)",
                                value="(0, 0, 0)",
                            )

                        all_page_line_level_ocr_results_with_words_df = gr.Dataframe(
                            pd.DataFrame(
                                data={
                                    "page": list(),
                                    "line": list(),
                                    "word_text": list(),
                                    "word_x0": list(),
                                    "word_y0": list(),
                                    "word_x1": list(),
                                    "word_y1": list(),
                                }
                            ),
                            type="pandas",
                            label="Click table row to select and go to page",
                            headers=[
                                "page",
                                "line",
                                "word_text",
                                "word_x0",
                                "word_y0",
                                "word_x1",
                                "word_y1",
                            ],
                            show_fullscreen_button=True,
                            wrap=False,
                            max_height=400,
                            show_search="filter",
                        )

                        redact_selected_btn = gr.Button(
                            value="Redact all text in table"
                        )
                        reset_ocr_with_words_df_btn = gr.Button(
                            value="Reset table to original state"
                        )

                        with gr.Accordion("Selected row", open=True):
                            selected_entity_dataframe_row_redact = gr.Dataframe(
                                pd.DataFrame(
                                    data={
                                        "page": list(),
                                        "line": list(),
                                        "word_text": list(),
                                        "word_x0": list(),
                                        "word_y0": list(),
                                        "word_x1": list(),
                                        "word_y1": list(),
                                    }
                                ),
                                type="pandas",
                                headers=[
                                    "page",
                                    "line",
                                    "word_text",
                                    "word_x0",
                                    "word_y0",
                                    "word_x1",
                                    "word_y1",
                                ],
                                wrap=False,
                            )
                            redact_selected_row_btn = gr.Button(
                                value="Redact specific text row"
                            )
                            redact_text_with_same_as_selected_row_btn = gr.Button(
                                value="Redact all words with same text as selected row"
                            )

                        undo_last_redact_btn = gr.Button(
                            value="Undo latest redaction", variant="primary"
                        )

                with gr.Accordion("Search extracted text", open=True):
                    all_page_line_level_ocr_results_df = gr.Dataframe(
                        value=pd.DataFrame(),
                        headers=["page", "line", "text"],
                        col_count=(3, "fixed"),
                        row_count=(0, "dynamic"),
                        label="All OCR results",
                        visible=True,
                        type="pandas",
                        wrap=True,
                        show_fullscreen_button=True,
                        show_search="filter",
                        show_label=False,
                        show_copy_button=True,
                        max_height=400,
                    )
                    reset_all_ocr_results_btn = gr.Button(
                        value="Reset OCR output table filter"
                    )
                    selected_ocr_dataframe_row = gr.Dataframe(
                        pd.DataFrame(
                            data={"page": list(), "line": list(), "text": list()}
                        ),
                        col_count=3,
                        type="pandas",
                        visible=False,
                        headers=["page", "line", "text"],
                        wrap=True,
                    )

        with gr.Accordion(
            "Convert review files loaded above to Adobe format, or convert from Adobe format to review file",
            open=False,
        ):
            convert_review_file_to_adobe_btn = gr.Button(
                "Convert review file to Adobe comment format", variant="primary"
            )
            adobe_review_files_out = gr.File(
                label="Output Adobe comment files will appear here. If converting from .xfdf file to review_file.csv, upload the original pdf with the xfdf file here then click Convert below.",
                file_count="multiple",
                file_types=[".csv", ".xfdf", ".pdf"],
            )
            convert_adobe_to_review_file_btn = gr.Button(
                "Convert Adobe .xfdf comment file to review_file.csv",
                variant="secondary",
            )

    ###
    # IDENTIFY DUPLICATE PAGES TAB
    ###
    with gr.Tab(label="Identify duplicate pages"):
        gr.Markdown(
            "Search for duplicate pages/subdocuments in your ocr_output files. By default, this function will search for duplicate text across multiple pages, and then join consecutive matching pages together into matched 'subdocuments'. The results can be reviewed below, false positives removed, and then the verified results applied to a document you have loaded in on the 'Review redactions' tab."
        )

        # Examples for duplicate page detection
        if SHOW_EXAMPLES == "True":
            gr.Markdown(
                "### Try an example - Click on an example below and then the 'Identify duplicate pages/subdocuments' button:"
            )
            
            # Check if duplicate example file exists
            duplicate_example_file = "example_data/example_outputs/doubled_output_joined.pdf_ocr_output.csv"
            
            if os.path.exists(duplicate_example_file):
                duplicate_examples = gr.Examples(
                    examples=[
                        [
                            [duplicate_example_file],
                            0.95,
                            10,
                            True,
                        ],
                        [
                            [duplicate_example_file],
                            0.95,
                            3,
                            False,
                        ],
                    ],
                    inputs=[
                        in_duplicate_pages,
                        duplicate_threshold_input,
                        min_word_count_input,
                        combine_page_text_for_duplicates_bool,
                    ],
                    example_labels=[
                        "Find duplicate pages of text in document OCR outputs",
                        "Find duplicate text lines in document OCR outputs",
                    ],
                )

        with gr.Accordion("Step 1: Configure and run analysis", open=True):
            # in_duplicate_pages = gr.File(
            #     label="Upload one or multiple 'ocr_output.csv' files to find duplicate pages and subdocuments",
            #     file_count="multiple",
            #     height=FILE_INPUT_HEIGHT,
            #     file_types=[".csv"],
            # )
            in_duplicate_pages.render()

            with gr.Accordion("Duplicate matching parameters", open=False):
                with gr.Row():
                    # duplicate_threshold_input = gr.Number(
                    #     value=DEFAULT_DUPLICATE_DETECTION_THRESHOLD,
                    #     label="Similarity threshold",
                    #     info="Score (0-1) to consider pages a match.",
                    # )
                    duplicate_threshold_input.render()

                    # min_word_count_input = gr.Number(
                    #     value=DEFAULT_MIN_WORD_COUNT,
                    #     label="Minimum word count",
                    #     info="Pages with fewer words than this value are ignored.",
                    # )
                    min_word_count_input.render()

                    # combine_page_text_for_duplicates_bool = gr.Checkbox(
                    #     value=True,
                    #     label="Analyse duplicate text by page (off for by line)",
                    # )
                    combine_page_text_for_duplicates_bool.render()

                gr.Markdown("#### Matching Strategy")
                greedy_match_input = gr.Checkbox(
                    label="Enable 'subdocument' matching",
                    value=USE_GREEDY_DUPLICATE_DETECTION,
                    info="If checked, finds the longest possible sequence of matching pages (subdocuments), minimum length one page. Overrides the slider below.",
                )
                min_consecutive_pages_input = gr.Slider(
                    minimum=1,
                    maximum=20,
                    value=DEFAULT_MIN_CONSECUTIVE_PAGES,
                    step=1,
                    label="Minimum consecutive pages (modified subdocument match)",
                    info="If greedy matching option above is unticked, use this to find only subdocuments of a minimum number of consecutive pages.",
                )

            find_duplicate_pages_btn = gr.Button(
                value="Identify duplicate pages/subdocuments", variant="primary"
            )

        with gr.Accordion("Step 2: Review and refine results", open=True):
            gr.Markdown(
                "### Analysis summary\nClick on a row to select it for preview or exclusion."
            )

            with gr.Row():
                results_df_preview = gr.Dataframe(
                    label="Similarity Results",
                    headers=[
                        "Page1_File",
                        "Page1_Start_Page",
                        "Page1_End_Page",
                        "Page2_File",
                        "Page2_Start_Page",
                        "Page2_End_Page",
                        "Match_Length",
                        "Avg_Similarity",
                        "Page1_Text",
                        "Page2_Text",
                    ],
                    wrap=True,
                    show_fullscreen_button=True,
                    show_search=True,
                    show_copy_button=True,
                )
            with gr.Row():
                exclude_match_btn = gr.Button(
                    value="❌ Exclude Selected Match", variant="stop"
                )
                gr.Markdown(
                    "Click a row in the table, then click this button to remove it from the results and update the downloadable files."
                )

            gr.Markdown("### Full Text Preview of Selected Match")
            with gr.Row():
                page1_text_preview = gr.Dataframe(
                    label="Match Source (Document 1)",
                    wrap=True,
                    headers=["page", "text"],
                    show_fullscreen_button=True,
                    show_search=True,
                    show_copy_button=True,
                )
                page2_text_preview = gr.Dataframe(
                    label="Match Duplicate (Document 2)",
                    wrap=True,
                    headers=["page", "text"],
                    show_fullscreen_button=True,
                    show_search=True,
                    show_copy_button=True,
                )

            gr.Markdown("### Downloadable Files")
            duplicate_files_out = gr.File(
                label="Download analysis summary and redaction lists (.csv)",
                file_count="multiple",
                height=FILE_INPUT_HEIGHT,
            )

            with gr.Row():
                apply_match_btn = gr.Button(
                    value="Apply relevant duplicate page output to document currently under review",
                    variant="secondary",
                )

    ###
    # WORD / TABULAR DATA TAB
    ###
    with gr.Tab(label="Word or Excel/csv files"):
        gr.Markdown(
            """Choose Word or a tabular data file (xlsx or csv) to redact. Note that when redacting complex Word files with e.g. images, some content/formatting will be removed, and it may not attempt to redact headers. You may prefer to convert the doc file to PDF in Word, and then run it through the first tab of this app (Print to PDF in print settings). Alternatively, an xlsx file output is provided when redacting docx files directly to allow for copying and pasting outputs back into the original document if preferred."""
        )

        # Examples for Word/Excel/csv redaction and tabular duplicate detection
        if SHOW_EXAMPLES == "True":
            gr.Markdown(
                "### Try an example - Click on an example below and then the 'Redact text/data files' button for redaction, or the 'Find duplicate cells/rows' button for duplicate detection:"
            )
            
            # Check which tabular example files exist
            tabular_example_files = [
                "example_data/combined_case_notes.csv",
                "example_data/Bold minimalist professional cover letter.docx",
                "example_data/Lambeth_2030-Our_Future_Our_Lambeth.pdf.csv"
            ]
            
            available_tabular_examples = []
            tabular_example_labels = []
            
            # Check each tabular example file and add to examples if it exists
            if os.path.exists(tabular_example_files[0]):
                available_tabular_examples.append([
                    [tabular_example_files[0]],
                    ["Case Note", "Client"],
                    "Local",
                    "replace with 'REDACTED'",
                    [tabular_example_files[0]],
                    ["Case Note"],
                ])
                tabular_example_labels.append("CSV file redaction with specific columns - remove text")
            
            if os.path.exists(tabular_example_files[1]):
                available_tabular_examples.append([
                    [tabular_example_files[1]],
                    [],
                    "Local",
                    "replace with 'REDACTED'",
                    [],
                    [],
                ])
                tabular_example_labels.append("Word document redaction - replace with REDACTED")
            
            if os.path.exists(tabular_example_files[2]):
                available_tabular_examples.append([
                    [tabular_example_files[2]],
                    ["text"],
                    "Local",
                    "replace with 'REDACTED'",
                    [tabular_example_files[2]],
                    ["text"],
                ])
                tabular_example_labels.append("Tabular duplicate detection in CSV files")
            
            # Only create examples if we have available files
            if available_tabular_examples:
                tabular_examples = gr.Examples(
                    examples=available_tabular_examples,
                    inputs=[
                        in_data_files,
                        in_colnames,
                        pii_identification_method_drop_tabular,
                        anon_strategy,
                        in_tabular_duplicate_files,
                        tabular_text_columns,
                    ],
                    example_labels=tabular_example_labels,
                )

        with gr.Accordion("Redact Word or Excel/csv files", open=True):
            with gr.Accordion("Upload docx, xlsx, or csv files", open=True):
                # in_data_files = gr.File(
                #     label="Choose Excel or csv files",
                #     file_count="multiple",
                #     file_types=[".xlsx", ".xls", ".csv", ".parquet", ".docx"],
                #     height=FILE_INPUT_HEIGHT,
                # )
                in_data_files.render()
            with gr.Accordion("Redact open text", open=False):
                in_text = gr.Textbox(
                    label="Enter open text",
                    lines=10,
                    max_length=MAX_OPEN_TEXT_CHARACTERS,
                )

            in_excel_sheets = gr.Dropdown(
                choices=["Choose Excel sheets to anonymise"],
                multiselect=True,
                label="Select Excel sheets that you want to anonymise (showing sheets present across all Excel files).",
                visible=False,
                allow_custom_value=True,
            )

            # in_colnames = gr.Dropdown(
            #     choices=["Choose columns to anonymise"],
            #     multiselect=True,
            #     allow_custom_value=True,
            #     label="Select columns that you want to anonymise (showing columns present across all files).",
            # )
            in_colnames.render()

            # pii_identification_method_drop_tabular = gr.Radio(
            #     label="Choose PII detection method. AWS Comprehend has a cost of approximately $0.01 per 10,000 characters.",
            #     value=DEFAULT_PII_DETECTION_MODEL,
            #     choices=TABULAR_PII_DETECTION_MODELS,
            # )
            pii_identification_method_drop_tabular.render()

            with gr.Accordion(
                "Anonymisation output format - by default will replace PII with a blank space",
                open=False,
            ):
                with gr.Row():
                    # anon_strategy = gr.Radio(
                    #     choices=[
                    #         "replace with 'REDACTED'",
                    #         "replace with <ENTITY_NAME>",
                    #         "redact completely",
                    #         "hash",
                    #         "mask",
                    #     ],
                    #     label="Select an anonymisation method.",
                    #     value=DEFAULT_TABULAR_ANONYMISATION_STRATEGY,
                    # )  # , "encrypt", "fake_first_name" are also available, but are not currently included as not that useful in current form
                    anon_strategy.render()

                    do_initial_clean = gr.Checkbox(
                        label="Do initial clean of text (remove URLs, HTML tags, and non-ASCII characters)",
                        value=DO_INITIAL_TABULAR_DATA_CLEAN,
                    )

            tabular_data_redact_btn = gr.Button(
                "Redact text/data files", variant="primary"
            )

        with gr.Row():
            text_output_summary = gr.Textbox(label="Output result", lines=4)
            text_output_file = gr.File(label="Output files")
            text_tabular_files_done = gr.Number(
                value=0,
                label="Number of tabular files redacted",
                interactive=False,
                visible=False,
            )

        ###
        # TABULAR DUPLICATE DETECTION
        ###
        with gr.Accordion(label="Find duplicate cells in tabular data", open=False):
            gr.Markdown(
                """Find duplicate cells or rows in CSV, Excel, or Parquet files. This tool analyzes text content across all columns to identify similar or identical entries that may be duplicates. You can review the results and choose to remove duplicate rows from your files."""
            )

            with gr.Accordion("Step 1: Upload files and configure analysis", open=True):
                # in_tabular_duplicate_files = gr.File(
                #     label="Upload CSV, Excel, or Parquet files to find duplicate cells/rows. Note that the app will remove duplicates from later cells/files that are found in earlier cells/files and not vice versa.",
                #     file_count="multiple",
                #     file_types=[".csv", ".xlsx", ".xls", ".parquet"],
                #     height=FILE_INPUT_HEIGHT,
                # )
                in_tabular_duplicate_files.render()

                with gr.Row(equal_height=True):
                    tabular_duplicate_threshold = gr.Number(
                        value=DEFAULT_DUPLICATE_DETECTION_THRESHOLD,
                        label="Similarity threshold",
                        info="Score (0-1) to consider cells a match. 1 = perfect match.",
                    )
                    tabular_min_word_count = gr.Number(
                        value=DEFAULT_MIN_WORD_COUNT,
                        label="Minimum word count",
                        info="Cells with fewer words than this are ignored.",
                    )
                    do_initial_clean_dup = gr.Checkbox(
                        label="Do initial clean of text (remove URLs, HTML tags, and non-ASCII characters)",
                        value=DO_INITIAL_TABULAR_DATA_CLEAN,
                    )
                    remove_duplicate_rows = gr.Checkbox(
                        label="Remove duplicate rows from deduplicated files",
                        value=REMOVE_DUPLICATE_ROWS,
                    )

                with gr.Row():
                    in_excel_tabular_sheets = gr.Dropdown(
                        choices=list(),
                        multiselect=True,
                        label="Select Excel sheet names that you want to deduplicate (showing sheets present across all Excel files).",
                        visible=True,
                        allow_custom_value=True,
                    )

                    # tabular_text_columns = gr.Dropdown(
                    #     choices=DEFAULT_TEXT_COLUMNS,
                    #     multiselect=True,
                    #     label="Select specific columns to analyse (leave empty to analyse all text columns simultaneously - i.e. all text is joined together)",
                    #     info="If no columns selected, all text columns will combined together and analysed",
                    # )
                    tabular_text_columns.render()

                find_tabular_duplicates_btn = gr.Button(
                    value="Find duplicate cells/rows", variant="primary"
                )

            with gr.Accordion("Step 2: Review results", open=True):
                gr.Markdown(
                    "### Duplicate Analysis Results\nClick on a row to see more details about the duplicate match."
                )

                tabular_results_df = gr.Dataframe(
                    label="Duplicate Cell Matches",
                    headers=[
                        "File1",
                        "Row1",
                        "File2",
                        "Row2",
                        "Similarity_Score",
                        "Text1",
                        "Text2",
                    ],
                    wrap=True,
                    show_fullscreen_button=True,
                    show_search=True,
                    show_copy_button=True,
                )

                with gr.Row(equal_height=True):
                    tabular_selected_row_index = gr.Number(value=None, visible=False)
                    tabular_text1_preview = gr.Textbox(
                        label="Text from File 1", lines=3, interactive=False
                    )
                    tabular_text2_preview = gr.Textbox(
                        label="Text from File 2", lines=3, interactive=False
                    )

            with gr.Accordion("Step 3: Remove duplicates", open=True):
                gr.Markdown(
                    "### Remove Duplicate Rows\nSelect a file and click to remove duplicate rows based on the analysis above."
                )

                with gr.Row():
                    tabular_file_to_clean = gr.Dropdown(
                        choices=list(),
                        label="Select file to clean",
                        info="Choose which file to remove duplicates from",
                        visible=False,
                    )
                    clean_duplicates_btn = gr.Button(
                        value="Remove duplicate rows from selected file",
                        variant="secondary",
                        visible=False,
                    )

                tabular_cleaned_file = gr.File(
                    label="Download cleaned file (duplicates removed)",
                    visible=True,
                    interactive=False,
                )

        # Feedback elements are invisible until revealed by redaction action
        data_feedback_title = gr.Markdown(
            value="## Please give feedback", visible=False
        )
        data_feedback_radio = gr.Radio(
            label="Please give some feedback about the results of the redaction. A reminder that the app is only expected to identify about 60% of personally identifiable information in a given (typed) document.",
            choices=["The results were good", "The results were not good"],
            visible=False,
            show_label=True,
        )
        data_further_details_text = gr.Textbox(
            label="Please give more detailed feedback about the results:", visible=False
        )
        data_submit_feedback_btn = gr.Button(value="Submit feedback", visible=False)

    ###
    # SETTINGS TAB
    ###
    with gr.Tab(label="Redaction settings"):
        with gr.Accordion(
            "Custom allow, deny, and full page redaction lists", open=True
        ):
            with gr.Row():
                with gr.Column():
                    in_allow_list = gr.File(
                        label="Import allow list file - csv table with one column of a different word/phrase on each row (case insensitive). Terms in this file will not be redacted.",
                        file_count="multiple",
                        height=FILE_INPUT_HEIGHT,
                    )
                    in_allow_list_text = gr.Textbox(
                        label="Custom allow list load status"
                    )
                with gr.Column():
                    in_deny_list = gr.File(
                        label="Import custom deny list - csv table with one column of a different word/phrase on each row (case insensitive). Terms in this file will always be redacted.",
                        file_count="multiple",
                        height=FILE_INPUT_HEIGHT,
                    )
                    in_deny_list_text = gr.Textbox(label="Custom deny list load status")
                with gr.Column():
                    in_fully_redacted_list = gr.File(
                        label="Import fully redacted pages list - csv table with one column of page numbers on each row. Page numbers in this file will be fully redacted.",
                        file_count="multiple",
                        height=FILE_INPUT_HEIGHT,
                    )
                    in_fully_redacted_list_text = gr.Textbox(
                        label="Fully redacted page list load status"
                    )
            with gr.Accordion(
                "Manually modify custom allow, deny, and full page redaction lists (NOTE: you need to press Enter after modifying/adding an entry to the lists to apply them)",
                open=False,
            ):
                with gr.Row():
                    in_allow_list_state = gr.Dataframe(
                        value=pd.DataFrame(),
                        headers=["allow_list"],
                        col_count=(1, "fixed"),
                        row_count=(0, "dynamic"),
                        label="Allow list",
                        visible=True,
                        type="pandas",
                        interactive=True,
                        show_fullscreen_button=True,
                        show_copy_button=True,
                        wrap=True,
                    )
                    in_deny_list_state = gr.Dataframe(
                        value=pd.DataFrame(),
                        headers=["deny_list"],
                        col_count=(1, "fixed"),
                        row_count=(0, "dynamic"),
                        label="Deny list",
                        visible=True,
                        type="pandas",
                        interactive=True,
                        show_fullscreen_button=True,
                        show_copy_button=True,
                        wrap=True,
                    )
                    in_fully_redacted_list_state = gr.Dataframe(
                        value=pd.DataFrame(),
                        headers=["fully_redacted_pages_list"],
                        col_count=(1, "fixed"),
                        row_count=(0, "dynamic"),
                        label="Fully redacted pages",
                        visible=True,
                        type="pandas",
                        interactive=True,
                        show_fullscreen_button=True,
                        show_copy_button=True,
                        datatype="number",
                        wrap=True,
                    )
                with gr.Row():
                    with gr.Column(scale=2):
                        markdown_placeholder = gr.Markdown("")
                    with gr.Column(scale=1):
                        apply_fully_redacted_list_btn = gr.Button(
                            value="Apply whole page redaction list to document currently under review",
                            variant="secondary",
                        )

        with gr.Accordion("Select entity types to redact", open=True):
            # in_redact_entities = gr.Dropdown(
            #     value=CHOSEN_REDACT_ENTITIES,
            #     choices=FULL_ENTITY_LIST,
            #     multiselect=True,
            #     label="Local PII identification model (click empty space in box for full list)",
            # )
            # in_redact_comprehend_entities = gr.Dropdown(
            #     value=CHOSEN_COMPREHEND_ENTITIES,
            #     choices=FULL_COMPREHEND_ENTITY_LIST,
            #     multiselect=True,
            #     label="AWS Comprehend PII identification model (click empty space in box for full list)",
            # )
            in_redact_entities.render()
            in_redact_comprehend_entities.render()

            with gr.Row():
                max_fuzzy_spelling_mistakes_num = gr.Number(
                    label="Maximum number of spelling mistakes allowed for fuzzy matching (CUSTOM_FUZZY entity).",
                    value=DEFAULT_FUZZY_SPELLING_MISTAKES_NUM,
                    minimum=0,
                    maximum=9,
                    precision=0,
                )
                match_fuzzy_whole_phrase_bool = gr.Checkbox(
                    label="Should fuzzy search match on entire phrases in deny list (as opposed to each word individually)?",
                    value=True,
                )

        with gr.Accordion("Redact only selected pages", open=False):
            with gr.Row():
                page_min = gr.Number(
                    value=DEFAULT_PAGE_MIN,
                    precision=0,
                    minimum=0,
                    maximum=9999,
                    label="Lowest page to redact",
                )
                page_max = gr.Number(
                    value=DEFAULT_PAGE_MAX,
                    precision=0,
                    minimum=0,
                    maximum=9999,
                    label="Highest page to redact",
                )

        if SHOW_LANGUAGE_SELECTION:
            with gr.Accordion("Language selection", open=False):
                gr.Markdown(
                    """Note that AWS Textract is compatible with English, Spanish, Italian, Portuguese, French, and German, and handwriting detection is only available in English. AWS Comprehend for detecting PII is only compatible with English and Spanish.
                The local models (Tesseract and SpaCy) are compatible with the other languages in the list below. However, the language packs for these models need to be installed on your system. When you first run a document through the app, the language packs will be downloaded automatically, but please expect a delay as the models are large."""
                )
                with gr.Row():
                    chosen_language_full_name_drop = gr.Dropdown(
                        value=DEFAULT_LANGUAGE_FULL_NAME,
                        choices=MAPPED_LANGUAGE_CHOICES,
                        label="Chosen language",
                        multiselect=False,
                        visible=True,
                    )
                    chosen_language_drop = gr.Dropdown(
                        value=DEFAULT_LANGUAGE,
                        choices=LANGUAGE_CHOICES,
                        label="Chosen language short code",
                        multiselect=False,
                        visible=True,
                        interactive=False,
                    )
        else:
            chosen_language_full_name_drop = gr.Dropdown(
                value=DEFAULT_LANGUAGE_FULL_NAME,
                choices=MAPPED_LANGUAGE_CHOICES,
                label="Chosen language",
                multiselect=False,
                visible=False,
            )
            chosen_language_drop = gr.Dropdown(
                value=DEFAULT_LANGUAGE,
                choices=LANGUAGE_CHOICES,
                label="Chosen language short code",
                multiselect=False,
                visible=False,
            )

        with gr.Accordion("Use API keys for AWS services", open=False):
            with gr.Row():
                aws_access_key_textbox = gr.Textbox(
                    value="",
                    label="AWS access key for account with permissions for AWS Textract and Comprehend",
                    visible=True,
                    type="password",
                )
                aws_secret_key_textbox = gr.Textbox(
                    value="",
                    label="AWS secret key for account with permissions for AWS Textract and Comprehend",
                    visible=True,
                    type="password",
                )

        with gr.Accordion("Log file outputs", open=False):
            log_files_output = gr.File(label="Log file output", interactive=False)

        with gr.Accordion("Combine multiple review files", open=False):
            multiple_review_files_in_out = gr.File(
                label="Combine multiple review_file.csv files together here.",
                file_count="multiple",
                file_types=[".csv"],
            )
            merge_multiple_review_files_btn = gr.Button(
                "Merge multiple review files into one", variant="primary"
            )

        with gr.Accordion("View all output files from this session", open=False):
            all_output_files_btn = gr.Button(
                "Click here to view all output files", variant="secondary"
            )
            all_output_files = gr.File(
                label="All files in output folder",
                file_count="multiple",
                file_types=[".csv"],
                interactive=False,
            )

    ###
    # UI INTERACTION
    ###

    ###
    # PDF/IMAGE REDACTION
    ###
    # Recalculate estimated costs based on changes to inputs
    if SHOW_COSTS == "True":
        # Calculate costs
        total_pdf_page_count.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        text_extract_method_radio.change(
            fn=check_for_relevant_ocr_output_with_words,
            inputs=[
                doc_file_name_no_extension_textbox,
                text_extract_method_radio,
                output_folder_textbox,
            ],
            outputs=[relevant_ocr_output_with_words_found_checkbox],
        ).success(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        pii_identification_method_drop.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        handwrite_signature_checkbox.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        textract_output_found_checkbox.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        only_extract_text_radio.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )
        textract_output_found_checkbox.change(
            calculate_aws_costs,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
            ],
            outputs=[estimated_aws_costs_number],
        )

        # Calculate time taken
        total_pdf_page_count.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        text_extract_method_radio.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        pii_identification_method_drop.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        handwrite_signature_checkbox.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        textract_output_found_checkbox.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                handwrite_signature_checkbox,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        only_extract_text_radio.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        textract_output_found_checkbox.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )
        relevant_ocr_output_with_words_found_checkbox.change(
            calculate_time_taken,
            inputs=[
                total_pdf_page_count,
                text_extract_method_radio,
                pii_identification_method_drop,
                textract_output_found_checkbox,
                only_extract_text_radio,
                relevant_ocr_output_with_words_found_checkbox,
            ],
            outputs=[estimated_time_taken_number],
        )

    # Allow user to select items from cost code dataframe for cost code
    if SHOW_COSTS == "True" and (
        GET_COST_CODES == "True" or ENFORCE_COST_CODES == "True"
    ):
        cost_code_dataframe.select(
            df_select_callback_cost,
            inputs=[cost_code_dataframe],
            outputs=[cost_code_choice_drop],
        )
        reset_cost_code_dataframe_button.click(
            reset_base_dataframe,
            inputs=[cost_code_dataframe_base],
            outputs=[cost_code_dataframe],
        )

        cost_code_choice_drop.select(
            update_cost_code_dataframe_from_dropdown_select,
            inputs=[cost_code_choice_drop, cost_code_dataframe_base],
            outputs=[cost_code_dataframe],
        )

    in_doc_files.upload(
        fn=get_input_file_names,
        inputs=[in_doc_files],
        outputs=[
            doc_file_name_no_extension_textbox,
            doc_file_name_with_extension_textbox,
            doc_full_file_name_textbox,
            doc_file_name_textbox_list,
            total_pdf_page_count,
        ],
    ).success(
        fn=prepare_image_or_pdf,
        inputs=[
            in_doc_files,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            first_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool_false,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_page_line_level_ocr_results_df_base,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        fn=check_for_existing_textract_file,
        inputs=[doc_file_name_no_extension_textbox, output_folder_textbox],
        outputs=[textract_output_found_checkbox],
    ).success(
        fn=check_for_relevant_ocr_output_with_words,
        inputs=[
            doc_file_name_no_extension_textbox,
            text_extract_method_radio,
            output_folder_textbox,
        ],
        outputs=[relevant_ocr_output_with_words_found_checkbox],
    )

    # Run redaction function
    document_redact_btn.click(
        fn=reset_state_vars,
        outputs=[
            all_image_annotations_state,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            comprehend_query_number,
            textract_metadata_textbox,
            annotator,
            output_file_list_state,
            log_files_output_list_state,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            pdf_doc_state,
            duplication_file_path_outputs_list_state,
            redaction_output_summary_textbox,
            is_a_textract_api_call,
            textract_query_number,
            all_page_line_level_ocr_results_with_words,
        ],
    ).success(
        fn=enforce_cost_codes,
        inputs=[
            enforce_cost_code_textbox,
            cost_code_choice_drop,
            cost_code_dataframe_base,
        ],
    ).success(
        fn=choose_and_run_redactor,
        inputs=[
            in_doc_files,
            prepared_pdf_state,
            images_pdf_state,
            in_redact_entities,
            in_redact_comprehend_entities,
            text_extract_method_radio,
            in_allow_list_state,
            in_deny_list_state,
            in_fully_redacted_list_state,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            output_file_list_state,
            log_files_output_list_state,
            first_loop_state,
            page_min,
            page_max,
            actual_time_taken_number,
            handwrite_signature_checkbox,
            textract_metadata_textbox,
            all_image_annotations_state,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            pdf_doc_state,
            current_loop_page_number,
            page_break_return,
            pii_identification_method_drop,
            comprehend_query_number,
            max_fuzzy_spelling_mistakes_num,
            match_fuzzy_whole_phrase_bool,
            aws_access_key_textbox,
            aws_secret_key_textbox,
            annotate_max_pages,
            review_file_df,
            output_folder_textbox,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            only_extract_text_radio,
            duplication_file_path_outputs_list_state,
            latest_review_file_path,
            input_folder_textbox,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            chosen_local_model_textbox,
            chosen_language_drop,
        ],
        outputs=[
            redaction_output_summary_textbox,
            output_file,
            output_file_list_state,
            latest_file_completed_num,
            log_files_output,
            log_files_output_list_state,
            actual_time_taken_number,
            textract_metadata_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            current_loop_page_number,
            page_break_return,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            comprehend_query_number,
            input_pdf_for_review,
            annotate_max_pages,
            annotate_max_pages_bottom,
            prepared_pdf_state,
            images_pdf_state,
            review_file_df,
            page_sizes,
            duplication_file_path_outputs_list_state,
            in_duplicate_pages,
            latest_review_file_path,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            backup_review_state,
            task_textbox,
        ],
        api_name="redact_doc",
        show_progress_on=[redaction_output_summary_textbox],
    )

    # If a file has been completed, the function will continue onto the next document
    latest_file_completed_num.change(
        fn=choose_and_run_redactor,
        inputs=[
            in_doc_files,
            prepared_pdf_state,
            images_pdf_state,
            in_redact_entities,
            in_redact_comprehend_entities,
            text_extract_method_radio,
            in_allow_list_state,
            in_deny_list_state,
            in_fully_redacted_list_state,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            output_file_list_state,
            log_files_output_list_state,
            second_loop_state,
            page_min,
            page_max,
            actual_time_taken_number,
            handwrite_signature_checkbox,
            textract_metadata_textbox,
            all_image_annotations_state,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            pdf_doc_state,
            current_loop_page_number,
            page_break_return,
            pii_identification_method_drop,
            comprehend_query_number,
            max_fuzzy_spelling_mistakes_num,
            match_fuzzy_whole_phrase_bool,
            aws_access_key_textbox,
            aws_secret_key_textbox,
            annotate_max_pages,
            review_file_df,
            output_folder_textbox,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            only_extract_text_radio,
            duplication_file_path_outputs_list_state,
            latest_review_file_path,
            input_folder_textbox,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            chosen_local_model_textbox,
            chosen_language_drop,
        ],
        outputs=[
            redaction_output_summary_textbox,
            output_file,
            output_file_list_state,
            latest_file_completed_num,
            log_files_output,
            log_files_output_list_state,
            actual_time_taken_number,
            textract_metadata_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            current_loop_page_number,
            page_break_return,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            comprehend_query_number,
            input_pdf_for_review,
            annotate_max_pages,
            annotate_max_pages_bottom,
            prepared_pdf_state,
            images_pdf_state,
            review_file_df,
            page_sizes,
            duplication_file_path_outputs_list_state,
            in_duplicate_pages,
            latest_review_file_path,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            backup_review_state,
            task_textbox,
        ],
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        fn=update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            page_min,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        fn=check_for_existing_textract_file,
        inputs=[doc_file_name_no_extension_textbox, output_folder_textbox],
        outputs=[textract_output_found_checkbox],
    ).success(
        fn=check_for_relevant_ocr_output_with_words,
        inputs=[
            doc_file_name_no_extension_textbox,
            text_extract_method_radio,
            output_folder_textbox,
        ],
        outputs=[relevant_ocr_output_with_words_found_checkbox],
    ).success(
        fn=reveal_feedback_buttons,
        outputs=[
            pdf_feedback_radio,
            pdf_further_details_text,
            pdf_submit_feedback_btn,
            pdf_feedback_title,
        ],
    ).success(
        fn=reset_aws_call_vars, outputs=[comprehend_query_number, textract_query_number]
    )

    # If the line level ocr results are changed by load in by user or by a new redaction task, replace the ocr results displayed in the table
    all_page_line_level_ocr_results_df_base.change(
        reset_ocr_base_dataframe,
        inputs=[all_page_line_level_ocr_results_df_base],
        outputs=[all_page_line_level_ocr_results_df],
    )
    all_page_line_level_ocr_results_with_words_df_base.change(
        reset_ocr_with_words_base_dataframe,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            page_entity_dropdown_redaction,
        ],
        outputs=[
            all_page_line_level_ocr_results_with_words_df,
            backup_all_page_line_level_ocr_results_with_words_df_base,
        ],
    )

    # Send whole document to Textract for text extraction
    send_document_to_textract_api_btn.click(
        analyse_document_with_textract_api,
        inputs=[
            prepared_pdf_state,
            s3_whole_document_textract_input_subfolder,
            s3_whole_document_textract_output_subfolder,
            textract_job_detail_df,
            s3_whole_document_textract_default_bucket,
            output_folder_textbox,
            handwrite_signature_checkbox,
            successful_textract_api_call_number,
            total_pdf_page_count,
        ],
        outputs=[
            job_output_textbox,
            job_id_textbox,
            job_type_dropdown,
            successful_textract_api_call_number,
            is_a_textract_api_call,
            textract_query_number,
            task_textbox,
        ],
    ).success(check_for_provided_job_id, inputs=[job_id_textbox]).success(
        poll_whole_document_textract_analysis_progress_and_download,
        inputs=[
            job_id_textbox,
            job_type_dropdown,
            s3_whole_document_textract_output_subfolder,
            doc_file_name_no_extension_textbox,
            textract_job_detail_df,
            s3_whole_document_textract_default_bucket,
            output_folder_textbox,
            s3_whole_document_textract_logs_subfolder,
            local_whole_document_textract_logs_subfolder,
        ],
        outputs=[
            textract_job_output_file,
            job_current_status,
            textract_job_detail_df,
            doc_file_name_no_extension_textbox,
        ],
    ).success(
        fn=check_for_existing_textract_file,
        inputs=[doc_file_name_no_extension_textbox, output_folder_textbox],
        outputs=[textract_output_found_checkbox],
    )

    check_state_of_textract_api_call_btn.click(
        check_for_provided_job_id, inputs=[job_id_textbox]
    ).success(
        poll_whole_document_textract_analysis_progress_and_download,
        inputs=[
            job_id_textbox,
            job_type_dropdown,
            s3_whole_document_textract_output_subfolder,
            doc_file_name_no_extension_textbox,
            textract_job_detail_df,
            s3_whole_document_textract_default_bucket,
            output_folder_textbox,
            s3_whole_document_textract_logs_subfolder,
            local_whole_document_textract_logs_subfolder,
        ],
        outputs=[
            textract_job_output_file,
            job_current_status,
            textract_job_detail_df,
            doc_file_name_no_extension_textbox,
        ],
    ).success(
        fn=check_for_existing_textract_file,
        inputs=[doc_file_name_no_extension_textbox, output_folder_textbox],
        outputs=[textract_output_found_checkbox],
    )

    textract_job_detail_df.select(
        df_select_callback_textract_api,
        inputs=[textract_output_found_checkbox],
        outputs=[job_id_textbox, job_type_dropdown, selected_job_id_row],
    )

    convert_textract_outputs_to_ocr_results.click(
        replace_existing_pdf_input_for_whole_document_outputs,
        inputs=[
            s3_whole_document_textract_input_subfolder,
            doc_file_name_no_extension_textbox,
            output_folder_textbox,
            s3_whole_document_textract_default_bucket,
            in_doc_files,
            input_folder_textbox,
        ],
        outputs=[
            in_doc_files,
            doc_file_name_no_extension_textbox,
            doc_file_name_with_extension_textbox,
            doc_full_file_name_textbox,
            doc_file_name_textbox_list,
            total_pdf_page_count,
        ],
    ).success(
        fn=prepare_image_or_pdf,
        inputs=[
            in_doc_files,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            first_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool_false,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_page_line_level_ocr_results_df_base,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        fn=check_for_existing_textract_file,
        inputs=[doc_file_name_no_extension_textbox, output_folder_textbox],
        outputs=[textract_output_found_checkbox],
    ).success(
        fn=check_for_relevant_ocr_output_with_words,
        inputs=[
            doc_file_name_no_extension_textbox,
            text_extract_method_radio,
            output_folder_textbox,
        ],
        outputs=[relevant_ocr_output_with_words_found_checkbox],
    ).success(
        fn=check_textract_outputs_exist, inputs=[textract_output_found_checkbox]
    ).success(
        fn=reset_state_vars,
        outputs=[
            all_image_annotations_state,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            comprehend_query_number,
            textract_metadata_textbox,
            annotator,
            output_file_list_state,
            log_files_output_list_state,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            pdf_doc_state,
            duplication_file_path_outputs_list_state,
            redaction_output_summary_textbox,
            is_a_textract_api_call,
            textract_query_number,
        ],
    ).success(
        fn=choose_and_run_redactor,
        inputs=[
            in_doc_files,
            prepared_pdf_state,
            images_pdf_state,
            in_redact_entities,
            in_redact_comprehend_entities,
            textract_only_method_drop,
            in_allow_list_state,
            in_deny_list_state,
            in_fully_redacted_list_state,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            output_file_list_state,
            log_files_output_list_state,
            first_loop_state,
            page_min,
            page_max,
            actual_time_taken_number,
            handwrite_signature_checkbox,
            textract_metadata_textbox,
            all_image_annotations_state,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            pdf_doc_state,
            current_loop_page_number,
            page_break_return,
            no_redaction_method_drop,
            comprehend_query_number,
            max_fuzzy_spelling_mistakes_num,
            match_fuzzy_whole_phrase_bool,
            aws_access_key_textbox,
            aws_secret_key_textbox,
            annotate_max_pages,
            review_file_df,
            output_folder_textbox,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            only_extract_text_radio,
            duplication_file_path_outputs_list_state,
            latest_review_file_path,
            input_folder_textbox,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            chosen_local_model_textbox,
            chosen_language_drop,
        ],
        outputs=[
            redaction_output_summary_textbox,
            output_file,
            output_file_list_state,
            latest_file_completed_num,
            log_files_output,
            log_files_output_list_state,
            actual_time_taken_number,
            textract_metadata_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            current_loop_page_number,
            page_break_return,
            all_page_line_level_ocr_results_df_base,
            all_decision_process_table_state,
            comprehend_query_number,
            input_pdf_for_review,
            annotate_max_pages,
            annotate_max_pages_bottom,
            prepared_pdf_state,
            images_pdf_state,
            review_file_df,
            page_sizes,
            duplication_file_path_outputs_list_state,
            in_duplicate_pages,
            latest_review_file_path,
            textract_query_number,
            latest_ocr_file_path,
            all_page_line_level_ocr_results,
            all_page_line_level_ocr_results_with_words,
            all_page_line_level_ocr_results_with_words_df_base,
            backup_review_state,
            task_textbox,
        ],
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        fn=update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            page_min,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    ###
    # REVIEW PDF REDACTIONS
    ###

    # Upload previous PDF for modifying redactions
    upload_pdf_for_review_btn.click(
        fn=reset_review_vars,
        inputs=None,
        outputs=[recogniser_entity_dataframe, recogniser_entity_dataframe_base],
    ).success(
        fn=get_input_file_names,
        inputs=[input_pdf_for_review],
        outputs=[
            doc_file_name_no_extension_textbox,
            doc_file_name_with_extension_textbox,
            doc_full_file_name_textbox,
            doc_file_name_textbox_list,
            total_pdf_page_count,
        ],
    ).success(
        fn=prepare_image_or_pdf,
        inputs=[
            input_pdf_for_review,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            second_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_page_line_level_ocr_results_df_base,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        api_name="prepare_doc",
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    # Upload previous review CSV files for modifying redactions
    upload_review_files_btn.click(
        fn=prepare_image_or_pdf,
        inputs=[
            input_review_files,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            second_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_page_line_level_ocr_results_df_base,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        show_progress_on=[redaction_output_summary_textbox],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    # Manual updates to review df
    review_file_df.input(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_entity_dataframe_row,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    # Page number controls
    annotate_current_page.submit(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    annotation_last_page_button.click(
        fn=decrease_page,
        inputs=[annotate_current_page, all_image_annotations_state],
        outputs=[annotate_current_page, annotate_current_page_bottom],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    annotation_next_page_button.click(
        fn=increase_page,
        inputs=[annotate_current_page, all_image_annotations_state],
        outputs=[annotate_current_page, annotate_current_page_bottom],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    annotation_last_page_button_bottom.click(
        fn=decrease_page,
        inputs=[annotate_current_page, all_image_annotations_state],
        outputs=[annotate_current_page, annotate_current_page_bottom],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    annotation_next_page_button_bottom.click(
        fn=increase_page,
        inputs=[annotate_current_page, all_image_annotations_state],
        outputs=[annotate_current_page, annotate_current_page_bottom],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    annotate_current_page_bottom.submit(
        update_other_annotator_number_from_current,
        inputs=[annotate_current_page_bottom],
        outputs=[annotate_current_page],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_previous_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    # Apply page redactions
    annotation_button_apply.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        scroll_to_output=True,
        show_progress_on=[input_pdf_for_review],
    )

    # Save current page manual redactions
    update_current_page_redactions_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    ###
    # Review and exclude suggested redactions
    ###

    # Review table controls
    recogniser_entity_dropdown.select(
        update_entities_df_recogniser_entities,
        inputs=[
            recogniser_entity_dropdown,
            recogniser_entity_dataframe_base,
            page_entity_dropdown,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dataframe,
            text_entity_dropdown,
            page_entity_dropdown,
        ],
    )
    page_entity_dropdown.select(
        update_entities_df_page,
        inputs=[
            page_entity_dropdown,
            recogniser_entity_dataframe_base,
            recogniser_entity_dropdown,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dataframe,
            recogniser_entity_dropdown,
            text_entity_dropdown,
        ],
    )
    text_entity_dropdown.select(
        update_entities_df_text,
        inputs=[
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            recogniser_entity_dropdown,
            page_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dataframe,
            recogniser_entity_dropdown,
            page_entity_dropdown,
        ],
    )

    # Clicking on a cell in the recogniser entity dataframe will take you to that page, and also highlight the target redaction box in blue
    recogniser_entity_dataframe.select(
        df_select_callback_dataframe_row,
        inputs=[recogniser_entity_dataframe],
        outputs=[selected_entity_dataframe_row, selected_entity_dataframe_row_text],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_and_merge_current_page_annotations,
        inputs=[
            page_sizes,
            annotate_current_page,
            all_image_annotations_state,
            review_file_df,
        ],
        outputs=[review_file_df],
    ).success(
        update_selected_review_df_row_colour,
        inputs=[
            selected_entity_dataframe_row,
            review_file_df,
            selected_entity_id,
            selected_entity_colour,
        ],
        outputs=[review_file_df, selected_entity_id, selected_entity_colour],
    ).success(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_entity_dataframe_row,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        increase_bottom_page_count_based_on_top,
        inputs=[annotate_current_page],
        outputs=[annotate_current_page_bottom],
    )

    reset_dropdowns_btn.click(
        reset_dropdowns,
        inputs=[recogniser_entity_dataframe_base],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    ### Exclude current selection from annotator and outputs
    # Exclude only selected row
    exclude_selected_row_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_and_merge_current_page_annotations,
        inputs=[
            page_sizes,
            annotate_current_page,
            all_image_annotations_state,
            review_file_df,
        ],
        outputs=[review_file_df],
    ).success(
        exclude_selected_items_from_redaction,
        inputs=[
            review_file_df,
            selected_entity_dataframe_row,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
        ],
        outputs=[
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            backup_review_state,
            backup_image_annotations_state,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            recogniser_entity_dataframe_base,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown,
        ],
    )

    # Exclude all items with same text as selected row
    exclude_text_with_same_as_selected_row_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_and_merge_current_page_annotations,
        inputs=[
            page_sizes,
            annotate_current_page,
            all_image_annotations_state,
            review_file_df,
        ],
        outputs=[review_file_df],
    ).success(
        get_all_rows_with_same_text,
        inputs=[recogniser_entity_dataframe_base, selected_entity_dataframe_row_text],
        outputs=[recogniser_entity_dataframe_same_text],
    ).success(
        exclude_selected_items_from_redaction,
        inputs=[
            review_file_df,
            recogniser_entity_dataframe_same_text,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
        ],
        outputs=[
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            backup_review_state,
            backup_image_annotations_state,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            recogniser_entity_dataframe_base,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown,
        ],
    )

    # Exclude everything visible in table
    exclude_selected_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_and_merge_current_page_annotations,
        inputs=[
            page_sizes,
            annotate_current_page,
            all_image_annotations_state,
            review_file_df,
        ],
        outputs=[review_file_df],
    ).success(
        exclude_selected_items_from_redaction,
        inputs=[
            review_file_df,
            recogniser_entity_dataframe,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
        ],
        outputs=[
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            backup_review_state,
            backup_image_annotations_state,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            recogniser_entity_dataframe_base,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown,
        ],
    )

    # Undo last redaction exclusion action
    undo_last_removal_btn.click(
        undo_last_removal,
        inputs=[
            backup_review_state,
            backup_image_annotations_state,
            backup_recogniser_entity_dataframe_base,
        ],
        outputs=[
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    ###
    # Add new redactions with table selection
    ###
    page_entity_dropdown_redaction.select(
        update_redact_choice_df_from_page_dropdown,
        inputs=[
            page_entity_dropdown_redaction,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        outputs=[all_page_line_level_ocr_results_with_words_df],
    )

    multi_word_search_text.submit(
        fn=run_full_search_and_analysis,
        inputs=[
            multi_word_search_text,
            all_page_line_level_ocr_results_with_words_df_base,
            similarity_search_score_minimum,
        ],
        outputs=[
            all_page_line_level_ocr_results_with_words_df,
            duplicate_files_out,
            full_duplicate_data_by_file,
        ],
    )

    multi_word_search_text_btn.click(
        fn=run_full_search_and_analysis,
        inputs=[
            multi_word_search_text,
            all_page_line_level_ocr_results_with_words_df_base,
            similarity_search_score_minimum,
        ],
        outputs=[
            all_page_line_level_ocr_results_with_words_df,
            duplicate_files_out,
            full_duplicate_data_by_file,
        ],
    )

    # Clicking on a cell in the redact items table will take you to that page
    all_page_line_level_ocr_results_with_words_df.select(
        df_select_callback_dataframe_row_ocr_with_words,
        inputs=[all_page_line_level_ocr_results_with_words_df],
        outputs=[
            selected_entity_dataframe_row_redact,
            selected_entity_dataframe_row_text_redact,
        ],
    ).success(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_and_merge_current_page_annotations,
        inputs=[
            page_sizes,
            annotate_current_page,
            all_image_annotations_state,
            review_file_df,
        ],
        outputs=[review_file_df],
    ).success(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_entity_dataframe_row_redact,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        increase_bottom_page_count_based_on_top,
        inputs=[annotate_current_page],
        outputs=[annotate_current_page_bottom],
    )

    # Reset dropdowns
    reset_dropdowns_btn_new.click(
        reset_dropdowns,
        inputs=[all_page_line_level_ocr_results_with_words_df_base],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown_redaction,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    # Redact everything visible in table
    redact_selected_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        create_annotation_objects_from_filtered_ocr_results_with_words,
        inputs=[
            all_page_line_level_ocr_results_with_words_df,
            all_page_line_level_ocr_results_with_words_df_base,
            page_sizes,
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            new_redaction_text_label,
            colour_label,
            annotate_current_page,
        ],
        outputs=[
            all_image_annotations_state,
            backup_image_annotations_state,
            review_file_df,
            backup_review_state,
            recogniser_entity_dataframe,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            recogniser_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown_redaction,
        ],
    )

    # Reset redaction table following filtering
    reset_ocr_with_words_df_btn.click(
        reset_ocr_with_words_base_dataframe,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            page_entity_dropdown_redaction,
        ],
        outputs=[
            all_page_line_level_ocr_results_with_words_df,
            backup_all_page_line_level_ocr_results_with_words_df_base,
        ],
    )

    # Redact current selection
    redact_selected_row_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        create_annotation_objects_from_filtered_ocr_results_with_words,
        inputs=[
            selected_entity_dataframe_row_redact,
            all_page_line_level_ocr_results_with_words_df_base,
            page_sizes,
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            new_redaction_text_label,
            colour_label,
            annotate_current_page,
        ],
        outputs=[
            all_image_annotations_state,
            backup_image_annotations_state,
            review_file_df,
            backup_review_state,
            recogniser_entity_dataframe,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            recogniser_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown_redaction,
        ],
    )

    # Redact all items with same text as selected row
    redact_text_with_same_as_selected_row_btn.click(
        update_all_page_annotation_object_based_on_previous_page,
        inputs=[
            annotator,
            annotate_current_page,
            annotate_current_page,
            all_image_annotations_state,
            page_sizes,
        ],
        outputs=[
            all_image_annotations_state,
            annotate_previous_page,
            annotate_current_page_bottom,
        ],
    ).success(
        get_all_rows_with_same_text_redact,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            selected_entity_dataframe_row_text_redact,
        ],
        outputs=[to_redact_dataframe_same_text],
    ).success(
        create_annotation_objects_from_filtered_ocr_results_with_words,
        inputs=[
            to_redact_dataframe_same_text,
            all_page_line_level_ocr_results_with_words_df_base,
            page_sizes,
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
            new_redaction_text_label,
            colour_label,
            annotate_current_page,
        ],
        outputs=[
            all_image_annotations_state,
            backup_image_annotations_state,
            review_file_df,
            backup_review_state,
            recogniser_entity_dataframe,
            backup_recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    ).success(
        update_all_entity_df_dropdowns,
        inputs=[
            all_page_line_level_ocr_results_with_words_df_base,
            recogniser_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
        ],
        outputs=[
            recogniser_entity_dropdown,
            text_entity_dropdown,
            page_entity_dropdown_redaction,
        ],
    )

    # Undo last redaction action
    undo_last_redact_btn.click(
        undo_last_removal,
        inputs=[
            backup_review_state,
            backup_image_annotations_state,
            backup_recogniser_entity_dataframe_base,
        ],
        outputs=[
            review_file_df,
            all_image_annotations_state,
            recogniser_entity_dataframe_base,
        ],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    ).success(
        apply_redactions_to_review_df_and_files,
        inputs=[
            annotator,
            doc_full_file_name_textbox,
            pdf_doc_state,
            all_image_annotations_state,
            annotate_current_page,
            review_file_df,
            output_folder_textbox,
            do_not_save_pdf_state,
            page_sizes,
        ],
        outputs=[
            pdf_doc_state,
            all_image_annotations_state,
            input_pdf_for_review,
            log_files_output,
            review_file_df,
        ],
        show_progress_on=[input_pdf_for_review],
    )

    ###
    # Review OCR text
    ###
    all_page_line_level_ocr_results_df.select(
        df_select_callback_ocr,
        inputs=[all_page_line_level_ocr_results_df],
        outputs=[annotate_current_page, selected_ocr_dataframe_row],
    ).success(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_ocr_dataframe_row,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        increase_bottom_page_count_based_on_top,
        inputs=[annotate_current_page],
        outputs=[annotate_current_page_bottom],
    )

    # Reset the OCR results filter
    reset_all_ocr_results_btn.click(
        reset_ocr_base_dataframe,
        inputs=[all_page_line_level_ocr_results_df_base],
        outputs=[all_page_line_level_ocr_results_df],
    )

    # Convert review file to xfdf Adobe format
    convert_review_file_to_adobe_btn.click(
        fn=get_input_file_names,
        inputs=[input_pdf_for_review],
        outputs=[
            doc_file_name_no_extension_textbox,
            doc_file_name_with_extension_textbox,
            doc_full_file_name_textbox,
            doc_file_name_textbox_list,
            total_pdf_page_count,
        ],
    ).success(
        fn=prepare_image_or_pdf,
        inputs=[
            input_pdf_for_review,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            second_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_line_level_ocr_results_df_placeholder,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        show_progress_on=[adobe_review_files_out],
    ).success(
        convert_df_to_xfdf,
        inputs=[
            input_pdf_for_review,
            pdf_doc_state,
            images_pdf_state,
            output_folder_textbox,
            document_cropboxes,
            page_sizes,
        ],
        outputs=[adobe_review_files_out],
    )

    # Convert xfdf Adobe file back to review_file.csv
    convert_adobe_to_review_file_btn.click(
        fn=get_input_file_names,
        inputs=[adobe_review_files_out],
        outputs=[
            doc_file_name_no_extension_textbox,
            doc_file_name_with_extension_textbox,
            doc_full_file_name_textbox,
            doc_file_name_textbox_list,
            total_pdf_page_count,
        ],
    ).success(
        fn=prepare_image_or_pdf,
        inputs=[
            adobe_review_files_out,
            text_extract_method_radio,
            all_page_line_level_ocr_results_df_base,
            all_page_line_level_ocr_results_with_words_df_base,
            latest_file_completed_num,
            redaction_output_summary_textbox,
            second_loop_state,
            annotate_max_pages,
            all_image_annotations_state,
            prepare_for_review_bool,
            in_fully_redacted_list_state,
            output_folder_textbox,
            input_folder_textbox,
            prepare_images_bool_false,
            page_sizes,
            pdf_doc_state,
        ],
        outputs=[
            redaction_output_summary_textbox,
            prepared_pdf_state,
            images_pdf_state,
            annotate_max_pages,
            annotate_max_pages_bottom,
            pdf_doc_state,
            all_image_annotations_state,
            review_file_df,
            document_cropboxes,
            page_sizes,
            textract_output_found_checkbox,
            all_img_details_state,
            all_line_level_ocr_results_df_placeholder,
            relevant_ocr_output_with_words_found_checkbox,
            all_page_line_level_ocr_results_with_words_df_base,
        ],
        show_progress_on=[adobe_review_files_out],
    ).success(
        fn=convert_xfdf_to_dataframe,
        inputs=[
            adobe_review_files_out,
            pdf_doc_state,
            images_pdf_state,
            output_folder_textbox,
        ],
        outputs=[input_pdf_for_review],
        scroll_to_output=True,
    )

    ###
    # WORD/TABULAR DATA REDACTION
    ###
    in_data_files.upload(
        fn=put_columns_in_df,
        inputs=[in_data_files],
        outputs=[in_colnames, in_excel_sheets],
    ).success(
        fn=get_input_file_names,
        inputs=[in_data_files],
        outputs=[
            data_file_name_no_extension_textbox,
            data_file_name_with_extension_textbox,
            data_full_file_name_textbox,
            data_file_name_textbox_list,
            total_pdf_page_count,
        ],
    )

    tabular_data_redact_btn.click(
        reset_data_vars,
        outputs=[
            actual_time_taken_number,
            log_files_output_list_state,
            comprehend_query_number,
        ],
    ).success(
        fn=anonymise_files_with_open_text,
        inputs=[
            in_data_files,
            in_text,
            anon_strategy,
            in_colnames,
            in_redact_entities,
            in_allow_list_state,
            text_tabular_files_done,
            text_output_summary,
            text_output_file_list_state,
            log_files_output_list_state,
            in_excel_sheets,
            first_loop_state,
            output_folder_textbox,
            in_deny_list_state,
            max_fuzzy_spelling_mistakes_num,
            pii_identification_method_drop_tabular,
            in_redact_comprehend_entities,
            comprehend_query_number,
            aws_access_key_textbox,
            aws_secret_key_textbox,
            actual_time_taken_number,
            do_initial_clean,
            chosen_language_drop,
        ],
        outputs=[
            text_output_summary,
            text_output_file,
            text_output_file_list_state,
            text_tabular_files_done,
            log_files_output,
            log_files_output_list_state,
            actual_time_taken_number,
            comprehend_query_number,
        ],
        api_name="redact_data",
        show_progress_on=[text_output_summary],
    )

    # If the output file count text box changes, keep going with redacting each data file until done
    text_tabular_files_done.change(
        fn=anonymise_files_with_open_text,
        inputs=[
            in_data_files,
            in_text,
            anon_strategy,
            in_colnames,
            in_redact_entities,
            in_allow_list_state,
            text_tabular_files_done,
            text_output_summary,
            text_output_file_list_state,
            log_files_output_list_state,
            in_excel_sheets,
            second_loop_state,
            output_folder_textbox,
            in_deny_list_state,
            max_fuzzy_spelling_mistakes_num,
            pii_identification_method_drop_tabular,
            in_redact_comprehend_entities,
            comprehend_query_number,
            aws_access_key_textbox,
            aws_secret_key_textbox,
            actual_time_taken_number,
            do_initial_clean,
            chosen_language_drop,
        ],
        outputs=[
            text_output_summary,
            text_output_file,
            text_output_file_list_state,
            text_tabular_files_done,
            log_files_output,
            log_files_output_list_state,
            actual_time_taken_number,
            comprehend_query_number,
        ],
        show_progress_on=[text_output_summary],
    ).success(
        fn=reveal_feedback_buttons,
        outputs=[
            data_feedback_radio,
            data_further_details_text,
            data_submit_feedback_btn,
            data_feedback_title,
        ],
    )

    ###
    # IDENTIFY DUPLICATE PAGES
    ###

    find_duplicate_pages_btn.click(
        fn=run_duplicate_analysis,
        inputs=[
            in_duplicate_pages,
            duplicate_threshold_input,
            min_word_count_input,
            min_consecutive_pages_input,
            greedy_match_input,
            combine_page_text_for_duplicates_bool,
            output_folder_textbox,
        ],
        outputs=[
            results_df_preview,
            duplicate_files_out,
            full_duplicate_data_by_file,
            actual_time_taken_number,
            task_textbox,
        ],
        show_progress_on=[results_df_preview],
    )

    # full_duplicated_data_df,
    results_df_preview.select(
        fn=handle_selection_and_preview,
        inputs=[results_df_preview, full_duplicate_data_by_file],
        outputs=[
            selected_duplicate_data_row_index,
            page1_text_preview,
            page2_text_preview,
        ],
    )

    # When the user clicks the "Exclude" button
    exclude_match_btn.click(
        fn=exclude_match,
        inputs=[results_df_preview, selected_duplicate_data_row_index],
        outputs=[
            results_df_preview,
            duplicate_files_out,
            page1_text_preview,
            page2_text_preview,
        ],
    )

    apply_match_btn.click(
        fn=create_annotation_objects_from_duplicates,
        inputs=[
            results_df_preview,
            all_page_line_level_ocr_results_df_base,
            page_sizes,
            combine_page_text_for_duplicates_bool,
        ],
        outputs=[new_duplicate_search_annotation_object],
    ).success(
        fn=apply_whole_page_redactions_from_list,
        inputs=[
            in_fully_redacted_list_state,
            doc_file_name_with_extension_textbox,
            review_file_df,
            duplicate_files_out,
            pdf_doc_state,
            page_sizes,
            all_image_annotations_state,
            combine_page_text_for_duplicates_bool,
            new_duplicate_search_annotation_object,
        ],
        outputs=[review_file_df, all_image_annotations_state],
    ).success(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_entity_dataframe_row,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    ###
    # TABULAR DUPLICATE DETECTION
    ###

    # Event handlers
    in_tabular_duplicate_files.upload(
        fn=put_columns_in_df,
        inputs=[in_tabular_duplicate_files],
        outputs=[tabular_text_columns, in_excel_tabular_sheets],
    )

    find_tabular_duplicates_btn.click(
        fn=run_tabular_duplicate_detection,
        inputs=[
            in_tabular_duplicate_files,
            tabular_duplicate_threshold,
            tabular_min_word_count,
            tabular_text_columns,
            output_folder_textbox,
            do_initial_clean_dup,
            in_excel_tabular_sheets,
            remove_duplicate_rows,
        ],
        outputs=[
            tabular_results_df,
            tabular_cleaned_file,
            tabular_file_to_clean,
            actual_time_taken_number,
            task_textbox,
        ],
        api_name="tabular_clean_duplicates",
        show_progress_on=[tabular_results_df],
    )

    tabular_results_df.select(
        fn=handle_tabular_row_selection,
        inputs=[tabular_results_df],
        outputs=[
            tabular_selected_row_index,
            tabular_text1_preview,
            tabular_text2_preview,
        ],
    )

    clean_duplicates_btn.click(
        fn=clean_tabular_duplicates,
        inputs=[
            tabular_file_to_clean,
            tabular_results_df,
            output_folder_textbox,
            in_excel_tabular_sheets,
        ],
        outputs=[tabular_cleaned_file],
    )

    ###
    # SETTINGS PAGE INPUT / OUTPUT
    ###
    # If a custom allow/deny/duplicate page list is uploaded
    in_allow_list.change(
        fn=custom_regex_load,
        inputs=[in_allow_list],
        outputs=[in_allow_list_text, in_allow_list_state],
    )
    in_deny_list.change(
        fn=custom_regex_load,
        inputs=[in_deny_list, in_deny_list_text_in],
        outputs=[in_deny_list_text, in_deny_list_state],
    )
    in_fully_redacted_list.change(
        fn=custom_regex_load,
        inputs=[in_fully_redacted_list, in_fully_redacted_text_in],
        outputs=[in_fully_redacted_list_text, in_fully_redacted_list_state],
    )

    # The following allows for more reliable updates of the data in the custom list dataframes
    in_allow_list_state.input(
        update_dataframe, inputs=[in_allow_list_state], outputs=[in_allow_list_state]
    )
    in_deny_list_state.input(
        update_dataframe, inputs=[in_deny_list_state], outputs=[in_deny_list_state]
    )
    in_fully_redacted_list_state.input(
        update_dataframe,
        inputs=[in_fully_redacted_list_state],
        outputs=[in_fully_redacted_list_state],
    )

    # Apply whole page redactions from the provided whole page redaction csv file upload/list of specific page numbers given by user
    apply_fully_redacted_list_btn.click(
        fn=apply_whole_page_redactions_from_list,
        inputs=[
            in_fully_redacted_list_state,
            doc_file_name_with_extension_textbox,
            review_file_df,
            duplicate_files_out,
            pdf_doc_state,
            page_sizes,
            all_image_annotations_state,
        ],
        outputs=[review_file_df, all_image_annotations_state],
    ).success(
        update_annotator_page_from_review_df,
        inputs=[
            review_file_df,
            images_pdf_state,
            page_sizes,
            all_image_annotations_state,
            annotator,
            selected_entity_dataframe_row,
            input_folder_textbox,
            doc_full_file_name_textbox,
        ],
        outputs=[
            annotator,
            all_image_annotations_state,
            annotate_current_page,
            page_sizes,
            review_file_df,
            annotate_previous_page,
        ],
        show_progress_on=[annotator],
    ).success(
        update_annotator_object_and_filter_df,
        inputs=[
            all_image_annotations_state,
            annotate_current_page,
            recogniser_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            text_entity_dropdown,
            recogniser_entity_dataframe_base,
            annotator_zoom_number,
            review_file_df,
            page_sizes,
            doc_full_file_name_textbox,
            input_folder_textbox,
        ],
        outputs=[
            annotator,
            annotate_current_page,
            annotate_current_page_bottom,
            annotate_previous_page,
            recogniser_entity_dropdown,
            recogniser_entity_dataframe,
            recogniser_entity_dataframe_base,
            text_entity_dropdown,
            page_entity_dropdown,
            page_entity_dropdown_redaction,
            page_sizes,
            all_image_annotations_state,
        ],
        show_progress_on=[annotator],
    )

    # Merge multiple review csv files together
    merge_multiple_review_files_btn.click(
        fn=merge_csv_files,
        inputs=multiple_review_files_in_out,
        outputs=multiple_review_files_in_out,
    )

    #
    all_output_files_btn.click(
        fn=load_all_output_files, inputs=output_folder_textbox, outputs=all_output_files
    )

    # Language selection dropdown
    chosen_language_full_name_drop.select(
        update_language_dropdown,
        inputs=[chosen_language_full_name_drop],
        outputs=[chosen_language_drop],
    )

    ###
    # APP LOAD AND LOGGING
    ###

    # Get connection details on app load

    if SHOW_WHOLE_DOCUMENT_TEXTRACT_CALL_OPTIONS == "True":
        app.load(
            get_connection_params,
            inputs=[
                output_folder_textbox,
                input_folder_textbox,
                session_output_folder_textbox,
                s3_whole_document_textract_input_subfolder,
                s3_whole_document_textract_output_subfolder,
                s3_whole_document_textract_logs_subfolder,
                local_whole_document_textract_logs_subfolder,
            ],
            outputs=[
                session_hash_state,
                output_folder_textbox,
                session_hash_textbox,
                input_folder_textbox,
                s3_whole_document_textract_input_subfolder,
                s3_whole_document_textract_output_subfolder,
                s3_whole_document_textract_logs_subfolder,
                local_whole_document_textract_logs_subfolder,
            ],
        ).success(
            load_in_textract_job_details,
            inputs=[
                load_s3_whole_document_textract_logs_bool,
                s3_whole_document_textract_logs_subfolder,
                local_whole_document_textract_logs_subfolder,
            ],
            outputs=[textract_job_detail_df],
        )
    else:
        app.load(
            get_connection_params,
            inputs=[
                output_folder_textbox,
                input_folder_textbox,
                session_output_folder_textbox,
                s3_whole_document_textract_input_subfolder,
                s3_whole_document_textract_output_subfolder,
                s3_whole_document_textract_logs_subfolder,
                local_whole_document_textract_logs_subfolder,
            ],
            outputs=[
                session_hash_state,
                output_folder_textbox,
                session_hash_textbox,
                input_folder_textbox,
                s3_whole_document_textract_input_subfolder,
                s3_whole_document_textract_output_subfolder,
                s3_whole_document_textract_logs_subfolder,
                local_whole_document_textract_logs_subfolder,
            ],
        )

    # If relevant environment variable is set, load in the default allow list file from S3 or locally. Even when setting S3 path, need to local path to give a download location
    if GET_DEFAULT_ALLOW_LIST == "True" and (ALLOW_LIST_PATH or S3_ALLOW_LIST_PATH):
        if (
            not os.path.exists(ALLOW_LIST_PATH)
            and S3_ALLOW_LIST_PATH
            and RUN_AWS_FUNCTIONS == "1"
        ):
            print("Downloading allow list from S3")
            app.load(
                download_file_from_s3,
                inputs=[
                    s3_default_bucket,
                    s3_default_allow_list_file,
                    default_allow_list_output_folder_location,
                ],
            ).success(
                load_in_default_allow_list,
                inputs=[default_allow_list_output_folder_location],
                outputs=[in_allow_list],
            )
            print("Successfully loaded allow list from S3")
        elif os.path.exists(ALLOW_LIST_PATH):
            print(
                "Loading allow list from default allow list output path location:",
                ALLOW_LIST_PATH,
            )
            app.load(
                load_in_default_allow_list,
                inputs=[default_allow_list_output_folder_location],
                outputs=[in_allow_list],
            )
        else:
            print("Could not load in default allow list")

    # If relevant environment variable is set, load in the default cost code file from S3 or locally
    if GET_COST_CODES == "True" and (COST_CODES_PATH or S3_COST_CODES_PATH):
        if (
            not os.path.exists(COST_CODES_PATH)
            and S3_COST_CODES_PATH
            and RUN_AWS_FUNCTIONS == "1"
        ):
            print("Downloading cost codes from S3")
            app.load(
                download_file_from_s3,
                inputs=[
                    s3_default_bucket,
                    s3_default_cost_codes_file,
                    default_cost_codes_output_folder_location,
                ],
            ).success(
                load_in_default_cost_codes,
                inputs=[
                    default_cost_codes_output_folder_location,
                    default_cost_code_textbox,
                ],
                outputs=[
                    cost_code_dataframe,
                    cost_code_dataframe_base,
                    cost_code_choice_drop,
                ],
            )
            print("Successfully loaded cost codes from S3")
        elif os.path.exists(COST_CODES_PATH):
            print(
                "Loading cost codes from default cost codes path location:",
                COST_CODES_PATH,
            )
            app.load(
                load_in_default_cost_codes,
                inputs=[
                    default_cost_codes_output_folder_location,
                    default_cost_code_textbox,
                ],
                outputs=[
                    cost_code_dataframe,
                    cost_code_dataframe_base,
                    cost_code_choice_drop,
                ],
            )
        else:
            print("Could not load in cost code data")

    ###
    # LOGGING
    ###

    ### ACCESS LOGS
    # Log usernames and times of access to file (to know who is using the app when running on AWS)
    access_callback = CSVLogger_custom(dataset_file_name=LOG_FILE_NAME)

    access_callback.setup([session_hash_textbox, host_name_textbox], ACCESS_LOGS_FOLDER)
    session_hash_textbox.change(
        lambda *args: access_callback.flag(
            list(args),
            save_to_csv=SAVE_LOGS_TO_CSV,
            save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
            dynamodb_table_name=ACCESS_LOG_DYNAMODB_TABLE_NAME,
            dynamodb_headers=DYNAMODB_ACCESS_LOG_HEADERS,
            replacement_headers=CSV_ACCESS_LOG_HEADERS,
        ),
        [session_hash_textbox, host_name_textbox],
        None,
        preprocess=False,
    ).success(
        fn=upload_log_file_to_s3,
        inputs=[access_logs_state, access_s3_logs_loc_state],
        outputs=[s3_logs_output_textbox],
    )

    ### FEEDBACK LOGS
    pdf_callback = CSVLogger_custom(dataset_file_name=FEEDBACK_LOG_FILE_NAME)
    data_callback = CSVLogger_custom(dataset_file_name=FEEDBACK_LOG_FILE_NAME)

    if DISPLAY_FILE_NAMES_IN_LOGS is True:
        # User submitted feedback for pdf redactions
        pdf_callback.setup(
            [
                pdf_feedback_radio,
                pdf_further_details_text,
                doc_file_name_no_extension_textbox,
            ],
            FEEDBACK_LOGS_FOLDER,
        )
        pdf_submit_feedback_btn.click(
            lambda *args: pdf_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=FEEDBACK_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_FEEDBACK_LOG_HEADERS,
                replacement_headers=CSV_FEEDBACK_LOG_HEADERS,
            ),
            [
                pdf_feedback_radio,
                pdf_further_details_text,
                doc_file_name_no_extension_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[feedback_logs_state, feedback_s3_logs_loc_state],
            outputs=[pdf_further_details_text],
        )

        # User submitted feedback for data redactions
        data_callback.setup(
            [
                data_feedback_radio,
                data_further_details_text,
                data_file_name_with_extension_textbox,
            ],
            FEEDBACK_LOGS_FOLDER,
        )
        data_submit_feedback_btn.click(
            lambda *args: data_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=FEEDBACK_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_FEEDBACK_LOG_HEADERS,
                replacement_headers=CSV_FEEDBACK_LOG_HEADERS,
            ),
            [
                data_feedback_radio,
                data_further_details_text,
                data_file_name_with_extension_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[feedback_logs_state, feedback_s3_logs_loc_state],
            outputs=[data_further_details_text],
        )
    else:
        # User submitted feedback for pdf redactions
        pdf_callback.setup(
            [
                pdf_feedback_radio,
                pdf_further_details_text,
                doc_file_name_no_extension_textbox,
            ],
            FEEDBACK_LOGS_FOLDER,
        )
        pdf_submit_feedback_btn.click(
            lambda *args: pdf_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=FEEDBACK_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_FEEDBACK_LOG_HEADERS,
                replacement_headers=CSV_FEEDBACK_LOG_HEADERS,
            ),
            [
                pdf_feedback_radio,
                pdf_further_details_text,
                placeholder_doc_file_name_no_extension_textbox_for_logs,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[feedback_logs_state, feedback_s3_logs_loc_state],
            outputs=[pdf_further_details_text],
        )

        # User submitted feedback for data redactions
        data_callback.setup(
            [
                data_feedback_radio,
                data_further_details_text,
                data_file_name_with_extension_textbox,
            ],
            FEEDBACK_LOGS_FOLDER,
        )
        data_submit_feedback_btn.click(
            lambda *args: data_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=FEEDBACK_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_FEEDBACK_LOG_HEADERS,
                replacement_headers=CSV_FEEDBACK_LOG_HEADERS,
            ),
            [
                data_feedback_radio,
                data_further_details_text,
                placeholder_data_file_name_no_extension_textbox_for_logs,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[feedback_logs_state, feedback_s3_logs_loc_state],
            outputs=[data_further_details_text],
        )

    ### USAGE LOGS
    # Log processing usage - time taken for redaction queries, and also logs for queries to Textract/Comprehend
    usage_callback = CSVLogger_custom(dataset_file_name=USAGE_LOG_FILE_NAME)

    if DISPLAY_FILE_NAMES_IN_LOGS is True:
        usage_callback.setup(
            [
                session_hash_textbox,
                doc_file_name_no_extension_textbox,
                data_file_name_with_extension_textbox,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            USAGE_LOGS_FOLDER,
        )

        latest_file_completed_num.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                doc_file_name_no_extension_textbox,
                data_file_name_with_extension_textbox,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
            api_name="usage_logs",
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        text_tabular_files_done.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                doc_file_name_no_extension_textbox,
                data_file_name_with_extension_textbox,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        successful_textract_api_call_number.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                doc_file_name_no_extension_textbox,
                data_file_name_with_extension_textbox,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        # Deduplication usage logging
        duplicate_files_out.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                blank_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        tabular_results_df.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                blank_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )
    else:
        usage_callback.setup(
            [
                session_hash_textbox,
                blank_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                total_pdf_page_count,
                actual_time_taken_number,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            USAGE_LOGS_FOLDER,
        )

        latest_file_completed_num.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                placeholder_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                actual_time_taken_number,
                total_pdf_page_count,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        text_tabular_files_done.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                blank_doc_file_name_no_extension_textbox_for_logs,
                placeholder_data_file_name_no_extension_textbox_for_logs,
                actual_time_taken_number,
                total_pdf_page_count,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        successful_textract_api_call_number.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                placeholder_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                actual_time_taken_number,
                total_pdf_page_count,
                textract_query_number,
                pii_identification_method_drop,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        # Deduplication usage logging (when file names not displayed)
        duplicate_files_out.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                placeholder_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                total_pdf_page_count,
                actual_time_taken_number,
                total_pdf_page_count,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

        tabular_results_df.change(
            lambda *args: usage_callback.flag(
                list(args),
                save_to_csv=SAVE_LOGS_TO_CSV,
                save_to_dynamodb=SAVE_LOGS_TO_DYNAMODB,
                dynamodb_table_name=USAGE_LOG_DYNAMODB_TABLE_NAME,
                dynamodb_headers=DYNAMODB_USAGE_LOG_HEADERS,
                replacement_headers=CSV_USAGE_LOG_HEADERS,
            ),
            [
                session_hash_textbox,
                placeholder_doc_file_name_no_extension_textbox_for_logs,
                blank_data_file_name_no_extension_textbox_for_logs,
                total_pdf_page_count,
                actual_time_taken_number,
                total_pdf_page_count,
                textract_query_number,
                pii_identification_method_drop_tabular,
                comprehend_query_number,
                cost_code_choice_drop,
                handwrite_signature_checkbox,
                host_name_textbox,
                text_extract_method_radio,
                is_a_textract_api_call,
                task_textbox,
            ],
            None,
            preprocess=False,
        ).success(
            fn=upload_log_file_to_s3,
            inputs=[usage_logs_state, usage_s3_logs_loc_state],
            outputs=[s3_logs_output_textbox],
        )

if __name__ == "__main__":
    if RUN_DIRECT_MODE == "0":

        if COGNITO_AUTH == "1":
            app.queue(
                max_size=int(MAX_QUEUE_SIZE),
                default_concurrency_limit=int(DEFAULT_CONCURRENCY_LIMIT),
            ).launch(
                show_error=True,
                inbrowser=True,
                auth=authenticate_user,
                max_file_size=MAX_FILE_SIZE,
                server_port=GRADIO_SERVER_PORT,
                root_path=ROOT_PATH,
            )
        else:
            app.queue(
                max_size=int(MAX_QUEUE_SIZE),
                default_concurrency_limit=int(DEFAULT_CONCURRENCY_LIMIT),
            ).launch(
                show_error=True,
                inbrowser=True,
                max_file_size=MAX_FILE_SIZE,
                server_port=GRADIO_SERVER_PORT,
                root_path=ROOT_PATH,
            )

    else:
        from cli_redact import main

        # Validate required direct mode configuration
        if not DIRECT_MODE_INPUT_FILE:
            print(
                "Error: DIRECT_MODE_INPUT_FILE environment variable must be set for direct mode."
            )
            print("Please set DIRECT_MODE_INPUT_FILE to the path of your input file.")
            exit(1)

        # Prepare direct mode arguments based on environment variables
        direct_mode_args = {
            "task": DIRECT_MODE_TASK,
            "input_file": DIRECT_MODE_INPUT_FILE,
            "output_dir": DIRECT_MODE_OUTPUT_DIR,
            "input_dir": INPUT_FOLDER,
            "language": DEFAULT_LANGUAGE,
            "allow_list": ALLOW_LIST_PATH,
            "pii_detector": LOCAL_PII_OPTION,
            "username": DIRECT_MODE_DEFAULT_USER,
            "save_to_user_folders": SESSION_OUTPUT_FOLDER,
            "aws_access_key": AWS_ACCESS_KEY,
            "aws_secret_key": AWS_SECRET_KEY,
            "aws_region": AWS_REGION,
            "s3_bucket": DOCUMENT_REDACTION_BUCKET,
            "do_initial_clean": DO_INITIAL_TABULAR_DATA_CLEAN,
            "save_logs_to_csv": SAVE_LOGS_TO_CSV,
            "save_logs_to_dynamodb": SAVE_LOGS_TO_DYNAMODB,
            "display_file_names_in_logs": DISPLAY_FILE_NAMES_IN_LOGS,
            "upload_logs_to_s3": RUN_AWS_FUNCTIONS == "1",
            "s3_logs_prefix": S3_USAGE_LOGS_FOLDER,
            "ocr_method": TESSERACT_TEXT_EXTRACT_OPTION,
            "page_min": DEFAULT_PAGE_MIN,
            "page_max": DEFAULT_PAGE_MAX,
            "images_dpi": IMAGES_DPI,
            "chosen_local_ocr_model": CHOSEN_LOCAL_OCR_MODEL,
            "preprocess_local_ocr_images": PREPROCESS_LOCAL_OCR_IMAGES,
            "compress_redacted_pdf": COMPRESS_REDACTED_PDF,
            "return_pdf_end_of_redaction": RETURN_PDF_END_OF_REDACTION,
            "allow_list_file": ALLOW_LIST_PATH,
            "deny_list_file": DENY_LIST_PATH,
            "redact_whole_page_file": WHOLE_PAGE_REDACTION_LIST_PATH,
            "handwrite_signature_extraction": DEFAULT_HANDWRITE_SIGNATURE_CHECKBOX,
            "extract_forms": False,
            "extract_tables": False,
            "extract_layout": False,
            "anon_strategy": DEFAULT_TABULAR_ANONYMISATION_STRATEGY,
            "excel_sheets": DEFAULT_EXCEL_SHEETS,
            "fuzzy_mistakes": DEFAULT_FUZZY_SPELLING_MISTAKES_NUM,
            "match_fuzzy_whole_phrase_bool": "True",  # Default value
            "duplicate_type": DIRECT_MODE_DUPLICATE_TYPE,
            "similarity_threshold": DEFAULT_DUPLICATE_DETECTION_THRESHOLD,
            "min_word_count": DEFAULT_MIN_WORD_COUNT,
            "min_consecutive_pages": DEFAULT_MIN_CONSECUTIVE_PAGES,
            "greedy_match": USE_GREEDY_DUPLICATE_DETECTION,
            "combine_pages": DEFAULT_COMBINE_PAGES,
            "search_query": DEFAULT_SEARCH_QUERY,
            "text_columns": DEFAULT_TEXT_COLUMNS,
            "remove_duplicate_rows": REMOVE_DUPLICATE_ROWS,
            # Textract specific arguments (with defaults)
            "textract_action": "",
            "job_id": "",
            "extract_signatures": False,
            "textract_bucket": TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_BUCKET,
            "textract_input_prefix": TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_INPUT_SUBFOLDER,
            "textract_output_prefix": TEXTRACT_WHOLE_DOCUMENT_ANALYSIS_OUTPUT_SUBFOLDER,
            "s3_textract_document_logs_subfolder": TEXTRACT_JOBS_S3_LOC,
            "local_textract_document_logs_subfolder": TEXTRACT_JOBS_LOCAL_LOC,
            "poll_interval": 30,
            "max_poll_attempts": 120,
            # General arguments that might be missing
            "local_redact_entities": CHOSEN_REDACT_ENTITIES,
            "aws_redact_entities": CHOSEN_COMPREHEND_ENTITIES,
            "cost_code": DEFAULT_COST_CODE,
        }

        print(f"Running in direct mode with task: {DIRECT_MODE_TASK}")
        print(f"Input file: {DIRECT_MODE_INPUT_FILE}")
        print(f"Output directory: {DIRECT_MODE_OUTPUT_DIR}")

        if DIRECT_MODE_TASK == "deduplicate":
            print(f"Duplicate type: {DIRECT_MODE_DUPLICATE_TYPE}")
            print(f"Similarity threshold: {DEFAULT_DUPLICATE_DETECTION_THRESHOLD}")
            print(f"Min word count: {DEFAULT_MIN_WORD_COUNT}")
            if DEFAULT_SEARCH_QUERY:
                print(f"Search query: {DEFAULT_SEARCH_QUERY}")
            if DEFAULT_TEXT_COLUMNS:
                print(f"Text columns: {DEFAULT_TEXT_COLUMNS}")
            print(f"Remove duplicate rows: {REMOVE_DUPLICATE_ROWS}")

        # Combine extraction options
        extraction_options = (
            list(direct_mode_args["handwrite_signature_extraction"])
            if direct_mode_args["handwrite_signature_extraction"]
            else []
        )
        if direct_mode_args["extract_forms"]:
            extraction_options.append("Extract forms")
        if direct_mode_args["extract_tables"]:
            extraction_options.append("Extract tables")
        if direct_mode_args["extract_layout"]:
            extraction_options.append("Extract layout")
        direct_mode_args["handwrite_signature_extraction"] = extraction_options

        # Run the CLI main function with direct mode arguments
        main(direct_mode_args=direct_mode_args)


        # Combine extraction options
        extraction_options = (
            list(direct_mode_args["handwrite_signature_extraction"])
            if direct_mode_args["handwrite_signature_extraction"]
            else []
        )
        if direct_mode_args["extract_forms"]:
            extraction_options.append("Extract forms")
        if direct_mode_args["extract_tables"]:
            extraction_options.append("Extract tables")
        if direct_mode_args["extract_layout"]:
            extraction_options.append("Extract layout")
        direct_mode_args["handwrite_signature_extraction"] = extraction_options

        # Run the CLI main function with direct mode arguments
        main(direct_mode_args=direct_mode_args)