tools/redaction_review.py

import os
import re
import gradio as gr
import pandas as pd
import numpy as np
from xml.etree.ElementTree import Element, SubElement, tostring, parse
from xml.dom import minidom
import uuid
from typing import List, Tuple
from gradio_image_annotation import image_annotator
from gradio_image_annotation.image_annotator import AnnotatedImageData
from pymupdf import Document, Rect
import pymupdf
from PIL import ImageDraw, Image

from tools.config import OUTPUT_FOLDER, CUSTOM_BOX_COLOUR, MAX_IMAGE_PIXELS, INPUT_FOLDER
from tools.file_conversion import is_pdf, convert_annotation_json_to_review_df, convert_review_df_to_annotation_json, process_single_page_for_image_conversion, multiply_coordinates_by_page_sizes, convert_annotation_data_to_dataframe, create_annotation_dicts_from_annotation_df, remove_duplicate_images_with_blank_boxes, fill_missing_ids, divide_coordinates_by_page_sizes
from tools.helper_functions import get_file_name_without_type,  detect_file_type
from tools.file_redaction import redact_page_with_pymupdf

if not MAX_IMAGE_PIXELS: Image.MAX_IMAGE_PIXELS = None

def decrease_page(number:int):
    '''
    Decrease page number for review redactions page.
    '''
    if number > 1:
        return number - 1, number - 1
    else:
        return 1, 1

def increase_page(number:int, page_image_annotator_object:AnnotatedImageData):
    '''
    Increase page number for review redactions page.
    '''

    if not page_image_annotator_object:
        return 1, 1

    max_pages = len(page_image_annotator_object)

    if number < max_pages:
        return number + 1, number + 1
    else:
        return max_pages, max_pages

def update_zoom(current_zoom_level:int, annotate_current_page:int, decrease:bool=True):
    if decrease == False:
        if current_zoom_level >= 70:
            current_zoom_level -= 10
    else:    
        if current_zoom_level < 110:
            current_zoom_level += 10
        
    return current_zoom_level, annotate_current_page

def update_dropdown_list_based_on_dataframe(df:pd.DataFrame, column:str) -> List["str"]:
    '''
    Gather unique elements from a string pandas Series, then append 'ALL' to the start and return the list.
    '''
    if isinstance(df, pd.DataFrame):
        # Check if the Series is empty or all NaN
        if column not in df.columns or df[column].empty or df[column].isna().all():
            return ["ALL"]
        elif column != "page":
            entities = df[column].astype(str).unique().tolist()        
            entities_for_drop = sorted(entities)
            entities_for_drop.insert(0, "ALL")
        else:
            # Ensure the column can be converted to int - assumes it is the page column
            try:
                entities = df[column].astype(int).unique()
                entities_for_drop = sorted(entities)
                entities_for_drop = [str(e) for e in entities_for_drop]  # Convert back to string
                entities_for_drop.insert(0, "ALL")
            except ValueError:
                return ["ALL"]  # Handle case where conversion fails

        return entities_for_drop  # Ensure to return the list
    else:
        return ["ALL"]

def get_filtered_recogniser_dataframe_and_dropdowns(page_image_annotator_object:AnnotatedImageData,
                                 recogniser_dataframe_base:pd.DataFrame,
                                 recogniser_dropdown_value:str,
                                 text_dropdown_value:str,
                                 page_dropdown_value:str,
                                 review_df:pd.DataFrame=[],
                                 page_sizes:List[str]=[]):
    '''
    Create a filtered recogniser dataframe and associated dropdowns based on current information in the image annotator and review data frame.
    '''

    recogniser_entities_list = ["Redaction"]
    recogniser_dataframe_out = recogniser_dataframe_base
    recogniser_dataframe_out_gr = gr.Dataframe()
    review_dataframe = review_df

    try:
        #print("converting annotation json in get_filtered_recogniser...")

        review_dataframe = convert_annotation_json_to_review_df(page_image_annotator_object, review_df, page_sizes)

        recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(review_dataframe, "label")
        recogniser_entities_drop = gr.Dropdown(value=recogniser_dropdown_value, choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)

        # This is the choice list for entities when creating a new redaction box
        recogniser_entities_list = [entity for entity in recogniser_entities_for_drop.copy() if entity != 'Redaction' and entity != 'ALL']  # Remove any existing 'Redaction'
        recogniser_entities_list.insert(0, 'Redaction')  # Add 'Redaction' to the start of the list        

        text_entities_for_drop = update_dropdown_list_based_on_dataframe(review_dataframe, "text")
        text_entities_drop = gr.Dropdown(value=text_dropdown_value, choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

        page_entities_for_drop = update_dropdown_list_based_on_dataframe(review_dataframe, "page")
        page_entities_drop = gr.Dropdown(value=page_dropdown_value, choices=page_entities_for_drop, allow_custom_value=True, interactive=True)

        recogniser_dataframe_out_gr = gr.Dataframe(review_dataframe[["page", "label", "text", "id"]], show_search="filter", col_count=(4, "fixed"), type="pandas", headers=["page", "label", "text", "id"], show_fullscreen_button=True, wrap=True, max_height=400, static_columns=[0,1,2,3])

        recogniser_dataframe_out = review_dataframe[["page", "label", "text", "id"]]

    except Exception as e:
        print("Could not extract recogniser information:", e)
        recogniser_dataframe_out = recogniser_dataframe_base[["page", "label", "text", "id"]]

        label_choices = review_dataframe["label"].astype(str).unique().tolist()
        text_choices = review_dataframe["text"].astype(str).unique().tolist()
        page_choices = review_dataframe["page"].astype(str).unique().tolist()

        recogniser_entities_drop = gr.Dropdown(value=recogniser_dropdown_value, choices=label_choices, allow_custom_value=True, interactive=True)
        recogniser_entities_list = ["Redaction"]
        text_entities_drop = gr.Dropdown(value=text_dropdown_value, choices=text_choices, allow_custom_value=True, interactive=True)
        page_entities_drop = gr.Dropdown(value=page_dropdown_value, choices=page_choices, allow_custom_value=True, interactive=True)

    return recogniser_dataframe_out_gr, recogniser_dataframe_out, recogniser_entities_drop, recogniser_entities_list, text_entities_drop, page_entities_drop

def update_recogniser_dataframes(page_image_annotator_object:AnnotatedImageData, recogniser_dataframe_base:pd.DataFrame, recogniser_entities_dropdown_value:str="ALL", text_dropdown_value:str="ALL", page_dropdown_value:str="ALL", review_df:pd.DataFrame=[], page_sizes:list[str]=[]):
    '''
    Update recogniser dataframe information that appears alongside the pdf pages on the review screen.
    '''
    recogniser_entities_list = ["Redaction"]
    recogniser_dataframe_out = pd.DataFrame()
    recogniser_dataframe_out_gr = gr.Dataframe()

    # If base recogniser dataframe is empy, need to create it.
    if recogniser_dataframe_base.empty:
        recogniser_dataframe_out_gr, recogniser_dataframe_out, recogniser_entities_drop, recogniser_entities_list, text_entities_drop, page_entities_drop = get_filtered_recogniser_dataframe_and_dropdowns(page_image_annotator_object, recogniser_dataframe_base, recogniser_entities_dropdown_value, text_dropdown_value, page_dropdown_value, review_df, page_sizes)    
    elif recogniser_dataframe_base.iloc[0,0] == "":
        recogniser_dataframe_out_gr, recogniser_dataframe_out, recogniser_entities_dropdown_value, recogniser_entities_list, text_entities_drop, page_entities_drop = get_filtered_recogniser_dataframe_and_dropdowns(page_image_annotator_object, recogniser_dataframe_base, recogniser_entities_dropdown_value, text_dropdown_value, page_dropdown_value, review_df, page_sizes)
    else:
        recogniser_dataframe_out_gr, recogniser_dataframe_out, recogniser_entities_dropdown, recogniser_entities_list, text_dropdown, page_dropdown = get_filtered_recogniser_dataframe_and_dropdowns(page_image_annotator_object, recogniser_dataframe_base, recogniser_entities_dropdown_value, text_dropdown_value, page_dropdown_value, review_df, page_sizes)

        review_dataframe, text_entities_drop, page_entities_drop = update_entities_df_recogniser_entities(recogniser_entities_dropdown_value, recogniser_dataframe_out, page_dropdown_value, text_dropdown_value)

        recogniser_dataframe_out_gr = gr.Dataframe(review_dataframe[["page", "label", "text", "id"]], show_search="filter", col_count=(4, "fixed"), type="pandas", headers=["page", "label", "text", "id"], show_fullscreen_button=True, wrap=True, max_height=400, static_columns=[0,1,2,3])
        
        recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(recogniser_dataframe_out, "label")
        recogniser_entities_drop = gr.Dropdown(value=recogniser_entities_dropdown_value, choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)

        recogniser_entities_list_base = recogniser_dataframe_out["label"].astype(str).unique().tolist()

        # Recogniser entities list is the list of choices that appear when you make a new redaction box
        recogniser_entities_list = [entity for entity in recogniser_entities_list_base if entity != 'Redaction']
        recogniser_entities_list.insert(0, 'Redaction')

    return recogniser_entities_list, recogniser_dataframe_out_gr, recogniser_dataframe_out, recogniser_entities_drop, text_entities_drop, page_entities_drop

def undo_last_removal(backup_review_state:pd.DataFrame, backup_image_annotations_state:list[dict], backup_recogniser_entity_dataframe_base:pd.DataFrame):
    return backup_review_state, backup_image_annotations_state, backup_recogniser_entity_dataframe_base

def update_annotator_page_from_review_df(
    review_df: pd.DataFrame,
    image_file_paths:List[str], # Note: This input doesn't seem used in the original logic flow after the first line was removed
    page_sizes:List[dict],
    current_image_annotations_state:List[str], # This should ideally be List[dict] based on its usage
    current_page_annotator:object, # Should be dict or a custom annotation object for one page
    selected_recogniser_entity_df_row:pd.DataFrame,
    input_folder:str,
    doc_full_file_name_textbox:str
) -> Tuple[object, List[dict], int, List[dict], pd.DataFrame, int]: # Correcting return types based on usage
    '''
    Update the visible annotation object and related objects with the latest review file information,
    optimizing by processing only the current page's data.
    '''
    # Assume current_image_annotations_state is List[dict] and current_page_annotator is dict
    out_image_annotations_state: List[dict] = list(current_image_annotations_state) # Make a copy to avoid modifying input in place
    out_current_page_annotator: dict = current_page_annotator

    # Get the target page number from the selected row
    # Safely access the page number, handling potential errors or empty DataFrame
    gradio_annotator_current_page_number: int = 0
    annotate_previous_page: int = 0 # Renaming for clarity if needed, matches original output
    if not selected_recogniser_entity_df_row.empty and 'page' in selected_recogniser_entity_df_row.columns:
        try:
            # Use .iloc[0] and .item() for robust scalar extraction
            gradio_annotator_current_page_number = int(selected_recogniser_entity_df_row['page'].iloc[0])
            annotate_previous_page = gradio_annotator_current_page_number # Store original page number
        except (IndexError, ValueError, TypeError):
            print("Warning: Could not extract valid page number from selected_recogniser_entity_df_row. Defaulting to page 0 (or 1).")
            gradio_annotator_current_page_number = 1 # Or 0 depending on 1-based vs 0-based indexing elsewhere

    # Ensure page number is valid and 1-based for external display/logic
    if gradio_annotator_current_page_number <= 0:
        gradio_annotator_current_page_number = 1

    page_max_reported = len(out_image_annotations_state)
    if gradio_annotator_current_page_number > page_max_reported:
        gradio_annotator_current_page_number = page_max_reported # Cap at max pages

    page_num_reported_zero_indexed = gradio_annotator_current_page_number - 1

    # Process page sizes DataFrame early, as it's needed for image path handling and potentially coordinate multiplication
    page_sizes_df = pd.DataFrame(page_sizes)
    if not page_sizes_df.empty:
        # Safely convert page column to numeric and then int
        page_sizes_df["page"] = pd.to_numeric(page_sizes_df["page"], errors="coerce")
        page_sizes_df.dropna(subset=["page"], inplace=True)
        if not page_sizes_df.empty:
            page_sizes_df["page"] = page_sizes_df["page"].astype(int)
        else:
            print("Warning: Page sizes DataFrame became empty after processing.")

    # --- OPTIMIZATION: Process only the current page's data from review_df ---
    if not review_df.empty:
        # Filter review_df for the current page
        # Ensure 'page' column in review_df is comparable to page_num_reported
        if 'page' in review_df.columns:
             review_df['page'] = pd.to_numeric(review_df['page'], errors='coerce').fillna(-1).astype(int)

             current_image_path = out_image_annotations_state[page_num_reported_zero_indexed]['image']

             replaced_image_path, page_sizes_df = replace_placeholder_image_with_real_image(doc_full_file_name_textbox, current_image_path, page_sizes_df, gradio_annotator_current_page_number, input_folder)

             # page_sizes_df has been changed - save back to page_sizes_object
             page_sizes = page_sizes_df.to_dict(orient='records')
             review_df.loc[review_df["page"]==gradio_annotator_current_page_number, 'image'] = replaced_image_path
             images_list = list(page_sizes_df["image_path"])
             images_list[page_num_reported_zero_indexed] = replaced_image_path
             out_image_annotations_state[page_num_reported_zero_indexed]['image'] = replaced_image_path

             current_page_review_df = review_df[review_df['page'] == gradio_annotator_current_page_number].copy()          
             current_page_review_df = multiply_coordinates_by_page_sizes(current_page_review_df, page_sizes_df)

        else:
            print(f"Warning: 'page' column not found in review_df. Cannot filter for page {gradio_annotator_current_page_number}. Skipping update from review_df.")
            current_page_review_df = pd.DataFrame() # Empty dataframe if filter fails

        if not current_page_review_df.empty:
            # Convert the current page's review data to annotation list format for *this page*

            current_page_annotations_list = []
            # Define expected annotation dict keys, including 'image', 'page', coords, 'label', 'text', 'color' etc.
            # Assuming review_df has compatible columns
            expected_annotation_keys = ['label', 'color', 'xmin', 'ymin', 'xmax', 'ymax', 'text', 'id'] # Add/remove as needed

            # Ensure necessary columns exist in current_page_review_df before converting rows
            for key in expected_annotation_keys:
                 if key not in current_page_review_df.columns:
                      # Add missing column with default value
                      # Use np.nan for numeric, '' for string/object
                      default_value = np.nan if key in ['xmin', 'ymin', 'xmax', 'ymax'] else ''
                      current_page_review_df[key] = default_value

            # Convert filtered DataFrame rows to list of dicts
            # Using .to_dict(orient='records') is efficient for this
            current_page_annotations_list_raw = current_page_review_df[expected_annotation_keys].to_dict(orient='records')

            current_page_annotations_list = current_page_annotations_list_raw

            # Update the annotations state for the current page
            # Each entry in out_image_annotations_state seems to be a dict containing keys like 'image', 'page', 'annotations' (List[dict])
            # Need to update the 'annotations' list for the specific page.
            # Find the entry for the current page in the state
            page_state_entry_found = False
            for i, page_state_entry in enumerate(out_image_annotations_state):
                # Assuming page_state_entry has a 'page' key (1-based)

                match = re.search(r"(\d+)\.png$", page_state_entry['image'])
                if match: page_no = int(match.group(1))
                else: page_no = -1

                if 'image' in page_state_entry and page_no == page_num_reported_zero_indexed:
                    # Replace the annotations list for this page with the new list from review_df
                    out_image_annotations_state[i]['boxes'] = current_page_annotations_list

                    # Update the image path as well, based on review_df if available, or keep existing
                    # Assuming review_df has an 'image' column for this page
                    if 'image' in current_page_review_df.columns and not current_page_review_df.empty:
                         # Use the image path from the first row of the filtered review_df
                         out_image_annotations_state[i]['image'] = current_page_review_df['image'].iloc[0]
                    page_state_entry_found = True
                    break

            if not page_state_entry_found:
                 # This scenario might happen if the current_image_annotations_state didn't initially contain
                 # an entry for this page number. Depending on the application logic, you might need to
                 # add a new entry here, but based on the original code's structure, it seems
                 # out_image_annotations_state is pre-populated for all pages.
                 print(f"Warning: Entry for page {gradio_annotator_current_page_number} not found in current_image_annotations_state. Cannot update page annotations.")


    # --- Image Path and Page Size Handling (already seems focused on current page, keep similar logic) ---
    # Get the image path for the current page from the updated state
    # Ensure the entry exists before accessing
    current_image_path = None
    if len(out_image_annotations_state) > page_num_reported_zero_indexed and 'image' in out_image_annotations_state[page_num_reported_zero_indexed]:
         current_image_path = out_image_annotations_state[page_num_reported_zero_indexed]['image']
    else:
         print(f"Warning: Could not get image path from state for page index {page_num_reported_zero_indexed}.")


    # Replace placeholder image with real image path if needed
    if current_image_path and not page_sizes_df.empty:
        try:
            replaced_image_path, page_sizes_df = replace_placeholder_image_with_real_image(
                doc_full_file_name_textbox, current_image_path, page_sizes_df,
                gradio_annotator_current_page_number, input_folder # Use 1-based page number
            )

            # Update state and review_df with the potentially replaced image path
            if len(out_image_annotations_state) > page_num_reported_zero_indexed:
                 out_image_annotations_state[page_num_reported_zero_indexed]['image'] = replaced_image_path

            if 'page' in review_df.columns and 'image' in review_df.columns:
                 review_df.loc[review_df["page"]==gradio_annotator_current_page_number, 'image'] = replaced_image_path

        except Exception as e:
             print(f"Error during image path replacement for page {gradio_annotator_current_page_number}: {e}")


    # Save back page_sizes_df to page_sizes list format
    if not page_sizes_df.empty:
        page_sizes = page_sizes_df.to_dict(orient='records')
    else:
        page_sizes = [] # Ensure page_sizes is a list if df is empty

    # --- Re-evaluate Coordinate Multiplication and Duplicate Removal ---
    # The original code multiplied coordinates for the *entire* document and removed duplicates
    # across the *entire* document *after* converting the full review_df to state.
    # With the optimized approach, we updated only one page's annotations in the state.

    # Let's assume remove_duplicate_images_with_blank_boxes expects the raw list of dicts state format:
    try:
         out_image_annotations_state = remove_duplicate_images_with_blank_boxes(out_image_annotations_state)
    except Exception as e:
         print(f"Error during duplicate removal: {e}. Proceeding without duplicate removal.")


    # Select the current page's annotation object from the (potentially updated) state
    if len(out_image_annotations_state) > page_num_reported_zero_indexed:
         out_current_page_annotator = out_image_annotations_state[page_num_reported_zero_indexed]
    else:
         print(f"Warning: Cannot select current page annotator object for index {page_num_reported_zero_indexed}.")
         out_current_page_annotator = {} # Or None, depending on expected output type


    # The original code returns gradio_annotator_current_page_number as the 3rd value,
    # which was potentially updated by bounding checks. Keep this.
    final_page_number_returned = gradio_annotator_current_page_number

    return (out_current_page_annotator,
            out_image_annotations_state,
            final_page_number_returned,
            page_sizes,
            review_df, # review_df might have its 'page' column type changed, keep it as is or revert if necessary
            annotate_previous_page) # The original page number from selected_recogniser_entity_df_row

def exclude_selected_items_from_redaction(review_df: pd.DataFrame,
                                          selected_rows_df: pd.DataFrame,
                                          image_file_paths:List[str],
                                          page_sizes:List[dict],
                                          image_annotations_state:dict,
                                          recogniser_entity_dataframe_base:pd.DataFrame):
    ''' 
    Remove selected items from the review dataframe from the annotation object and review dataframe.
    '''

    backup_review_state = review_df
    backup_image_annotations_state = image_annotations_state
    backup_recogniser_entity_dataframe_base = recogniser_entity_dataframe_base

    if not selected_rows_df.empty and not review_df.empty:
        use_id = (
            "id" in selected_rows_df.columns 
            and "id" in review_df.columns 
            and not selected_rows_df["id"].isnull().all() 
            and not review_df["id"].isnull().all()
        )

        selected_merge_cols = ["id"] if use_id else ["label", "page", "text"]

        # Subset and drop duplicates from selected_rows_df
        selected_subset = selected_rows_df[selected_merge_cols].drop_duplicates(subset=selected_merge_cols)

        # Perform anti-join using merge with indicator
        merged_df = review_df.merge(selected_subset, on=selected_merge_cols, how='left', indicator=True)
        out_review_df = merged_df[merged_df['_merge'] == 'left_only'].drop(columns=['_merge'])

        out_image_annotations_state = convert_review_df_to_annotation_json(out_review_df, image_file_paths, page_sizes)

        out_recogniser_entity_dataframe_base = out_review_df[["page", "label", "text", "id"]]
    
    # Either there is nothing left in the selection dataframe, or the review dataframe
    else:
        out_review_df = review_df
        out_recogniser_entity_dataframe_base = recogniser_entity_dataframe_base
        out_image_annotations_state = image_annotations_state

    return out_review_df, out_image_annotations_state, out_recogniser_entity_dataframe_base, backup_review_state, backup_image_annotations_state, backup_recogniser_entity_dataframe_base

def replace_annotator_object_img_np_array_with_page_sizes_image_path(
        all_image_annotations:List[dict],
        page_image_annotator_object:AnnotatedImageData,
        page_sizes:List[dict],
        page:int):

        '''
        Check if the image value in an AnnotatedImageData dict is a placeholder or np.array. If either of these, replace the value with the file path of the image that is hopefully already loaded into the app related to this page.
        '''

        page_zero_index = page - 1
        
        if isinstance(all_image_annotations[page_zero_index]["image"], np.ndarray) or "placeholder_image" in all_image_annotations[page_zero_index]["image"] or isinstance(page_image_annotator_object['image'], np.ndarray):
            page_sizes_df = pd.DataFrame(page_sizes)
            page_sizes_df[["page"]] = page_sizes_df[["page"]].apply(pd.to_numeric, errors="coerce")

            # Check for matching pages
            matching_paths = page_sizes_df.loc[page_sizes_df['page'] == page, "image_path"].unique()

            if matching_paths.size > 0:
                image_path = matching_paths[0]
                page_image_annotator_object['image'] = image_path
                all_image_annotations[page_zero_index]["image"] = image_path
            else:
                print(f"No image path found for page {page}.")

        return page_image_annotator_object, all_image_annotations

def replace_placeholder_image_with_real_image(doc_full_file_name_textbox:str, current_image_path:str, page_sizes_df:pd.DataFrame, page_num_reported:int, input_folder:str):
        ''' If image path is still not valid, load in a new image an overwrite it. Then replace all items in the image annotation object for all pages based on the updated information.'''
        page_num_reported_zero_indexed = page_num_reported - 1

        if not os.path.exists(current_image_path):        

            page_num, replaced_image_path, width, height = process_single_page_for_image_conversion(doc_full_file_name_textbox, page_num_reported_zero_indexed, input_folder=input_folder)

            # Overwrite page_sizes values 
            page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_width"] = width
            page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_height"] = height
            page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_path"] = replaced_image_path
        
        else:
            if not page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_width"].isnull().all():
                width = page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_width"].max()
                height = page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_height"].max()      
            else:
                image = Image.open(current_image_path)
                width = image.width
                height = image.height

                page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_width"] = width
                page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_height"] = height

            page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_path"] = current_image_path

            replaced_image_path = current_image_path
        
        return replaced_image_path, page_sizes_df

def update_annotator_object_and_filter_df(
    all_image_annotations:List[AnnotatedImageData],
    gradio_annotator_current_page_number:int,
    recogniser_entities_dropdown_value:str="ALL",
    page_dropdown_value:str="ALL",
    text_dropdown_value:str="ALL",
    recogniser_dataframe_base:gr.Dataframe=None, # Simplified default
    zoom:int=100,
    review_df:pd.DataFrame=None, # Use None for default empty DataFrame
    page_sizes:List[dict]=[],
    doc_full_file_name_textbox:str='',
    input_folder:str=INPUT_FOLDER
) -> Tuple[image_annotator, gr.Number, gr.Number, int, str, gr.Dataframe, pd.DataFrame, List[str], List[str], List[dict], List[AnnotatedImageData]]:
    '''
    Update a gradio_image_annotation object with new annotation data for the current page
    and update filter dataframes, optimizing by processing only the current page's data for display.
    '''
    zoom_str = str(zoom) + '%'

    # Handle default empty review_df and recogniser_dataframe_base
    if review_df is None or not isinstance(review_df, pd.DataFrame):
         review_df = pd.DataFrame(columns=["image", "page", "label", "color", "xmin", "ymin", "xmax", "ymax", "text", "id"])
    if recogniser_dataframe_base is None: # Create a simple default if None
         recogniser_dataframe_base = gr.Dataframe(pd.DataFrame(data={"page":[], "label":[], "text":[], "id":[]}))


    # Handle empty all_image_annotations state early
    if not all_image_annotations:
        print("No all_image_annotation object found")
        # Return blank/default outputs
        blank_annotator = gr.ImageAnnotator(
            value = None, boxes_alpha=0.1, box_thickness=1, label_list=[], label_colors=[],
            show_label=False, height=zoom_str, width=zoom_str, box_min_size=1,
            box_selected_thickness=2, handle_size=4, sources=None,
            show_clear_button=False, show_share_button=False, show_remove_button=False,
            handles_cursor=True, interactive=True, use_default_label=True
        )
        blank_df_out_gr = gr.Dataframe(pd.DataFrame(columns=["page", "label", "text", "id"]))
        blank_df_modified = pd.DataFrame(columns=["page", "label", "text", "id"])

        return (blank_annotator, gr.Number(value=1), gr.Number(value=1), 1,
                recogniser_entities_dropdown_value, blank_df_out_gr, blank_df_modified,
                [], [], [], []) # Return empty lists/defaults for other outputs

    # Validate and bound the current page number (1-based logic)
    page_num_reported = max(1, gradio_annotator_current_page_number) # Minimum page is 1
    page_max_reported = len(all_image_annotations)
    if page_num_reported > page_max_reported:
        page_num_reported = page_max_reported

    page_num_reported_zero_indexed = page_num_reported - 1
    annotate_previous_page = page_num_reported # Store the determined page number

    # --- Process page sizes DataFrame ---
    page_sizes_df = pd.DataFrame(page_sizes)
    if not page_sizes_df.empty:
        page_sizes_df["page"] = pd.to_numeric(page_sizes_df["page"], errors="coerce")
        page_sizes_df.dropna(subset=["page"], inplace=True)
        if not page_sizes_df.empty:
            page_sizes_df["page"] = page_sizes_df["page"].astype(int)
        else:
            print("Warning: Page sizes DataFrame became empty after processing.")

    # --- Handle Image Path Replacement for the Current Page ---
    # This modifies the specific page entry within all_image_annotations list
    # Assuming replace_annotator_object_img_np_array_with_page_sizes_image_path
    # correctly updates the image path within the list element.
    if len(all_image_annotations) > page_num_reported_zero_indexed:
        # Make a shallow copy of the list and deep copy the specific page dict before modification
        # to avoid modifying the input list unexpectedly if it's used elsewhere.
        # However, the original code modified the list in place, so we'll stick to that
        # pattern but acknowledge it.
        page_object_to_update = all_image_annotations[page_num_reported_zero_indexed]

        # Use the helper function to replace the image path within the page object
        # Note: This helper returns the potentially modified page_object and the full state.
        # The full state return seems redundant if only page_object_to_update is modified.
        # Let's call it and assume it correctly updates the item in the list.
        updated_page_object, all_image_annotations_after_img_replace = replace_annotator_object_img_np_array_with_page_sizes_image_path(
             all_image_annotations, page_object_to_update, page_sizes, page_num_reported)

        # The original code immediately re-assigns all_image_annotations.
        # We'll rely on the function modifying the list element in place or returning the updated list.
        # Assuming it returns the updated list for robustness:
        all_image_annotations = all_image_annotations_after_img_replace


        # Now handle the actual image file path replacement using replace_placeholder_image_with_real_image
        current_image_path = updated_page_object.get('image') # Get potentially updated image path

        if current_image_path and not page_sizes_df.empty:
            try:
                replaced_image_path, page_sizes_df = replace_placeholder_image_with_real_image(
                    doc_full_file_name_textbox, current_image_path, page_sizes_df,
                    page_num_reported, input_folder=input_folder # Use 1-based page num
                )

                # Update the image path in the state and review_df for the current page
                # Find the correct entry in all_image_annotations list again by index
                if len(all_image_annotations) > page_num_reported_zero_indexed:
                     all_image_annotations[page_num_reported_zero_indexed]['image'] = replaced_image_path

                # Update review_df's image path for this page
                if 'page' in review_df.columns and 'image' in review_df.columns:
                     # Ensure review_df page column is numeric for filtering
                     review_df['page'] = pd.to_numeric(review_df['page'], errors='coerce').fillna(-1).astype(int)
                     review_df.loc[review_df["page"]==page_num_reported, 'image'] = replaced_image_path


            except Exception as e:
                 print(f"Error during image path replacement for page {page_num_reported}: {e}")
    else:
         print(f"Warning: Page index {page_num_reported_zero_indexed} out of bounds for all_image_annotations list.")


    # Save back page_sizes_df to page_sizes list format
    if not page_sizes_df.empty:
        page_sizes = page_sizes_df.to_dict(orient='records')
    else:
        page_sizes = [] # Ensure page_sizes is a list if df is empty

    # --- OPTIMIZATION: Prepare data *only* for the current page for display ---
    current_page_image_annotator_object = None
    if len(all_image_annotations) > page_num_reported_zero_indexed:
        page_data_for_display = all_image_annotations[page_num_reported_zero_indexed]

        # Convert current page annotations list to DataFrame for coordinate multiplication IF needed
        # Assuming coordinate multiplication IS needed for display if state stores relative coords
        current_page_annotations_df = convert_annotation_data_to_dataframe([page_data_for_display])


        if not current_page_annotations_df.empty and not page_sizes_df.empty:
             # Multiply coordinates *only* for this page's DataFrame
             try:
                 # Need the specific page's size for multiplication
                 page_size_row = page_sizes_df[page_sizes_df['page'] == page_num_reported]
                 if not page_size_row.empty:
                      current_page_annotations_df = multiply_coordinates_by_page_sizes(
                          current_page_annotations_df, page_size_row, # Pass only the row for the current page
                          xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"
                      )
             
             except Exception as e:
                  print(f"Warning: Error during coordinate multiplication for page {page_num_reported}: {e}. Using original coordinates.")
                  # If error, proceed with original coordinates or handle as needed

        if "color" not in current_page_annotations_df.columns:
            current_page_annotations_df['color'] = '(0, 0, 0)'

        # Convert the processed DataFrame back to the list of dicts format for the annotator
        processed_current_page_annotations_list = current_page_annotations_df[["xmin", "xmax", "ymin", "ymax", "label", "color", "text", "id"]].to_dict(orient='records')

        # Construct the final object expected by the Gradio ImageAnnotator value parameter
        current_page_image_annotator_object: AnnotatedImageData = {
            'image': page_data_for_display.get('image'), # Use the (potentially updated) image path
            'boxes': processed_current_page_annotations_list
        }

    # --- Update Dropdowns and Review DataFrame ---
    # This external function still operates on potentially large DataFrames.
    # It receives all_image_annotations and a copy of review_df.
    try:
        recogniser_entities_list, recogniser_dataframe_out_gr, recogniser_dataframe_modified, recogniser_entities_dropdown_value, text_entities_drop, page_entities_drop = update_recogniser_dataframes(
             all_image_annotations, # Pass the updated full state
             recogniser_dataframe_base,
             recogniser_entities_dropdown_value,
             text_dropdown_value,
             page_dropdown_value,
             review_df.copy(), # Keep the copy as per original function call
             page_sizes # Pass updated page sizes
        )
        # Generate default black colors for labels if needed by image_annotator
        recogniser_colour_list = [(0, 0, 0) for _ in range(len(recogniser_entities_list))]

    except Exception as e:
        print(f"Error calling update_recogniser_dataframes: {e}. Returning empty/default filter data.")
        recogniser_entities_list = []
        recogniser_colour_list = []
        recogniser_dataframe_out_gr = gr.Dataframe(pd.DataFrame(columns=["page", "label", "text", "id"]))
        recogniser_dataframe_modified = pd.DataFrame(columns=["page", "label", "text", "id"])
        text_entities_drop = []
        page_entities_drop = []


    # --- Final Output Components ---
    page_number_reported_gradio_comp = gr.Number(label = "Current page", value=page_num_reported, precision=0)

    

    ### Present image_annotator outputs
    # Handle the case where current_page_image_annotator_object couldn't be prepared
    if current_page_image_annotator_object is None:
        # This should ideally be covered by the initial empty check for all_image_annotations,
        # but as a safeguard:
        print("Warning: Could not prepare annotator object for the current page.")
        out_image_annotator = image_annotator(value=None, interactive=False) # Present blank/non-interactive
    else:
        out_image_annotator = image_annotator(
            value = current_page_image_annotator_object,
            boxes_alpha=0.1,
            box_thickness=1,
            label_list=recogniser_entities_list, # Use labels from update_recogniser_dataframes
            label_colors=recogniser_colour_list,
            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=True # Keep interactive if data is present
        )

    # The original code returned page_number_reported_gradio twice;
    # returning the Gradio component and the plain integer value.
    # Let's match the output signature.
    return (out_image_annotator,
            page_number_reported_gradio_comp,
            page_number_reported_gradio_comp, # Redundant, but matches original return signature
            page_num_reported, # Plain integer value
            recogniser_entities_dropdown_value,
            recogniser_dataframe_out_gr,
            recogniser_dataframe_modified,
            text_entities_drop, # List of text entities for dropdown
            page_entities_drop, # List of page numbers for dropdown
            page_sizes, # Updated page_sizes list
            all_image_annotations) # Return the updated full state

def update_all_page_annotation_object_based_on_previous_page(
                                    page_image_annotator_object:AnnotatedImageData,
                                    current_page:int,
                                    previous_page:int,
                                    all_image_annotations:List[AnnotatedImageData],
                                    page_sizes:List[dict]=[],
                                    clear_all:bool=False
                                    ):
    '''
    Overwrite image annotations on the page we are moving from with modifications.
    '''

    previous_page_zero_index = previous_page -1
 
    if not current_page: current_page = 1
    
    # This replaces the numpy array image object with the image file path
    page_image_annotator_object, all_image_annotations = replace_annotator_object_img_np_array_with_page_sizes_image_path(all_image_annotations, page_image_annotator_object, page_sizes, previous_page)

    if clear_all == False: all_image_annotations[previous_page_zero_index] = page_image_annotator_object
    else: all_image_annotations[previous_page_zero_index]["boxes"] = []

    return all_image_annotations, current_page, current_page

def apply_redactions_to_review_df_and_files(page_image_annotator_object:AnnotatedImageData,
                     file_paths:List[str],
                     doc:Document,
                     all_image_annotations:List[AnnotatedImageData],
                     current_page:int,
                     review_file_state:pd.DataFrame,
                     output_folder:str = OUTPUT_FOLDER,
                     save_pdf:bool=True,
                     page_sizes:List[dict]=[],
                     progress=gr.Progress(track_tqdm=True)):
    '''
    Apply modified redactions to a pymupdf and export review files
    '''

    output_files = []
    output_log_files = []
    pdf_doc = []
    review_df = review_file_state

    page_image_annotator_object = all_image_annotations[current_page - 1]   

    # This replaces the numpy array image object with the image file path
    page_image_annotator_object, all_image_annotations = replace_annotator_object_img_np_array_with_page_sizes_image_path(all_image_annotations, page_image_annotator_object, page_sizes, current_page)
    page_image_annotator_object['image'] = all_image_annotations[current_page - 1]["image"]

    if not page_image_annotator_object:
        print("No image annotations object found for page")
        return doc, all_image_annotations, output_files, output_log_files, review_df
    
    if isinstance(file_paths, str):
        file_paths = [file_paths]

    for file_path in file_paths:
        file_name_without_ext = get_file_name_without_type(file_path)
        file_name_with_ext = os.path.basename(file_path)

        file_extension = os.path.splitext(file_path)[1].lower()
        
        if save_pdf == True:
            # If working with image docs
            if (is_pdf(file_path) == False) & (file_extension not in '.csv'):
                image = Image.open(file_paths[-1])

                draw = ImageDraw.Draw(image)

                for img_annotation_box in page_image_annotator_object['boxes']:
                    coords = [img_annotation_box["xmin"],
                    img_annotation_box["ymin"],
                    img_annotation_box["xmax"],
                    img_annotation_box["ymax"]]

                    fill = img_annotation_box["color"]

                    # Ensure fill is a valid RGB tuple
                    if isinstance(fill, tuple) and len(fill) == 3:
                        # Check if all elements are integers in the range 0-255
                        if all(isinstance(c, int) and 0 <= c <= 255 for c in fill):
                            pass

                        else:
                            print(f"Invalid color values: {fill}. Defaulting to black.")
                            fill = (0, 0, 0)  # Default to black if invalid
                    else:
                        print(f"Invalid fill format: {fill}. Defaulting to black.")
                        fill = (0, 0, 0)  # Default to black if not a valid tuple

                        # Ensure the image is in RGB mode
                    if image.mode not in ("RGB", "RGBA"):
                        image = image.convert("RGB")

                    draw = ImageDraw.Draw(image)

                    draw.rectangle(coords, fill=fill)
                    
                    output_image_path = output_folder + file_name_without_ext + "_redacted.png"
                    image.save(output_folder + file_name_without_ext + "_redacted.png")

                output_files.append(output_image_path)

                doc = [image]

            elif file_extension in '.csv':
                pdf_doc = []

            # If working with pdfs
            elif is_pdf(file_path) == True:
                pdf_doc = pymupdf.open(file_path)
                orig_pdf_file_path = file_path

                output_files.append(orig_pdf_file_path)

                number_of_pages = pdf_doc.page_count
                original_cropboxes = []

                page_sizes_df = pd.DataFrame(page_sizes)
                page_sizes_df[["page"]] = page_sizes_df[["page"]].apply(pd.to_numeric, errors="coerce")

                for i in progress.tqdm(range(0, number_of_pages), desc="Saving redacted pages to file", unit = "pages"):
           
                    image_loc = all_image_annotations[i]['image']

                    # Load in image object
                    if isinstance(image_loc, np.ndarray):
                        image = Image.fromarray(image_loc.astype('uint8'))
                    elif isinstance(image_loc, Image.Image):
                        image = image_loc
                    elif isinstance(image_loc, str):
                        if not os.path.exists(image_loc):
                            image=page_sizes_df.loc[page_sizes_df['page']==i, "image_path"]
                        try:
                            image = Image.open(image_loc)
                        except Exception as e:
                            image = None

                    pymupdf_page = pdf_doc.load_page(i) #doc.load_page(current_page -1)
                    original_cropboxes.append(pymupdf_page.cropbox)
                    pymupdf_page.set_cropbox(pymupdf_page.mediabox)

                    pymupdf_page = redact_page_with_pymupdf(page=pymupdf_page, page_annotations=all_image_annotations[i], image=image, original_cropbox=original_cropboxes[-1], page_sizes_df= page_sizes_df) # image=image,
            else:
                print("File type not recognised.")

            progress(0.9, "Saving output files")

            #try:
            if pdf_doc:
                out_pdf_file_path = output_folder + file_name_without_ext + "_redacted.pdf"
                pdf_doc.save(out_pdf_file_path, garbage=4, deflate=True, clean=True)
                output_files.append(out_pdf_file_path)

            else:
                print("PDF input not found. Outputs not saved to PDF.")

        # If save_pdf is not true, then add the original pdf to the output files
        else:
            if is_pdf(file_path) == True:                
                orig_pdf_file_path = file_path
                output_files.append(orig_pdf_file_path)

        try:
            #print("Saving review file.")
            review_df = convert_annotation_json_to_review_df(all_image_annotations, review_file_state.copy(), page_sizes=page_sizes)

            page_sizes_df = pd.DataFrame(page_sizes)
            page_sizes_df .loc[:, "page"] = pd.to_numeric(page_sizes_df["page"], errors="coerce")
            review_df = divide_coordinates_by_page_sizes(review_df, page_sizes_df)

            review_df = review_df[["image",	"page",	"label","color", "xmin", "ymin", "xmax", "ymax", "text", "id"]]

            out_review_file_file_path = output_folder + file_name_with_ext + '_review_file.csv'

            review_df.to_csv(out_review_file_file_path, index=None)
            output_files.append(out_review_file_file_path)

        except Exception as e:
            print("In apply redactions function, could not save annotations to csv file:", e)

    return doc, all_image_annotations, output_files, output_log_files, review_df

def get_boxes_json(annotations:AnnotatedImageData):
    return annotations["boxes"]

def update_all_entity_df_dropdowns(df:pd.DataFrame, label_dropdown_value:str, page_dropdown_value:str, text_dropdown_value:str):
    '''
    Update all dropdowns based on rows that exist in a dataframe
    '''

    if isinstance(label_dropdown_value, str):
        label_dropdown_value = [label_dropdown_value]
    if isinstance(page_dropdown_value, str):
        page_dropdown_value = [page_dropdown_value]
    if isinstance(text_dropdown_value, str):
        text_dropdown_value = [text_dropdown_value]
    
    filtered_df = df.copy()

    # Apply filtering based on dropdown selections
    # if not "ALL" in page_dropdown_value:
    #     filtered_df = filtered_df[filtered_df["page"].astype(str).isin(page_dropdown_value)]
    
    # if not "ALL" in text_dropdown_value:
    #     filtered_df = filtered_df[filtered_df["text"].astype(str).isin(text_dropdown_value)]

    # if not "ALL" in label_dropdown_value:
    #     filtered_df = filtered_df[filtered_df["label"].astype(str).isin(label_dropdown_value)]

    recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "label")
    recogniser_entities_drop = gr.Dropdown(value=label_dropdown_value[0], choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)    

    text_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "text")
    text_entities_drop = gr.Dropdown(value=text_dropdown_value[0], choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

    page_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "page")
    page_entities_drop = gr.Dropdown(value=page_dropdown_value[0], choices=page_entities_for_drop, allow_custom_value=True, interactive=True)

    return recogniser_entities_drop, text_entities_drop, page_entities_drop

def update_entities_df_recogniser_entities(choice:str, df:pd.DataFrame, page_dropdown_value:str, text_dropdown_value:str):
    '''
    Update the rows in a dataframe depending on the user choice from a dropdown
    '''

    if isinstance(choice, str):
        choice = [choice]
    if isinstance(page_dropdown_value, str):
        page_dropdown_value = [page_dropdown_value]
    if isinstance(text_dropdown_value, str):
        text_dropdown_value = [text_dropdown_value]
    
    filtered_df = df.copy()

    # Apply filtering based on dropdown selections
    if not "ALL" in page_dropdown_value:
        filtered_df = filtered_df[filtered_df["page"].astype(str).isin(page_dropdown_value)]
    
    if not "ALL" in text_dropdown_value:
        filtered_df = filtered_df[filtered_df["text"].astype(str).isin(text_dropdown_value)]

    if not "ALL" in choice:
        filtered_df = filtered_df[filtered_df["label"].astype(str).isin(choice)]

    recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "label")
    recogniser_entities_drop = gr.Dropdown(value=choice[0], choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)    

    text_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "text")
    text_entities_drop = gr.Dropdown(value=text_dropdown_value[0], choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

    page_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "page")
    page_entities_drop = gr.Dropdown(value=page_dropdown_value[0], choices=page_entities_for_drop, allow_custom_value=True, interactive=True)

    return filtered_df, text_entities_drop, page_entities_drop
    
def update_entities_df_page(choice:str, df:pd.DataFrame, label_dropdown_value:str, text_dropdown_value:str):
    '''
    Update the rows in a dataframe depending on the user choice from a dropdown
    '''
    if isinstance(choice, str):
        choice = [choice]
    if isinstance(label_dropdown_value, str):
        label_dropdown_value = [label_dropdown_value]
    if isinstance(text_dropdown_value, str):
        text_dropdown_value = [text_dropdown_value]

    filtered_df = df.copy()

    # Apply filtering based on dropdown selections
    if not "ALL" in text_dropdown_value:
        filtered_df = filtered_df[filtered_df["text"].astype(str).isin(text_dropdown_value)]
    
    if not "ALL" in label_dropdown_value:
        filtered_df = filtered_df[filtered_df["label"].astype(str).isin(label_dropdown_value)]

    if not "ALL" in choice:
        filtered_df = filtered_df[filtered_df["page"].astype(str).isin(choice)]

    recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "label")
    recogniser_entities_drop = gr.Dropdown(value=label_dropdown_value[0], choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)    

    text_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "text")
    text_entities_drop = gr.Dropdown(value=text_dropdown_value[0], choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

    page_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "page")
    page_entities_drop = gr.Dropdown(value=choice[0], choices=page_entities_for_drop, allow_custom_value=True, interactive=True)    

    return filtered_df, recogniser_entities_drop, text_entities_drop
    
def update_entities_df_text(choice:str, df:pd.DataFrame, label_dropdown_value:str, page_dropdown_value:str):
    '''
    Update the rows in a dataframe depending on the user choice from a dropdown
    '''
    if isinstance(choice, str):
        choice = [choice]
    if isinstance(label_dropdown_value, str):
        label_dropdown_value = [label_dropdown_value]
    if isinstance(page_dropdown_value, str):
        page_dropdown_value = [page_dropdown_value]

    filtered_df = df.copy()

    # Apply filtering based on dropdown selections
    if not "ALL" in page_dropdown_value:
        filtered_df = filtered_df[filtered_df["page"].astype(str).isin(page_dropdown_value)]
    
    if not "ALL" in label_dropdown_value:
        filtered_df = filtered_df[filtered_df["label"].astype(str).isin(label_dropdown_value)]

    if not "ALL" in choice:
        filtered_df = filtered_df[filtered_df["text"].astype(str).isin(choice)]

    recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "label")
    recogniser_entities_drop = gr.Dropdown(value=label_dropdown_value[0], choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)    

    text_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "text")
    text_entities_drop = gr.Dropdown(value=choice[0], choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

    page_entities_for_drop = update_dropdown_list_based_on_dataframe(filtered_df, "page")
    page_entities_drop = gr.Dropdown(value=page_dropdown_value[0], choices=page_entities_for_drop, allow_custom_value=True, interactive=True)    

    return filtered_df, recogniser_entities_drop, page_entities_drop
    
def reset_dropdowns(df:pd.DataFrame):
    '''
    Return Gradio dropdown objects with value 'ALL'.
    '''

    recogniser_entities_for_drop = update_dropdown_list_based_on_dataframe(df, "label")
    recogniser_entities_drop = gr.Dropdown(value="ALL", choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True)    

    text_entities_for_drop = update_dropdown_list_based_on_dataframe(df, "text")
    text_entities_drop = gr.Dropdown(value="ALL", choices=text_entities_for_drop, allow_custom_value=True, interactive=True)

    page_entities_for_drop = update_dropdown_list_based_on_dataframe(df, "page")
    page_entities_drop = gr.Dropdown(value="ALL", choices=page_entities_for_drop, allow_custom_value=True, interactive=True)

    return recogniser_entities_drop, text_entities_drop, page_entities_drop
    
def df_select_callback(df: pd.DataFrame, evt: gr.SelectData):

        row_value_page = evt.row_value[0] # This is the page number value
        row_value_label = evt.row_value[1] # This is the label number value
        row_value_text = evt.row_value[2] # This is the text number value
        row_value_id = evt.row_value[3] # This is the text number value

        row_value_df = pd.DataFrame(data={"page":[row_value_page], "label":[row_value_label], "text":[row_value_text], "id":[row_value_id]})

        return row_value_df

def df_select_callback_textract_api(df: pd.DataFrame, evt: gr.SelectData):

        row_value_job_id = evt.row_value[0] # This is the page number value
        # row_value_label = evt.row_value[1] # This is the label number value
        row_value_job_type = evt.row_value[2] # This is the text number value

        row_value_df = pd.DataFrame(data={"job_id":[row_value_job_id], "label":[row_value_job_type]})

        return row_value_job_id, row_value_job_type, row_value_df

def df_select_callback_cost(df: pd.DataFrame, evt: gr.SelectData):

        row_value_code = evt.row_value[0] # This is the value for cost code
        #row_value_label = evt.row_value[1] # This is the label number value

        #row_value_df = pd.DataFrame(data={"page":[row_value_code], "label":[row_value_label]})

        return row_value_code

def df_select_callback_ocr(df: pd.DataFrame, evt: gr.SelectData):

        row_value_page = evt.row_value[0] # This is the page_number value
        row_value_text = evt.row_value[1] # This is the text contents

        row_value_df = pd.DataFrame(data={"page":[row_value_page], "text":[row_value_text]})

        return row_value_page, row_value_df

def update_selected_review_df_row_colour(
    redaction_row_selection: pd.DataFrame,
    review_df: pd.DataFrame,
    previous_id: str = "",
    previous_colour: str = '(0, 0, 0)',
    colour: str = '(1, 0, 255)'
) -> tuple[pd.DataFrame, str, str]:
    '''
    Update the colour of a single redaction box based on the values in a selection row
    (Optimized Version)
    '''

    # Ensure 'color' column exists, default to previous_colour if previous_id is provided
    if "color" not in review_df.columns:
        review_df["color"] = previous_colour if previous_id else '(0, 0, 0)'

    # Ensure 'id' column exists
    if "id" not in review_df.columns:
         # Assuming fill_missing_ids is a defined function that returns a DataFrame
         # It's more efficient if this is handled outside if possible,
         # or optimized internally.
         print("Warning: 'id' column not found. Calling fill_missing_ids.")
         review_df = fill_missing_ids(review_df) # Keep this if necessary, but note it can be slow

    # --- Optimization 1 & 2: Reset existing highlight colours using vectorized assignment ---
    # Reset the color of the previously highlighted row
    if previous_id and previous_id in review_df["id"].values:
         review_df.loc[review_df["id"] == previous_id, "color"] = previous_colour

    # Reset the color of any row that currently has the highlight colour (handle cases where previous_id might not have been tracked correctly)
    # Convert to string for comparison only if the dtype might be mixed or not purely string
    # If 'color' is consistently string, the .astype(str) might be avoidable.
    # Assuming color is consistently string format like '(R, G, B)'
    review_df.loc[review_df["color"] == colour, "color"] = '(0, 0, 0)'


    if not redaction_row_selection.empty and not review_df.empty:
        use_id = (
            "id" in redaction_row_selection.columns
            and "id" in review_df.columns
            and not redaction_row_selection["id"].isnull().all()
            and not review_df["id"].isnull().all()
        )

        selected_merge_cols = ["id"] if use_id else ["label", "page", "text"]

        # --- Optimization 3: Use inner merge directly ---
        # Merge to find rows in review_df that match redaction_row_selection
        merged_reviews = review_df.merge(
            redaction_row_selection[selected_merge_cols],
            on=selected_merge_cols,
            how="inner" # Use inner join as we only care about matches
        )

        if not merged_reviews.empty:
             # Assuming we only expect one match for highlighting a single row
             # If multiple matches are possible and you want to highlight all,
             # the logic for previous_id and previous_colour needs adjustment.
            new_previous_colour = str(merged_reviews["color"].iloc[0])
            new_previous_id = merged_reviews["id"].iloc[0]

            # --- Optimization 1 & 2: Update color of the matched row using vectorized assignment ---

            if use_id:
                 # Faster update if using unique 'id' as merge key
                 review_df.loc[review_df["id"].isin(merged_reviews["id"]), "color"] = colour
            else:
                 # More general case using multiple columns - might be slower
                 # Create a temporary key for comparison
                 def create_merge_key(df, cols):
                     return df[cols].astype(str).agg('_'.join, axis=1)

                 review_df_key = create_merge_key(review_df, selected_merge_cols)
                 merged_reviews_key = create_merge_key(merged_reviews, selected_merge_cols)

                 review_df.loc[review_df_key.isin(merged_reviews_key), "color"] = colour

            previous_colour = new_previous_colour
            previous_id = new_previous_id
        else:
             # No rows matched the selection
             print("No reviews found matching selection criteria")
             # The reset logic at the beginning already handles setting color to (0, 0, 0)
             # if it was the highlight colour and didn't match.
             # No specific action needed here for color reset beyond what's done initially.
             previous_colour = '(0, 0, 0)' # Reset previous_colour as no row was highlighted
             previous_id = '' # Reset previous_id

    else:
         # If selection is empty, reset any existing highlights
         review_df.loc[review_df["color"] == colour, "color"] = '(0, 0, 0)'
         previous_colour = '(0, 0, 0)'
         previous_id = ''


    # Ensure column order is maintained if necessary, though pandas generally preserves order
    # Creating a new DataFrame here might involve copying data, consider if this is strictly needed.
    if set(["image", "page", "label", "color", "xmin","ymin", "xmax", "ymax", "text", "id"]).issubset(review_df.columns):
        review_df = review_df[["image", "page", "label", "color", "xmin","ymin", "xmax", "ymax", "text", "id"]]
    else:
         print("Warning: Not all expected columns are present in review_df for reordering.")


    return review_df, previous_id, previous_colour

def update_boxes_color(images: list, redaction_row_selection: pd.DataFrame, colour: tuple = (0, 255, 0)):
    """
    Update the color of bounding boxes in the images list based on redaction_row_selection.
    
    Parameters:
    - images (list): List of dictionaries containing image paths and box metadata.
    - redaction_row_selection (pd.DataFrame): DataFrame with 'page', 'label', and optionally 'text' columns.
    - colour (tuple): RGB tuple for the new color.
    
    Returns:
    - Updated list with modified colors.
    """
    # Convert DataFrame to a set for fast lookup
    selection_set = set(zip(redaction_row_selection["page"], redaction_row_selection["label"]))

    for page_idx, image_obj in enumerate(images):
        if "boxes" in image_obj:
            for box in image_obj["boxes"]:
                if (page_idx, box["label"]) in selection_set:
                    box["color"] = colour  # Update color
    
    return images

def update_other_annotator_number_from_current(page_number_first_counter:int):
    return page_number_first_counter

def convert_image_coords_to_adobe(pdf_page_width:float, pdf_page_height:float, image_width:float, image_height:float, x1:float, y1:float, x2:float, y2:float):
    '''
    Converts coordinates from image space to Adobe PDF space.
    
    Parameters:
    - pdf_page_width: Width of the PDF page
    - pdf_page_height: Height of the PDF page
    - image_width: Width of the source image
    - image_height: Height of the source image
    - x1, y1, x2, y2: Coordinates in image space
    - page_sizes: List of dicts containing sizes of page as pymupdf page or PIL image
    
    Returns:
    - Tuple of converted coordinates (x1, y1, x2, y2) in Adobe PDF space
    '''

    
    
    # Calculate scaling factors
    scale_width = pdf_page_width / image_width
    scale_height = pdf_page_height / image_height
    
    # Convert coordinates
    pdf_x1 = x1 * scale_width
    pdf_x2 = x2 * scale_width
    
    # Convert Y coordinates (flip vertical axis)
    # Adobe coordinates start from bottom-left
    pdf_y1 = pdf_page_height - (y1 * scale_height)
    pdf_y2 = pdf_page_height - (y2 * scale_height)
    
    # Make sure y1 is always less than y2 for Adobe's coordinate system
    if pdf_y1 > pdf_y2:
        pdf_y1, pdf_y2 = pdf_y2, pdf_y1
    
    return pdf_x1, pdf_y1, pdf_x2, pdf_y2

def convert_pymupdf_coords_to_adobe(x1: float, y1: float, x2: float, y2: float, pdf_page_height: float):
    """
    Converts coordinates from PyMuPDF (fitz) space to Adobe PDF space.
    
    Parameters:
    - x1, y1, x2, y2: Coordinates in PyMuPDF space
    - pdf_page_height: Total height of the PDF page
    
    Returns:
    - Tuple of converted coordinates (x1, y1, x2, y2) in Adobe PDF space
    """

    # PyMuPDF uses (0,0) at the bottom-left, while Adobe uses (0,0) at the top-left
    adobe_y1 = pdf_page_height - y2  # Convert top coordinate
    adobe_y2 = pdf_page_height - y1  # Convert bottom coordinate
    
    return x1, adobe_y1, x2, adobe_y2

def create_xfdf(review_file_df:pd.DataFrame, pdf_path:str, pymupdf_doc:object, image_paths:List[str], document_cropboxes:List=[], page_sizes:List[dict]=[]):
    '''
    Create an xfdf file from a review csv file and a pdf
    '''
    pages_are_images = True

    # Create root element
    xfdf = Element('xfdf', xmlns="http://ns.adobe.com/xfdf/", xml_space="preserve")
    
    # Add header
    header = SubElement(xfdf, 'header')
    header.set('pdf-filepath', pdf_path)
    
    # Add annots
    annots = SubElement(xfdf, 'annots')

    # Check if page size object exists, and if current coordinates are in relative format or image coordinates format.
    if page_sizes: 
        
        page_sizes_df = pd.DataFrame(page_sizes)

        # If there are no image coordinates, then convert coordinates to pymupdf coordinates prior to export
        pages_are_images = False

        if "mediabox_width" not in review_file_df.columns:            
                review_file_df = review_file_df.merge(page_sizes_df, how="left", on = "page")
        
        # If all coordinates are less or equal to one, this is a relative page scaling - change back to image coordinates
        if review_file_df["xmin"].max() <= 1 and review_file_df["xmax"].max() <= 1 and review_file_df["ymin"].max() <= 1 and review_file_df["ymax"].max() <= 1:
            review_file_df["xmin"] = review_file_df["xmin"] * review_file_df["mediabox_width"]
            review_file_df["xmax"] = review_file_df["xmax"] * review_file_df["mediabox_width"]
            review_file_df["ymin"] = review_file_df["ymin"] * review_file_df["mediabox_height"]
            review_file_df["ymax"] = review_file_df["ymax"] * review_file_df["mediabox_height"]

        # If all nulls, then can do image coordinate conversion
        if len(page_sizes_df.loc[page_sizes_df["mediabox_width"].isnull(),"mediabox_width"]) == len(page_sizes_df["mediabox_width"]):

            pages_are_images = True

            review_file_df = multiply_coordinates_by_page_sizes(review_file_df, page_sizes_df, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax")

            # if "image_width" not in review_file_df.columns:            
            #         review_file_df = review_file_df.merge(page_sizes_df, how="left", on = "page")
            
            # # If all coordinates are less or equal to one, this is a relative page scaling - change back to image coordinates
            # if review_file_df["xmin"].max() <= 1 and review_file_df["xmax"].max() <= 1 and review_file_df["ymin"].max() <= 1 and review_file_df["ymax"].max() <= 1:
            #     review_file_df["xmin"] = review_file_df["xmin"] * review_file_df["image_width"]
            #     review_file_df["xmax"] = review_file_df["xmax"] * review_file_df["image_width"]
            #     review_file_df["ymin"] = review_file_df["ymin"] * review_file_df["image_height"]
            #     review_file_df["ymax"] = review_file_df["ymax"] * review_file_df["image_height"]

                
    
    # Go through each row of the review_file_df, create an entry in the output Adobe xfdf file.
    for _, row in review_file_df.iterrows():
        page_num_reported = row["page"]
        page_python_format = int(row["page"])-1

        pymupdf_page = pymupdf_doc.load_page(page_python_format)

        # Load cropbox sizes. Set cropbox to the original cropbox sizes from when the document was loaded into the app.
        if document_cropboxes:

            # Extract numbers safely using regex
            match = re.findall(r"[-+]?\d*\.\d+|\d+", document_cropboxes[page_python_format])

            if match and len(match) == 4:
                rect_values = list(map(float, match))  # Convert extracted strings to floats
                pymupdf_page.set_cropbox(Rect(*rect_values))
            else:
                raise ValueError(f"Invalid cropbox format: {document_cropboxes[page_python_format]}")
        else:
            print("Document cropboxes not found.")
        
        pdf_page_height = pymupdf_page.mediabox.height
        pdf_page_width = pymupdf_page.mediabox.width     

        # Create redaction annotation
        redact_annot = SubElement(annots, 'redact')
        
        # Generate unique ID
        annot_id = str(uuid.uuid4())
        redact_annot.set('name', annot_id)
        
        # Set page number (subtract 1 as PDF pages are 0-based)
        redact_annot.set('page', str(int(row['page']) - 1))
        
        # # Convert coordinates
        # if pages_are_images == True:
        #     x1, y1, x2, y2 = convert_image_coords_to_adobe(
        #         pdf_page_width,
        #         pdf_page_height,
        #         image_page_width,
        #         image_page_height,
        #         row['xmin'],
        #         row['ymin'],
        #         row['xmax'],
        #         row['ymax']
        #     )
        # else:
        x1, y1, x2, y2 = convert_pymupdf_coords_to_adobe(row['xmin'],
            row['ymin'],
            row['xmax'],
            row['ymax'], pdf_page_height)

        if CUSTOM_BOX_COLOUR == "grey":
            colour_str = "0.5,0.5,0.5"        
        else:
            colour_str = row['color'].strip('()').replace(' ', '')
        
        # Set coordinates
        redact_annot.set('rect', f"{x1:.2f},{y1:.2f},{x2:.2f},{y2:.2f}")
        
        # Set redaction properties
        redact_annot.set('title', row['label'])  # The type of redaction (e.g., "PERSON")
        redact_annot.set('contents', row['text'])  # The redacted text
        redact_annot.set('subject', row['label'])  # The redacted text
        redact_annot.set('mimetype', "Form")
        
        # Set appearance properties
        redact_annot.set('border-color', colour_str)  # Black border
        redact_annot.set('repeat', 'false')
        redact_annot.set('interior-color', colour_str)
        #redact_annot.set('fill-color', colour_str)
        #redact_annot.set('outline-color', colour_str)
        #redact_annot.set('overlay-color', colour_str)
        #redact_annot.set('overlay-text', row['label'])
        redact_annot.set('opacity', "0.5")

        # Add appearance dictionary
        # appearanceDict = SubElement(redact_annot, 'appearancedict')
        
        # # Normal appearance
        # normal = SubElement(appearanceDict, 'normal')
        # #normal.set('appearance', 'redact')
                
        # # Color settings for the mark (before applying redaction)
        # markAppearance = SubElement(redact_annot, 'markappearance')
        # markAppearance.set('stroke-color', colour_str)  # Red outline
        # markAppearance.set('fill-color', colour_str)    # Light red fill
        # markAppearance.set('opacity', '0.5')          # 50% opacity
        
        # # Final redaction appearance (after applying)
        # redactAppearance = SubElement(redact_annot, 'redactAppearance')
        # redactAppearance.set('fillColor', colour_str)  # Black fill
        # redactAppearance.set('fontName', 'Helvetica')
        # redactAppearance.set('fontSize', '12')
        # redactAppearance.set('textAlignment', 'left')
        # redactAppearance.set('textColor', colour_str)  # White text
    
    # Convert to pretty XML string
    xml_str = minidom.parseString(tostring(xfdf)).toprettyxml(indent="  ")
    
    return xml_str

def convert_df_to_xfdf(input_files:List[str], pdf_doc:Document, image_paths:List[str], output_folder:str = OUTPUT_FOLDER, document_cropboxes:List=[], page_sizes:List[dict]=[]):
    '''
    Load in files to convert a review file into an Adobe comment file format
    '''
    output_paths = []
    pdf_name = ""
    file_path_name = ""

    if isinstance(input_files, str):
        file_paths_list = [input_files]
    else:
        file_paths_list = input_files

    # Sort the file paths so that the pdfs come first
    file_paths_list = sorted(file_paths_list, key=lambda x: (os.path.splitext(x)[1] != '.pdf', os.path.splitext(x)[1] != '.json')) 
    
    for file in file_paths_list:

        if isinstance(file, str):
            file_path = file
        else:
            file_path = file.name
    
        file_path_name = get_file_name_without_type(file_path)
        file_path_end = detect_file_type(file_path)

        if file_path_end == "pdf":
            pdf_name = os.path.basename(file_path)

        if file_path_end == "csv":
            # If no pdf name, just get the name of the file path
            if not pdf_name:
                pdf_name = file_path_name
            # Read CSV file
            review_file_df = pd.read_csv(file_path)

            review_file_df.fillna('', inplace=True)  # Replace NaN in review file with an empty string

            xfdf_content = create_xfdf(review_file_df, pdf_name, pdf_doc, image_paths, document_cropboxes, page_sizes)

            output_path = output_folder + file_path_name + "_adobe.xfdf"        
            
            with open(output_path, 'w', encoding='utf-8') as f:
                f.write(xfdf_content)

            output_paths.append(output_path)

    return output_paths


### Convert xfdf coordinates back to image for app

def convert_adobe_coords_to_image(pdf_page_width:float, pdf_page_height:float, image_width:float, image_height:float, x1:float, y1:float, x2:float, y2:float):
    '''
    Converts coordinates from Adobe PDF space to image space.
    
    Parameters:
    - pdf_page_width: Width of the PDF page
    - pdf_page_height: Height of the PDF page
    - image_width: Width of the source image
    - image_height: Height of the source image
    - x1, y1, x2, y2: Coordinates in Adobe PDF space
    
    Returns:
    - Tuple of converted coordinates (x1, y1, x2, y2) in image space
    '''
    
    # Calculate scaling factors
    scale_width = image_width / pdf_page_width
    scale_height = image_height / pdf_page_height
    
    # Convert coordinates
    image_x1 = x1 * scale_width
    image_x2 = x2 * scale_width
    
    # Convert Y coordinates (flip vertical axis)
    # Adobe coordinates start from bottom-left
    image_y1 = (pdf_page_height - y1) * scale_height
    image_y2 = (pdf_page_height - y2) * scale_height
    
    # Make sure y1 is always less than y2 for image's coordinate system
    if image_y1 > image_y2:
        image_y1, image_y2 = image_y2, image_y1
    
    return image_x1, image_y1, image_x2, image_y2

def parse_xfdf(xfdf_path:str):
    '''
    Parse the XFDF file and extract redaction annotations.
    
    Parameters:
    - xfdf_path: Path to the XFDF file
    
    Returns:
    - List of dictionaries containing redaction information
    '''
    tree = parse(xfdf_path)
    root = tree.getroot()
    
    # Define the namespace
    namespace = {'xfdf': 'http://ns.adobe.com/xfdf/'}
    
    redactions = []
    
    # Find all redact elements using the namespace
    for redact in root.findall('.//xfdf:redact', namespaces=namespace):

        redaction_info = {
            'image': '', # Image will be filled in later
            'page': int(redact.get('page')) + 1,  # Convert to 1-based index
            'xmin': float(redact.get('rect').split(',')[0]),
            'ymin': float(redact.get('rect').split(',')[1]),
            'xmax': float(redact.get('rect').split(',')[2]),
            'ymax': float(redact.get('rect').split(',')[3]),
            'label': redact.get('title'),
            'text': redact.get('contents'),
            'color': redact.get('border-color', '(0, 0, 0)')  # Default to black if not specified
        }
        redactions.append(redaction_info)
    
    return redactions

def convert_xfdf_to_dataframe(file_paths_list:List[str], pymupdf_doc, image_paths:List[str], output_folder:str=OUTPUT_FOLDER):
    '''
    Convert redaction annotations from XFDF and associated images into a DataFrame.
    
    Parameters:
    - xfdf_path: Path to the XFDF file
    - pdf_doc: PyMuPDF document object
    - image_paths: List of PIL Image objects corresponding to PDF pages
    
    Returns:
    - DataFrame containing redaction information
    '''
    output_paths = []
    xfdf_paths = []
    df = pd.DataFrame()

    # Sort the file paths so that the pdfs come first
    file_paths_list = sorted(file_paths_list, key=lambda x: (os.path.splitext(x)[1] != '.pdf', os.path.splitext(x)[1] != '.json'))
    
    for file in file_paths_list:

        if isinstance(file, str):
            file_path = file
        else:
            file_path = file.name
    
        file_path_name = get_file_name_without_type(file_path)
        file_path_end = detect_file_type(file_path)

        if file_path_end == "pdf":
            pdf_name = os.path.basename(file_path)

            # Add pdf to outputs
            output_paths.append(file_path)

        if file_path_end == "xfdf":

            if not pdf_name:
                message = "Original PDF needed to convert from .xfdf format"
                print(message)
                raise ValueError(message)
            xfdf_path = file

            file_path_name = get_file_name_without_type(xfdf_path)

            # Parse the XFDF file
            redactions = parse_xfdf(xfdf_path)
            
            # Create a DataFrame from the redaction information
            df = pd.DataFrame(redactions)

            df.fillna('', inplace=True)  # Replace NaN with an empty string

            for _, row in df.iterrows():
                page_python_format = int(row["page"])-1

                pymupdf_page = pymupdf_doc.load_page(page_python_format)

                pdf_page_height = pymupdf_page.rect.height
                pdf_page_width = pymupdf_page.rect.width 

                image_path = image_paths[page_python_format]

                if isinstance(image_path, str):
                    image = Image.open(image_path)

                image_page_width, image_page_height = image.size

                # Convert to image coordinates
                image_x1, image_y1, image_x2, image_y2 = convert_adobe_coords_to_image(pdf_page_width, pdf_page_height, image_page_width, image_page_height, row['xmin'], row['ymin'], row['xmax'], row['ymax'])

                df.loc[_, ['xmin', 'ymin', 'xmax', 'ymax']] = [image_x1, image_y1, image_x2, image_y2]
            
                # Optionally, you can add the image path or other relevant information
                df.loc[_, 'image'] = image_path

    out_file_path = output_folder + file_path_name + "_review_file.csv"
    df.to_csv(out_file_path, index=None)

    output_paths.append(out_file_path)
    
    return output_paths