import os
import contextlib
import logging
import random
import re
import time
from pathlib import Path
import gradio as gr
import nltk
from cleantext import clean
from doctr.io import DocumentFile
from doctr.models import ocr_predictor
from pdf2text import convert_PDF_to_Text
from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
from utils import load_example_filenames, truncate_word_count, saves_summary
_here = Path(__file__).parent
nltk.download("stopwords") # TODO=find where this requirement originates from
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
def proc_submission(
input_text: str,
model_size: str,
num_beams,
token_batch_length,
length_penalty,
repetition_penalty,
no_repeat_ngram_size,
max_input_length: int = 1024,
):
"""
proc_submission - a helper function for the gradio module to process submissions
Args:
input_text (str): the input text to summarize
model_size (str): the size of the model to use
num_beams (int): the number of beams to use
token_batch_length (int): the length of the token batches to use
length_penalty (float): the length penalty to use
repetition_penalty (float): the repetition penalty to use
no_repeat_ngram_size (int): the no repeat ngram size to use
max_input_length (int, optional): the maximum input length to use. Defaults to 768.
Returns:
str in HTML format, string of the summary, str of score
"""
settings = {
"length_penalty": float(length_penalty),
"repetition_penalty": float(repetition_penalty),
"no_repeat_ngram_size": int(no_repeat_ngram_size),
"encoder_no_repeat_ngram_size": 4,
"num_beams": int(num_beams),
"min_length": 4,
"max_length": int(token_batch_length // 4),
"early_stopping": True,
"do_sample": False,
}
st = time.perf_counter()
history = {}
clean_text = clean(input_text, lower=False)
max_input_length = 2560 if "base" in model_size.lower() else max_input_length
processed = truncate_word_count(clean_text, max_input_length)
if processed["was_truncated"]:
tr_in = processed["truncated_text"]
# create elaborate HTML warning
input_wc = re.split(r"\s+", input_text)
msg = f"""
<div style="background-color: #FFA500; color: white; padding: 20px;">
<h3>Warning</h3>
<p>Input text was truncated to {max_input_length} words. That's about {100*max_input_length/len(input_wc):.2f}% of the submission.</p>
</div>
"""
logging.warning(msg)
history["WARNING"] = msg
else:
tr_in = input_text
msg = None
if len(input_text) < 50:
# this is essentially a different case from the above
msg = f"""
<div style="background-color: #880808; color: white; padding: 20px;">
<h3>Warning</h3>
<p>Input text is too short to summarize. Detected {len(input_text)} characters.
Please load text by selecting an example from the dropdown menu or by pasting text into the text box.</p>
</div>
"""
logging.warning(msg)
logging.warning("RETURNING EMPTY STRING")
history["WARNING"] = msg
return msg, "", []
_summaries = summarize_via_tokenbatches(
tr_in,
model_sm if "base" in model_size.lower() else model,
tokenizer_sm if "base" in model_size.lower() else tokenizer,
batch_length=token_batch_length,
**settings,
)
sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
sum_scores = [
f" - Section {i}: {round(s['summary_score'],4)}"
for i, s in enumerate(_summaries)
]
sum_text_out = "\n".join(sum_text)
history["Summary Scores"] = "<br><br>"
scores_out = "\n".join(sum_scores)
rt = round((time.perf_counter() - st) / 60, 2)
print(f"Runtime: {rt} minutes")
html = ""
html += f"<p>Runtime: {rt} minutes on CPU</p>"
if msg is not None:
html += msg
html += ""
# save to file
saved_file = saves_summary(_summaries)
return html, sum_text_out, scores_out, saved_file
def load_single_example_text(
example_path: str or Path,
max_pages=20,
):
"""
load_single_example - a helper function for the gradio module to load examples
Returns:
list of str, the examples
"""
global name_to_path
full_ex_path = name_to_path[example_path]
full_ex_path = Path(full_ex_path)
if full_ex_path.suffix == ".txt":
with open(full_ex_path, "r", encoding="utf-8", errors="ignore") as f:
raw_text = f.read()
text = clean(raw_text, lower=False)
elif full_ex_path.suffix == ".pdf":
logging.info(f"Loading PDF file {full_ex_path}")
conversion_stats = convert_PDF_to_Text(
full_ex_path,
ocr_model=ocr_model,
max_pages=max_pages,
)
text = conversion_stats["converted_text"]
else:
logging.error(f"Unknown file type {full_ex_path.suffix}")
text = "ERROR - check example path"
return text
def load_uploaded_file(file_obj, max_pages=20):
"""
load_uploaded_file - process an uploaded file
Args:
file_obj (POTENTIALLY list): Gradio file object inside a list
Returns:
str, the uploaded file contents
"""
# file_path = Path(file_obj[0].name)
# check if mysterious file object is a list
if isinstance(file_obj, list):
file_obj = file_obj[0]
file_path = Path(file_obj.name)
try:
if file_path.suffix == ".txt":
with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
raw_text = f.read()
text = clean(raw_text, lower=False)
elif file_path.suffix == ".pdf":
logging.info(f"Loading PDF file {file_path}")
conversion_stats = convert_PDF_to_Text(
file_path,
ocr_model=ocr_model,
max_pages=max_pages,
)
text = conversion_stats["converted_text"]
else:
logging.error(f"Unknown file type {file_path.suffix}")
text = "ERROR - check example path"
return text
except Exception as e:
logging.info(f"Trying to load file with path {file_path}, error: {e}")
return "Error: Could not read file. Ensure that it is a valid text file with encoding UTF-8 if text, and a PDF if PDF."
if __name__ == "__main__":
logging.info("Starting app instance")
os.environ[
"TOKENIZERS_PARALLELISM"
] = "false" # parallelism on tokenizers is buggy with gradio
logging.info("Loading summ models")
with contextlib.redirect_stdout(None):
model, tokenizer = load_model_and_tokenizer(
"pszemraj/pegasus-x-large-book-summary"
)
model_sm, tokenizer_sm = load_model_and_tokenizer(
"pszemraj/long-t5-tglobal-base-16384-book-summary"
)
logging.info("Loading OCR model")
with contextlib.redirect_stdout(None):
ocr_model = ocr_predictor(
"db_resnet50",
"crnn_mobilenet_v3_large",
pretrained=True,
assume_straight_pages=True,
)
name_to_path = load_example_filenames(_here / "examples")
logging.info(f"Loaded {len(name_to_path)} examples")
demo = gr.Blocks()
_examples = list(name_to_path.keys())
with demo:
gr.Markdown("# Document Summarization with Long-Document Transformers")
gr.Markdown(
"This is an example use case for fine-tuned long document transformers. The model is trained on book summaries (via the BookSum dataset). The models in this demo are [LongT5-base](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) and [Pegasus-X-Large](https://huggingface.co/pszemraj/pegasus-x-large-book-summary)."
)
with gr.Column():
gr.Markdown("## Load Inputs & Select Parameters")
gr.Markdown(
"Enter text below in the text area. The text will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). Optionally load an example below or upload a file. (`.txt` or `.pdf` - _[link to guide](https://i.imgur.com/c6Cs9ly.png)_)"
)
with gr.Row(variant="compact"):
with gr.Column(scale=0.5, variant="compact"):
model_size = gr.Radio(
choices=["LongT5-base", "Pegasus-X-large"],
label="Model Variant",
value="LongT5-base",
)
num_beams = gr.Radio(
choices=[2, 3, 4],
label="Beam Search: # of Beams",
value=2,
)
with gr.Column(variant="compact"):
example_name = gr.Dropdown(
_examples,
label="Examples",
value=random.choice(_examples),
)
uploaded_file = gr.File(
label="File Upload",
file_count="single",
type="file",
)
with gr.Row():
input_text = gr.Textbox(
lines=4,
label="Input Text (for summarization)",
placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
)
with gr.Column(min_width=100, scale=0.5):
load_examples_button = gr.Button(
"Load Example",
)
load_file_button = gr.Button("Upload File")
with gr.Column():
gr.Markdown("## Generate Summary")
gr.Markdown(
"Summarization should take ~1-2 minutes for most settings, but may extend up to 5-10 minutes in some scenarios."
)
summarize_button = gr.Button(
"Summarize!",
variant="primary",
)
output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
gr.Markdown("### Summary Output")
summary_text = gr.Textbox(
label="Summary", placeholder="The generated summary will appear here"
)
gr.Markdown(
"The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better:"
)
summary_scores = gr.Textbox(
label="Summary Scores", placeholder="Summary scores will appear here"
)
text_file = gr.File(
label="Download Summary as Text File",
file_count="single",
type="file",
interactive=False,
)
gr.Markdown("---")
with gr.Column():
gr.Markdown("### Advanced Settings")
with gr.Row(variant="compact"):
length_penalty = gr.inputs.Slider(
minimum=0.5,
maximum=1.0,
label="length penalty",
default=0.7,
step=0.05,
)
token_batch_length = gr.Radio(
choices=[512, 768, 1024, 1536],
label="token batch length",
value=1024,
)
with gr.Row(variant="compact"):
repetition_penalty = gr.inputs.Slider(
minimum=1.0,
maximum=5.0,
label="repetition penalty",
default=3.5,
step=0.1,
)
no_repeat_ngram_size = gr.Radio(
choices=[2, 3, 4],
label="no repeat ngram size",
value=3,
)
with gr.Column():
gr.Markdown("### About the Model")
gr.Markdown(
"These models are fine-tuned on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
)
gr.Markdown("---")
load_examples_button.click(
fn=load_single_example_text, inputs=[example_name], outputs=[input_text]
)
load_file_button.click(
fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text]
)
summarize_button.click(
fn=proc_submission,
inputs=[
input_text,
model_size,
num_beams,
token_batch_length,
length_penalty,
repetition_penalty,
no_repeat_ngram_size,
],
outputs=[output_text, summary_text, summary_scores, text_file],
)
demo.launch(enable_queue=True)