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 _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"""

Warning

Input text was truncated to {max_input_length} words. That's about {100*max_input_length/len(input_wc):.2f}% of the submission.

""" 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"""

Warning

Input text is too short to summarize. Detected {len(input_text)} characters. Check your input as strange things may happen.

""" logging.warning(msg) history["WARNING"] = 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"] = "

" scores_out = "\n".join(sum_scores) rt = round((time.perf_counter() - st) / 60, 2) print(f"Runtime: {rt} minutes") html = "" html += f"

Runtime: {rt} minutes on CPU

" if msg is not None: html += msg html += "" return html, sum_text_out, scores_out 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("

Output will appear below:

") 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" ) 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], ) demo.launch(enable_queue=True)