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| import contextlib | |
| import logging | |
| 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 = 2048 if model_size == "base" 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. This is 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 | |
| _summaries = summarize_via_tokenbatches( | |
| tr_in, | |
| model_sm if model_size == "base" else model, | |
| tokenizer_sm if model_size == "base" 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 += "" | |
| 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") | |
| 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() | |
| with demo: | |
| gr.Markdown("# Document Summarization with Long-Document Transformers") | |
| gr.Markdown( | |
| "TODO: Add a description of the model and how it works, and a link to the paper" | |
| ) | |
| 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." | |
| ) | |
| with gr.Row(): | |
| model_size = gr.Radio( | |
| choices=["base", "large"], label="Model Variant", value="base" | |
| ) | |
| num_beams = gr.Radio( | |
| choices=[2, 3, 4], | |
| label="Beam Search: # of Beams", | |
| value=2, | |
| ) | |
| with gr.Row(): | |
| example_name = gr.Dropdown( | |
| list(name_to_path.keys()), | |
| label="Choose an Example", | |
| ) | |
| load_examples_button = gr.Button( | |
| "Load Example", | |
| ) | |
| input_text = gr.Textbox( | |
| lines=6, | |
| 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 :)", | |
| ) | |
| gr.Markdown("Upload a file (`.txt` or `.pdf`)") | |
| with gr.Row(): | |
| uploaded_file = gr.File( | |
| label="Upload file", | |
| file_count="single", | |
| type="file", | |
| ) | |
| load_file_button = gr.Button("Load Uploaded File") | |
| gr.Markdown("---") | |
| with gr.Column(): | |
| gr.Markdown("## Generate Summary") | |
| gr.Markdown( | |
| "Summary generation should take approximately 1-2 minutes for most settings." | |
| ) | |
| 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" | |
| ) | |
| gr.Markdown("---") | |
| with gr.Column(): | |
| gr.Markdown("### Advanced Settings") | |
| with gr.Row(): | |
| 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(): | |
| 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( | |
| "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) 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( | |
| "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries." | |
| ) | |
| gr.Markdown( | |
| "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial." | |
| ) | |
| 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) | |