Spaces:
Running
Running
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) | |