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import logging
from pathlib import Path
import os
import re
import gradio as gr
import nltk
import torch
from cleantext import clean
from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
_here = Path(__file__).parent
nltk.download("stopwords") # TODO=find where this requirement originates from
import transformers
transformers.logging.set_verbosity_error()
logging.basicConfig()
def truncate_word_count(text, max_words=512):
"""
truncate_word_count - a helper function for the gradio module
Parameters
----------
text : str, required, the text to be processed
max_words : int, optional, the maximum number of words, default=512
Returns
-------
dict, the text and whether it was truncated
"""
# split on whitespace with regex
words = re.split(r"\s+", text)
processed = {}
if len(words) > max_words:
processed["was_truncated"] = True
processed["truncated_text"] = " ".join(words[:max_words])
else:
processed["was_truncated"] = False
processed["truncated_text"] = text
return processed
def proc_submission(
input_text: str,
num_beams,
length_penalty,
repetition_penalty,
no_repeat_ngram_size,
token_batch_length,
max_input_length: int = 512,
):
"""
proc_submission - a helper function for the gradio module
Parameters
----------
input_text : str, required, the text to be processed
max_input_length : int, optional, the maximum length of the input text, default=512
Returns
-------
str of HTML, the interactive HTML form for the model
"""
settings = {
"length_penalty": length_penalty,
"repetition_penalty": repetition_penalty,
"no_repeat_ngram_size": no_repeat_ngram_size,
"encoder_no_repeat_ngram_size": 4,
"num_beams": num_beams,
}
history = {}
clean_text = clean(input_text, lower=False)
processed = truncate_word_count(clean_text, max_input_length)
if processed["was_truncated"]:
history["input_text"] = processed["truncated_text"]
history["was_truncated"] = True
msg = f"Input text was truncated to {max_input_length} characters."
logging.warning(msg)
history["WARNING"] = msg
else:
history["input_text"] = input_text
history["was_truncated"] = False
_summaries = summarize_via_tokenbatches(
history["input_text"],
model,
tokenizer,
batch_length=token_batch_length,
**settings,
)
sum_text = [s["summary"][0] for s in _summaries]
sum_scores = [f"\n - {round(s['summary_score'],4)}" for s in _summaries]
history["Input"] = input_text
history["Summary Text"] = "\n\t".join(sum_text)
history["Summary Scores"] = "\n".join(sum_scores)
html = ""
for name, item in history.items():
html += (
f"<h2>{name}:</h2><hr><b>{item}</b><br><br>"
if "summary" not in name.lower()
else f"<h2>{name}:</h2><hr><b>{item}</b>"
)
html += ""
return html
def load_examples(examples_dir="examples"):
src = _here / examples_dir
src.mkdir(exist_ok=True)
examples = [f for f in src.glob("*.txt")]
# load the examples into a list
text_examples = []
for example in examples:
with open(example, "r") as f:
text = f.read()
text_examples.append([text, 4, 0.7, 2048, 3.5, 3])
return text_examples
if __name__ == "__main__":
model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
title = "Long-form text summarization with LED on the BookSumm dataset"
description = (
"This is a simple example of using the LED model to summarize a long-form text."
)
gr.Interface(
proc_submission,
inputs=[
gr.inputs.Textbox(lines=10, label="input text"),
gr.inputs.Slider(
minimum=4, maximum=10, label="num_beams", default=4, step=1
),
gr.inputs.Slider(
minimum=512, maximum=4096, label="token_batch_length", default=2048, step=512,
),
gr.inputs.Slider(
minimum=0.5, maximum=1.1, label="length_penalty", default=0.7, step=0.05
),
gr.inputs.Slider(
minimum=1.0,
maximum=5.0,
label="repetition_penalty",
default=3.5,
step=0.1,
),
gr.inputs.Slider(
minimum=2, maximum=4, label="no_repeat_ngram_size", default=3, step=1
),
],
outputs="html",
examples_per_page=4,
title=title,
description=description,
examples=load_examples(),
).launch(enable_queue=True, share=True)
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