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from text_extractor import TextExtractor
from tqdm import tqdm
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
from transformers import pipeline
from mdutils.mdutils import MdUtils
from pathlib import Path
import gradio as gr
import fitz
import torch
import copy
import os
FILENAME = ""
preprocess = TextExtractor()
model_name = "sshleifer/distill-pegasus-cnn-16-4"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = PegasusTokenizer.from_pretrained(model_name, max_length=500)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
def summarize(slides):
generated_slides = copy.deepcopy(slides)
for page, contents in tqdm(generated_slides.items()):
for idx, (tag, content) in enumerate(contents):
if tag.startswith('p'):
try:
input = tokenizer(content, truncation=True, padding="longest", return_tensors="pt").to(device)
tensor = model.generate(**input)
summary = tokenizer.batch_decode(tensor, skip_special_tokens=True)[0]
contents[idx] = (tag, summary)
except Exception as e:
print(e)
print("Summarization Fails")
return generated_slides
def convert2markdown(generate_slides):
# save_path = f"tmp/{FILENAME}"
mdFile = MdUtils(file_name=FILENAME, title=f'{FILENAME} Presentation')
for k, v in generate_slides.items():
mdFile.new_paragraph('---')
for section in v:
tag = section[0]
content = section[1]
if tag.startswith('h'):
mdFile.new_header(level=int(tag[1]), title=content)
if tag == 'p':
contents = content.split('<n>')
for content in contents:
mdFile.new_paragraph(content)
mdFile.create_md_file()
return f"{FILENAME}.md"
def inference(document):
global FILENAME
doc = fitz.open(document)
FILENAME = Path(doc.name).stem
font_counts, styles = preprocess.get_font_info(doc, granularity=False)
size_tag = preprocess.get_font_tags(font_counts, styles)
texts = preprocess.assign_tags(doc, size_tag)
slides = preprocess.get_slides(texts)
generated_slides = summarize(slides)
markdown_path = convert2markdown(generated_slides)
with open(markdown_path, 'rt') as f:
markdown_str = f.read()
return markdown_str
with gr.Blocks() as demo:
inp = gr.File( file_types=['pdf'])
out = gr.Textbox(label="Markdown Content")
inference_btn = gr.Button("Summarized PDF")
inference_btn.click(fn=inference, inputs=inp, outputs=out, show_progress=True, api_name="summarize")
demo.launch() |