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import fitz
import gradio as gr
import requests
import io
import re
from PIL import Image
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
qa_model = pipeline("question-answering", model="deepset/bert-large-uncased-whole-word-masking-squad2")
def extract_text_from_pdf(pdf_file):
with fitz.open(pdf_file) as pdf:
text = ""
for page in pdf:
text += page.get_text("text")
text = re.sub(r'\s+', ' ', text).strip()
return text
def summarize(text):
if len(text) > 1000:
chunks = [text[i:i+1000] for i in range(0, len(text), 1000)]
summary = ""
for chunk in chunks:
summary += summarizer(chunk, max_length=150, min_length=50, do_sample=False)[0]['summary_text'] + " "
else:
summary = summarizer(text, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
return summary
# API_URL = "https://api-inference.huggingface.co/models/deepset/bert-large-uncased-whole-word-masking-squad2"
# headers = {"Authorization": f"Bearer {my_key}"}
# def query(payload):
# response = requests.post(API_URL, headers=headers, json=payload)
# return response.content
def answer_question(text, question):
response = qa_model(question=question, context=text)
answer = response['answer']
return answer
def summarize_and_qa(pdf_file, question):
text = extract_text_from_pdf(pdf_file)
summary = summarize(text)
answer = answer_question(text, question)
# image_bytes = query({"inputs": answer})
# if image_bytes:
# try:
# image = Image.open(io.BytesIO(image_bytes))
# except Exception as e:
# return summary, answer, None
# else:
# image = None
return summary, answer
gr.Interface(
fn=summarize_and_qa,
inputs=["file", "text"],
outputs=["textbox", "textbox"],
title="Understand your PDF Better",
description="Upload a PDF to get a summary. You can ask any question regardging the content of the PDF. It will also generate a picture to help you better understand the content."
).launch(debug=True, share=True) |