import fitz
import gradio as gr
import re
from transformers import pipeline

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

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)
    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 regarding the content of the PDF."
).launch(debug=True, share=True)