app.py

import fitz
from transformers import pipeline
import gradio as gr


def extract_text_from_pdf(pdf_content):
    with fitz.open("temp.pdf", pdf_content) as doc:
        text = ""
        for page_num in range(doc.page_count):
            page = doc[page_num]
            text += page.get_text()
    return text


def generate_summary(file_content, user_input_text):
    if file_content:
        if isinstance(file_content, bytes):
            input_text = extract_text_from_pdf(file_content)
        else:
            input_text = file_content.read().decode("utf-8")
    else:
        input_text = user_input_text
    text_generator = pipeline("text2text-generation", model="google/flan-t5-base")

    summary = text_generator(input_text, max_length=1024, num_beams=4)

    return {
        "Extracted Information": input_text,
        "Book Summary": summary[0]["generated_text"],
        "Review": "The book conveys a powerful message about...",
    }


iface = gr.Interface(
    fn=generate_summary,
    inputs=[gr.File(label="Upload File"), gr.Textbox(label="Enter Text")],
    outputs=[
        gr.Textbox(label="Extracted Information"),
        gr.Textbox(label="Book Summary"),
        gr.Textbox(label="Rewiew"),
    ],
    live=True,
)
iface.launch()