app.py

import gradio as gr
import uuid
import sqlite3
import json
import re
import PyPDF2
import numpy as np
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
from sklearn.metrics.pairwise import cosine_similarity

# Local imports
from database1 import create_db
from first1 import pdf_query

from ans_generator1 import AnswerGenerator

import sqlite3, json
from q_generator1 import QGenerator
from transformers import pipeline
# Initialize models
qgen = QGenerator()
ansgen = AnswerGenerator()

# Load FLAN-T5 model
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base", use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
qa_model = pipeline("text2text-generation", model=model, tokenizer=tokenizer)


# ✅ Upload and process PDF
# ✅ Updated version – supports multiple PDF files
def upload_pdf(files):
    try:
        messages = []

        for file in files:
            filename = file.name
            token = str(uuid.uuid4())

            pdf_reader = PyPDF2.PdfReader(file)
            text = "".join([page.extract_text() or "" for page in pdf_reader.pages])
            chunks = [text[i:i + 500] for i in range(0, len(text), 500)]

            create_db(token, chunks, filename, text)
            messages.append(f"✅ Uploaded and stored: {filename} (Token: {token})")

        return "\n".join(messages)

    except Exception as e:
        return f"❌ Error: {str(e)}"



# Load QG and QA once
qgen = QGenerator()
qa_model = pipeline("text2text-generation", model="google/flan-t5-base")
def generate_qa(token):
    try:
        if not token:
            return "⚠️ Please provide a token."

        print("📥 Received Token:", token)

        # Load chunk_data using token
        with sqlite3.connect("my_database.db") as conn:
            cursor = conn.cursor()
            cursor.execute("SELECT chunk_data FROM token_data WHERE token_id = ?", (token,))
            row = cursor.fetchone()

        if not row:
            print("❌ No data found for token in DB.")
            return "❌ No data found for this token."

        chunks = json.loads(row[0])
        if not chunks:
            print("⚠️ Chunk data is empty.")
            return "⚠️ No content available in database for this PDF."

        qa_pairs = []

        for i, chunk in enumerate(chunks):
            print(f"\n🔹 Processing chunk {i+1}/{len(chunks)}")
            questions = qgen.generate(chunk)
            print(f"🧠 Questions generated: {questions}")

            if not questions:
                print("⚠️ No questions generated for this chunk.")
                continue

            for question in questions[:2]:  # Max 2 Qs per chunk
                prompt = f"Context: {chunk}\n\nQuestion: {question}\n\nAnswer:"
                print(f"➡️ Prompt:\n{prompt}")

                try:
                    result = qa_model(prompt, max_length=256, do_sample=False)
                    print(f"⬅️ Raw model output: {result}")

                    if isinstance(result, list) and "generated_text" in result[0]:
                        answer = result[0]["generated_text"].strip()
                    elif isinstance(result, dict) and "answer" in result:
                        answer = result["answer"].strip()
                    else:
                        answer = "N/A"

                    print(f"✅ Final Answer: {answer}")
                    qa_pairs.append(f"Q: {question}\nA: {answer}")

                except Exception as e:
                    print(f"❌ QA model failed: {e}")
                    continue

        if not qa_pairs:
            print("⚠️ No Q&A pairs generated.")
            return "⚠️ No Q&A pairs generated."

        print("✅ Final Q&A generated successfully.")
        return "\n\n".join(qa_pairs)

    except Exception as e:
        print(f"🔥 Exception in generate_qa(): {e}")
        return f"❌ Error: {str(e)}"


# ✅ Ask question using token (semantic similarity)
def ask_question(token, question):
    try:
        with sqlite3.connect("my_database.db") as conn:
            cursor = conn.cursor()
            cursor.execute("SELECT chunk_data FROM token_data WHERE token_id = ?", (token,))
            row = cursor.fetchone()

        if not row:
            return "❌ Token not found."

        chunks = json.loads(row[0])
        processor = pdf_query()
        model = processor.model

        clean_chunks = [re.sub(r'\s+', ' ', c.strip()) for c in chunks if c.strip()]
        if not clean_chunks:
            return "⚠️ No valid content found in PDF."

        chunk_embeddings = model.encode(clean_chunks)
        q_embedding = model.encode([question])
        scores = cosine_similarity(q_embedding, chunk_embeddings)[0]

        top_index = int(np.argmax(scores))
        top_score = float(scores[top_index])
        best_text = clean_chunks[top_index]

        return f"Q: {question}\nA: {best_text}\nScore: {round(top_score, 3)}"

    except Exception as e:
        return f"❌ Error: {str(e)}"






# ✅ Gradio UI
with gr.Blocks(theme="default") as demo:
    gr.Markdown(
        """
        <div style='text-align: center; padding: 1rem;'>
            <h1 style='color: #3b82f6;'>📄 AI-Powered PDF Q&A System</h1>
            <p style='font-size: 1.1rem;'>Upload your PDFs, generate smart questions, and get intelligent answers.</p>
        </div>
        """
    )

    with gr.Tab("📤 1. Upload PDF"):
        gr.Markdown("### 🗂 Upload a PDF File")
        file = gr.File(label="Upload one or more PDFs", file_types=[".pdf"], file_count="multiple")

        upload_out = gr.Textbox(label="Upload Result", interactive=False)
        file.change(fn=upload_pdf, inputs=file, outputs=upload_out)

    with gr.Blocks(title="PDF Q&A Generator") as demo:
        with gr.Tab("🧠 2. Generate Questions & Answers"):
            gr.Markdown("### 🤖 Generate Questions and Answers from Uploaded PDF")
            fname = gr.Textbox(label="📄 Enter Uploaded Filename", placeholder="example.pdf")
            output_box = gr.Textbox(label="📝 Generated Q&A", lines=15, interactive=False)
            gr.Button("🚀 Generate Q&A").click(fn=generate_qa, inputs=fname, outputs=output_box)

    with gr.Tab("❓ 3. Ask a Question"):
        gr.Markdown("### 💬 Ask a question based on uploaded PDF")
        token_box = gr.Textbox(label="Token ID", placeholder="e.g., 123e4567-e89b-12d3-a456...")
        question_box = gr.Textbox(label="Type your question", placeholder="What is the main topic discussed?")
        answer_result = gr.Textbox(label="Answer Output", lines=6, interactive=False)
        gr.Button("🎯 Get Answer").click(fn=ask_question, inputs=[token_box, question_box], outputs=answer_result)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)