app.py

from flask import Flask, request, jsonify, render_template
import fitz  # PyMuPDF for PDF text extraction
import faiss  # FAISS for vector search
import numpy as np
from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient
from typing import List, Tuple

app = Flask(__name__)

# Default settings
class ChatConfig:
    MODEL = "google/gemma-3-27b-it"
    DEFAULT_SYSTEM_MSG = "You are an AI assistant answering only based on the uploaded PDF."
    DEFAULT_MAX_TOKENS = 512
    DEFAULT_TEMP = 0.3
    DEFAULT_TOP_P = 0.95

client = InferenceClient(ChatConfig.MODEL)
embed_model = SentenceTransformer("all-MiniLM-L6-v2")  # Lightweight embedding model
vector_dim = 384  # Embedding size
index = faiss.IndexFlatL2(vector_dim)  # FAISS index

documents = []  # Store extracted text

def extract_text_from_pdf(pdf_path):
    """Extracts text from PDF"""
    doc = fitz.open(pdf_path)
    text_chunks = [page.get_text("text") for page in doc]
    return text_chunks

def create_vector_db(text_chunks):
    """Embeds text chunks and adds them to FAISS index"""
    global documents, index
    documents = text_chunks
    embeddings = embed_model.encode(text_chunks)
    index.add(np.array(embeddings, dtype=np.float32))

def search_relevant_text(query):
    """Finds the most relevant text chunk for the given query"""
    query_embedding = embed_model.encode([query])
    _, closest_idx = index.search(np.array(query_embedding, dtype=np.float32), k=3)
    return "\n".join([documents[i] for i in closest_idx[0]])

def generate_response(
    message: str,
    history: List[Tuple[str, str]],
    system_message: str = ChatConfig.DEFAULT_SYSTEM_MSG,
    max_tokens: int = ChatConfig.DEFAULT_MAX_TOKENS,
    temperature: float = ChatConfig.DEFAULT_TEMP,
    top_p: float = ChatConfig.DEFAULT_TOP_P
) -> str:
    if not documents:
        return "Please upload a PDF first."

    context = search_relevant_text(message)  # Get relevant content from PDF

    messages = [{"role": "system", "content": system_message}]
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})

    messages.append({"role": "user", "content": f"Context: {context}\nQuestion: {message}"})

    response = ""
    for chunk in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = chunk.choices[0].delta.content or ""
        response += token
    return response

@app.route('/')
def index():
    """Serve the HTML page for the user interface"""
    return render_template('index.html')

@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    """Handle PDF upload"""
    file = request.files['pdf']
    pdf_path = f"uploaded_files/{file.filename}"
    file.save(pdf_path)

    # Extract text and create vector database
    text_chunks = extract_text_from_pdf(pdf_path)
    create_vector_db(text_chunks)

    return jsonify({"message": "PDF uploaded and indexed successfully!"})

@app.route('/ask_question', methods=['POST'])
def ask_question():
    """Handle user question"""
    message = request.json.get('message')
    history = request.json.get('history', [])
    response = generate_response(message, history)
    return jsonify({"response": response})

if __name__ == '__main__':
    app.run(debug=True)