app.py

import os
import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain_community.vectorstores import Pinecone as LangchainPinecone
from langchain.chains import RetrievalQA
import pinecone  # OLD SDK (pinecone-client==2.2.4)

INDEX_NAME = "rag-demo-index"

def process_rag(api_key_gemini, api_key_pinecone, pinecone_env, pdf_file, user_question):
    if not api_key_gemini or not api_key_pinecone:
        return "❌ Please provide both Gemini and Pinecone API keys."
    
    if not pdf_file:
        return "❌ Please upload a PDF file."

    try:
        # Step 1: Load and split the PDF
        loader = PyPDFLoader(pdf_file.name)
        documents = loader.load()
        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        docs = splitter.split_documents(documents)

        # Step 2: Set up embeddings using Gemini
        embeddings = GoogleGenerativeAIEmbeddings(
            model="models/embedding-001", 
            google_api_key=api_key_gemini
        )

        # Step 3: Initialize Pinecone (old SDK)
        pinecone.init(api_key=api_key_pinecone, environment=pinecone_env)

        if INDEX_NAME not in pinecone.list_indexes():
            pinecone.create_index(name=INDEX_NAME, dimension=768, metric="cosine")

        # Step 4: Store docs in Pinecone using LangChain wrapper
        vectordb = LangchainPinecone.from_documents(
            docs, 
            embedding=embeddings, 
            index_name=INDEX_NAME
        )

        # Step 5: Create retriever and chain
        retriever = vectordb.as_retriever()
        llm = ChatGoogleGenerativeAI(
            model="gemini-pro", 
            google_api_key=api_key_gemini,
            temperature=0
        )
        qa_chain = RetrievalQA.from_chain_type(
            llm=llm, 
            retriever=retriever, 
            return_source_documents=True
        )

        # Step 6: Ask question
        result = qa_chain({"query": user_question})
        return result["result"]

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

# 🎛️ Gradio UI
with gr.Blocks() as app:
    gr.Markdown("## 📄🔍 PDF Q&A using Pinecone + Gemini (RAG)")

    with gr.Row():
        gemini_key = gr.Textbox(label="🔐 Gemini API Key", type="password")
        pinecone_key = gr.Textbox(label="🌲 Pinecone API Key", type="password")
        pinecone_env = gr.Textbox(label="🌍 Pinecone Environment (e.g., us-east-1)")

    pdf_file = gr.File(label="📄 Upload PDF", file_types=[".pdf"])
    user_question = gr.Textbox(label="❓ Ask your question")
    answer_output = gr.Textbox(label="🤖 Gemini Answer", lines=10)

    submit_btn = gr.Button("🔍 Ask")

    submit_btn.click(
        fn=process_rag,
        inputs=[gemini_key, pinecone_key, pinecone_env, pdf_file, user_question],
        outputs=answer_output
    )

app.launch()