app.py

import os
import gradio as gr
import tempfile
from typing import List, Optional
import shutil
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.llms.base import LLM
from groq import Groq

# ---- Custom GroqLLM class using LangChain LLM base ----
class GroqLLM(LLM):
    model: str = "llama3-8b-8192"
    api_key: str = os.environ.get("GROQ_API_KEY")  # Load from HF secrets
    temperature: float = 0.7

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        client = Groq(api_key=self.api_key)
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
        response = client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=self.temperature,
        )
        return response.choices[0].message.content

    @property
    def _llm_type(self) -> str:
        return "groq-llm"

# Global cache for vectorstore
rag_context = {"retriever": None}

# ---- Step 1: Upload & Embed PDF ----
def process_pdf(file):
    if file is None:
        return "❌ Please upload a PDF."

    # Save uploaded file to a real file path
    with tempfile.TemporaryDirectory() as temp_dir:
        # Gradio provides file path directly via file.name
        temp_pdf_path = os.path.join(temp_dir, "uploaded.pdf")
        shutil.copy(file.name, temp_pdf_path)

        # Load and split PDF
        try:
            loader = PyPDFLoader(temp_pdf_path)
            documents = loader.load()

            text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
            chunks = text_splitter.split_documents(documents)

            # Create embeddings
            embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
            vectorstore = Chroma.from_documents(chunks, embedding, persist_directory=temp_dir)
            vectorstore.persist()

            rag_context["retriever"] = vectorstore.as_retriever()
            return "✅ PDF processed and ready. Ask your questions!"

        except Exception as e:
            return f"❌ Failed to load PDF: {e}"

# ---- Step 2: Ask questions to the RAG chain ----
def ask_question(query):
    retriever = rag_context.get("retriever")
    if retriever is None:
        return "❌ Please upload and process a PDF first."

    llm = GroqLLM()
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        retriever=retriever,
        return_source_documents=True
    )

    result = qa_chain({"query": query})
    answer = result["result"]
    return f"### Answer:\n{answer}"

# ---- Gradio UI ----
with gr.Blocks() as demo:
    gr.Markdown("# 📚 RAG Chatbot with Groq & LangChain\nUpload a PDF, then ask questions about it!")

    with gr.Row():
        pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
        upload_btn = gr.Button("Process PDF")
    upload_status = gr.Textbox(label="Status", interactive=False)

    upload_btn.click(process_pdf, inputs=pdf_input, outputs=upload_status)

    query_input = gr.Textbox(label="Ask a question")
    ask_btn = gr.Button("Get Answer")
    answer_output = gr.Markdown()

    ask_btn.click(ask_question, inputs=query_input, outputs=answer_output)

demo.launch()