app.py

import os
import tempfile
import streamlit as st

from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_groq import GroqLLM

# --- Environment Variable Setup ---
GROQ_API_KEY = os.getenv("GROQ_API_KEY", "your-groq-api-key")
HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY", "your-huggingface-api-key")

# --- Groq LLM Initialization ---
llm = GroqLLM(
    api_key=GROQ_API_KEY,
    model="llama3-8b-8192",
    temperature=0.1
)

# --- HuggingFace Embeddings (add a default model name if needed) ---
embedding = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2",
    cache_folder="./hf_cache",
    huggingfacehub_api_token=HUGGINGFACE_API_KEY
)

# --- Streamlit UI ---
st.title("📄 RAG Chat with Groq + HuggingFace")

uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
user_query = st.text_input("Ask something about the document")
submit_button = st.button("Submit")

if uploaded_file and submit_button:
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
        tmp_file.write(uploaded_file.read())
        tmp_path = tmp_file.name

    # --- Load and Split PDF ---
    loader = PyPDFLoader(tmp_path)
    pages = loader.load_and_split()

    # --- FAISS Vector Store ---
    vectorstore = FAISS.from_documents(pages, embedding)
    retriever = vectorstore.as_retriever()

    # --- Optional Custom Prompt ---
    prompt_template = PromptTemplate(
        input_variables=["context", "question"],
        template="""
        You are an intelligent assistant. Use the following context to answer the question accurately.
        Context: {context}
        Question: {question}
        Answer:"""
    )

    # --- RetrievalQA Chain ---
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True,
        chain_type_kwargs={"prompt": prompt_template}
    )

    # --- Run the Chain ---
    result = qa_chain({"query": user_query})
    st.markdown("### 💬 Answer")
    st.write(result["result"])

    # --- Optional: Show Source Documents ---
    with st.expander("📄 Sources"):
        for i, doc in enumerate(result["source_documents"]):
            st.write(f"**Page {i+1}** — {doc.metadata.get('source', 'Unknown')}")