app.py

import os
import streamlit as st
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq  # ✅ Correct import

# Set up Streamlit UI
st.set_page_config(page_title="SMEHelpBot 🤖", layout="wide")
st.title("🤖 SMEHelpBot – Your AI Assistant for Small Businesses")

# Set Groq API key (use .streamlit/secrets.toml or environment variable)
GROQ_API_KEY = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
if not GROQ_API_KEY:
    st.error("❌ Please set your GROQ_API_KEY in environment or .streamlit/secrets.toml")
    st.stop()

os.environ["GROQ_API_KEY"] = GROQ_API_KEY

# Upload PDF
uploaded_file = st.file_uploader("📄 Upload a PDF (e.g., SME policies, documents):", type=["pdf"])
user_question = st.text_input("💬 Ask a question about the uploaded document:")

if uploaded_file:
    with open("temp.pdf", "wb") as f:
        f.write(uploaded_file.read())

    # Load PDF and split into chunks
    loader = PyPDFLoader("temp.pdf")
    documents = loader.load()

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

    # Create vector store
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vectorstore = FAISS.from_documents(chunks, embeddings)
    retriever = vectorstore.as_retriever()

    # Load Groq LLaMA3
    llm = ChatGroq(temperature=0.3, model_name="llama3-8b-8192")

    # Set up RetrievalQA chain
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )

    # Handle user query
    if user_question:
        with st.spinner("🤔 Thinking..."):
            result = qa_chain({"query": user_question})
            st.success("✅ Answer:")
            st.write(result["result"])

            with st.expander("📄 Source Snippets"):
                for i, doc in enumerate(result["source_documents"]):
                    st.markdown(f"**Source {i+1}:**\n{doc.page_content[:300]}...")
else:
    st.info("📂 Upload a PDF and ask a question to get started.")