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 for Groq API

# App config
st.set_page_config(page_title="SMEHelpBot 🤖", layout="wide")
st.title("🤖 SMEHelpBot – Your AI Assistant for Small Businesses")

# API key setup
GROQ_API_KEY = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY") or "your_groq_api_key_here"
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 based on the document:")

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

    loader = PyPDFLoader("temp.pdf")
    documents = loader.load()

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

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

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

    # Retrieval QA
    qa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True
    )

    if user_question:
        with st.spinner("Generating answer..."):
            result = qa({"query": user_question})
            st.success(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 enter a question to get started.")