app.py

import os
import logging
import time
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_groq import ChatGroq

# --------------------------
# Load environment variables
# --------------------------
load_dotenv()

# --------------------------
# Logging configuration
# --------------------------
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

# --------------------------
# PDF text extraction
# --------------------------
def get_pdf_text(pdf_docs):
    text = ""
    page_count = 0
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        page_count += len(pdf_reader.pages)
        for page in pdf_reader.pages:
            extracted_text = page.extract_text()
            if extracted_text:
                text += extracted_text + "\n"
    return text, page_count

# --------------------------
# Text chunking
# --------------------------
def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    return text_splitter.split_text(text)

# --------------------------
# FAISS VectorStore creation
# --------------------------
def get_vectorstore(text_chunks):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)

# --------------------------
# Conversation chain
# --------------------------
def get_conversation_chain(vectorstore):
    llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0.5)
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    return ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )

# --------------------------
# Handle user input
# --------------------------
def handle_userinput(user_question):
    if st.session_state.conversation is not None:
        start_time = time.time()
        with st.spinner("🤖 Thinking..."):
            response = st.session_state.conversation({'question': user_question})
        elapsed_time = round(time.time() - start_time, 2)

        # Show response only (no chat history)
        st.markdown(f"**🤖 Bot:** {response['answer']}")
        st.info(f"⏱ Response Time: {elapsed_time}s | 📄 Words: {len(response['answer'].split())}")
    else:
        st.warning("⚠ Please process the documents first.")

# --------------------------
# Main Streamlit App
# --------------------------
def main():
    st.set_page_config(page_title="InfinaDocs Knowledge Sphere", page_icon="📚", layout="wide")
    st.title("📚 InfinaDocs Knowledge Sphere")
    st.markdown("Chat with your documents using **LLaMA 3.3** and **Groq AI**. 🚀")

    # Session state initialization
    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "pages_processed" not in st.session_state:
        st.session_state.pages_processed = 0

    # Sidebar - Upload PDFs
    with st.sidebar:
        st.header("📂 Upload & Process")
        pdf_docs = st.file_uploader("Upload PDFs", accept_multiple_files=True, type=["pdf"])
        if st.button("🚀 Process Documents"):
            if pdf_docs:
                with st.spinner("📖 Reading & Processing..."):
                    raw_text, page_count = get_pdf_text(pdf_docs)
                    st.session_state.pages_processed = page_count
                    if raw_text.strip():
                        text_chunks = get_text_chunks(raw_text)
                        vectorstore = get_vectorstore(text_chunks)
                        st.session_state.conversation = get_conversation_chain(vectorstore)
                        st.success(f"✅ {len(pdf_docs)} file(s) processed | 📄 {page_count} pages")
                    else:
                        st.error("No valid text found in PDFs.")
            else:
                st.warning("Please upload at least one PDF.")

    # Main Chat Section
    st.subheader("💬 Ask a Question")
    user_question = st.text_input("Type your question here...")
    if st.button("Submit Question"):
        if user_question.strip():
            handle_userinput(user_question)
        else:
            st.warning("Please enter a question before submitting.")

if __name__ == '__main__':
    main()