import streamlit as st
import os
import logging
from io import BytesIO
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_community.llms import HuggingFaceHub
from transformers import pipeline  # For fallback if Hub fails

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Check API token
if "HUGGINGFACEHUB_API_TOKEN" not in os.environ:
    st.error("HUGGINGFACEHUB_API_TOKEN not set in secrets. Add it in Space settings.")
    st.stop()

try:
    # Function to process PDF
    def process_pdf(uploaded_file):
        try:
            logger.info("Starting PDF processing")
            pdf_reader = PdfReader(BytesIO(uploaded_file.getvalue()))
            text = ""
            for page in pdf_reader.pages:
                extracted = page.extract_text()
                if extracted:
                    text += extracted + "\n"
            
            if not text:
                raise ValueError("No text extracted from PDF.")
            
            # Chunk text (increased overlap for better context)
            text_splitter = CharacterTextSplitter(separator="\n", chunk_size=800, chunk_overlap=200, length_function=len)
            chunks = text_splitter.split_text(text)
            
            # Embeddings (light model)
            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
            
            # Vector store
            vector_store = FAISS.from_texts(chunks, embedding=embeddings)
            logger.info("PDF processed successfully")
            return vector_store
        except Exception as e:
            logger.error(f"PDF processing error: {str(e)}")
            st.error(f"Error processing PDF: {str(e)}")
            return None

    # Function to answer questions
    def answer_question(vector_store, query):
        try:
            logger.info(f"Answering query: {query}")
            # Lighter LLM via pipeline for faster CPU inference
            qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
            
            # Retrieve top chunks
            docs = vector_store.similarity_search(query, k=3)
            context = "\n".join([doc.page_content for doc in docs])
            
            # Prompt
            prompt = f"Use this context to answer concisely: {context}\nQuestion: {query}\nAnswer:"
            response = qa_pipeline(prompt, max_length=256, num_return_sequences=1)[0]['generated_text']
            
            logger.info("Answer generated")
            return response.strip()
        except Exception as e:
            logger.error(f"Answer generation error: {str(e)}")
            st.error(f"Error answering: {str(e)}")
            return "Unable to generate answer."

    # Streamlit UI with chat history
    st.title("Smart PDF Q&A")
    st.write("Upload a PDF and ask questions! Chat history is preserved.")

    # Initialize session state
    if "messages" not in st.session_state:
        st.session_state.messages = []
    if "vector_store" not in st.session_state:
        st.session_state.vector_store = None

    # PDF upload and process
    uploaded_file = st.file_uploader("Upload PDF", type="pdf")
    if uploaded_file:
        if st.button("Process PDF"):
            with st.spinner("Processing..."):
                vector_store = process_pdf(uploaded_file)
                if vector_store:
                    st.session_state.vector_store = vector_store
                    st.success("PDF ready! Ask away.")
                    st.session_state.messages = []  # Reset chat on new PDF

    # Display chat history
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Question input
    if st.session_state.vector_store:
        if prompt := st.chat_input("Ask a question:"):
            # Add user message
            st.session_state.messages.append({"role": "user", "content": prompt})
            with st.chat_message("user"):
                st.markdown(prompt)
            
            # Generate answer
            with st.chat_message("assistant"):
                with st.spinner("Thinking..."):
                    answer = answer_question(st.session_state.vector_store, prompt)
                st.markdown(answer)
            st.session_state.messages.append({"role": "assistant", "content": answer})

except Exception as e:
    logger.error(f"App initialization failed: {str(e)}")
    st.error(f"Initialization error: {str(e)}. Check logs or try factory reset.")