import os
import streamlit as st
import fitz  # PyMuPDF
import logging
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.document_loaders import TextLoader

# --- Configuration ---
st.set_page_config(page_title="📚 RAG PDF Chatbot", layout="wide")
st.title("📚 RAG-based PDF Chatbot")
device = "cpu"

# --- Logging ---
logging.basicConfig(level=logging.INFO)

# --- Load LLM ---
@st.cache_resource
def load_model():
    checkpoint = "MBZUAI/LaMini-T5-738M"
    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
    model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
    pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, max_length=1024, do_sample=True, temperature=0.3, top_k=50, top_p=0.95)
    return HuggingFacePipeline(pipeline=pipe)

# --- Extract PDF Text ---
def read_pdf(file):
    try:
        doc = fitz.open(stream=file.read(), filetype="pdf")
        text = ""
        for page in doc:
            text += page.get_text()
        return text.strip()
    except Exception as e:
        logging.error(f"Failed to extract text: {e}")
        return ""

# --- Process Answer ---
def process_answer(question, full_text):
    # Save the full_text to a temporary file
    with open("temp_text.txt", "w") as f:
        f.write(full_text)

    loader = TextLoader("temp_text.txt")
    docs = loader.load()

    # Chunk the documents with increased size and overlap
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=300)
    splits = text_splitter.split_documents(docs)

    # Load embeddings
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

    # Create Chroma in-memory vector store
    db = Chroma.from_documents(splits, embedding=embeddings)
    retriever = db.as_retriever()

    # Set up the model
    llm = load_model()

    # Create a custom prompt
    prompt_template = PromptTemplate.from_template("""
    You are a helpful assistant. Use the following context to answer the question as accurately and thoroughly as possible.

    Context: {context}

    Question: {question}

    Answer in detail:""")

    # Retrieval QA with custom prompt
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        retriever=retriever,
        chain_type="stuff",
        chain_type_kwargs={"prompt": prompt_template}
    )

    # Return the answer using the retrieval QA chain
    return qa_chain.run(question)

# --- UI Layout ---
with st.sidebar:
    st.header("📄 Upload PDF")
    uploaded_file = st.file_uploader("Choose a PDF", type=["pdf"])

# --- Main Interface ---
if uploaded_file:
    st.success(f"You uploaded: {uploaded_file.name}")
    full_text = read_pdf(uploaded_file)

    if full_text:
        st.subheader("📁 PDF Preview")
        with st.expander("View Extracted Text"):
            st.write(full_text[:3000] + ("..." if len(full_text) > 3000 else ""))

        st.subheader("💬 Ask a Question")
        user_question = st.text_input("Type your question about the PDF content")

        if user_question:
            with st.spinner("Thinking..."):
                answer = process_answer(user_question, full_text)
                st.markdown("### 🤖 Answer")
                st.write(answer)

        with st.sidebar:
            st.markdown("---")
            st.markdown("**💡 Suggestions:**")
            st.caption("Try: \"Summarize this document\" or \"What is the key idea?\"") 
        with st.expander("💡 Suggestions", expanded=True):
            st.markdown("""
            - "Summarize this document"
            - "Give a quick summary"
            - "What are the main points?"
            - "Explain this document in short"
            """)

    else:
        st.error("⚠️ No text could be extracted from the PDF. Try another file.")
else:
    st.info("Upload a PDF to begin.")