import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Setup
st.set_page_config(page_title="Noor-e-Hidayat 🌙", layout="centered")
st.markdown("<h1 style='text-align: center;'>🌙 Noor-e-Hidayat – Islamic Chatbot</h1>", unsafe_allow_html=True)
st.markdown("Ask anything based on the Qur’an. This assistant replies gently, spiritually, and with reference.")

# Load model
model_id = "llm-soda/quran-qa-phi-2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Chat history in session
if "messages" not in st.session_state:
    st.session_state.messages = []

# Chat input box
user_input = st.chat_input("Type your question about Islam...")

if user_input:
    # Save user message
    st.session_state.messages.append({"role": "user", "content": user_input})

    # Generate bot reply
    with st.spinner("Answering with Qur’an wisdom..."):
        prompt = f"Answer the following Islamic question with Qur’an-based reasoning and reference:\nQuestion: {user_input}\nAnswer:"
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to("cuda" if torch.cuda.is_available() else "cpu")
        outputs = model.generate(**inputs, max_new_tokens=300)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        response = response.replace(prompt, "").strip()

    # Save bot message
    st.session_state.messages.append({"role": "bot", "content": response})

# Display chat messages like ChatGPT
for msg in st.session_state.messages:
    if msg["role"] == "user":
        with st.chat_message("user"):
            st.markdown(msg["content"])
    else:
        with st.chat_message("assistant"):
            st.markdown(msg["content"])