import streamlit as st
import time
import os
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent))

from utils.config import config
from core.llm import send_to_ollama, send_to_hf
from core.session import session_manager

st.set_page_config(page_title="AI Life Coach", page_icon="🧠", layout="wide")

# Initialize session state
if "messages" not in st.session_state:
    st.session_state.messages = []

# Sidebar
with st.sidebar:
    st.title("AI Life Coach")
    st.markdown("Your personal AI-powered life development assistant")
    
    # Model selection
    model_options = {
        "Mistral 7B (Local)": "mistral:latest",
        "Llama 2 7B (Local)": "llama2:latest",
        "OpenChat 3.5 (Local)": "openchat:latest"
    }
    
    selected_model_name = st.selectbox(
        "Select Model",
        options=list(model_options.keys()),
        index=0
    )
    
    st.session_state.selected_model = model_options[selected_model_name]
    
    # Ollama URL input
    st.session_state.ngrok_url = st.text_input(
        "Ollama Server URL",
        value=st.session_state.get("ngrok_url", "http://localhost:11434"),
        help="Enter the URL to your Ollama server"
    )
    
    # Conversation history
    st.subheader("Conversation History")
    if st.button("Clear History"):
        st.session_state.messages = []
        st.success("History cleared!")

# Main chat interface
st.title("🧠 AI Life Coach")
st.markdown("Ask me anything about personal development, goal setting, or life advice!")

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

# Chat input and send button
col1, col2 = st.columns([4, 1])
with col1:
    user_input = st.text_input(
        "Your message...",
        key="user_message_input",
        placeholder="Type your message here...",
        label_visibility="collapsed"
    )

with col2:
    send_button = st.button("Send", key="send_message_button", use_container_width=True)

if send_button and user_input.strip():
    # Display user message
    with st.chat_message("user"):
        st.markdown(user_input)
    
    # Add user message to history
    st.session_state.messages.append({"role": "user", "content": user_input})
    
    # Get conversation history
    user_session = session_manager.get_session("default_user")
    conversation = user_session.get("conversation", [])
    conversation_history = conversation[-5:]  # Last 5 messages
    conversation_history.append({"role": "user", "content": user_input})
    
    # Send to backend
    with st.chat_message("assistant"):
        with st.spinner("AI Coach is thinking..."):
            ai_response = None
            backend_used = ""
            
            # Try Ollama first if not falling back
            if not config.use_fallback:
                ai_response = send_to_ollama(
                    user_input,
                    conversation_history,
                    st.session_state.ngrok_url,
                    st.session_state.selected_model
                )
                backend_used = "Ollama"
            
            # Fallback to Hugging Face
            if not ai_response and config.hf_token:
                ai_response = send_to_hf(user_input, conversation_history)
                backend_used = "Hugging Face"
            
            if ai_response:
                st.markdown(f"{ai_response}")
                
                # Update conversation history (stub – actual save will come later)
                conversation.append({"role": "user", "content": user_input})
                conversation.append({"role": "assistant", "content": ai_response})
                
                # Update session
                user_session["conversation"] = conversation
                session_manager.save_session("default_user", user_session)
                
                # Add assistant response to history
                st.session_state.messages.append({"role": "assistant", "content": ai_response})
            else:
                st.error("Failed to get response from both providers.")
    
    # Clear input by forcing rerun
    st.experimental_rerun()