import streamlit as st
import streamlit.components.v1 as components
from huggingface_hub import InferenceClient

# Force layout optimization
st.set_page_config(page_title="SparkHelix AI", page_icon="🧬", layout="wide")

# Hide standard Streamlit header frameworks
st.markdown("""
<style>
    #MainMenu, footer, header {visibility: hidden;} 
    div.block-container {padding: 0rem !important; max-width: 100% !important;}
    [data-testid="stSidebar"] {background-color: #0d1017 !important;}
</style>
""", unsafe_allow_html=True)

# Initialize Session Logs
if "messages" not in st.session_state:
    st.session_state.messages = [{"role": "assistant", "content": "Hello! I am SparkHelix. How can I help you discover or build today?"}]

# =====================================================================
# 🎨 STREAMLINED COPILOT ARCHITECTURE
# =====================================================================

# Build clean container styles using native markdown components
st.title("🧬 SparkHelix Copilot")
st.caption("Next-gen companion agent connected to web engines and inference clusters.")
st.markdown("---")

# Render elements using clear blocks
for msg in st.session_state.messages:
    if msg["role"] == "assistant":
        with st.chat_message("assistant", avatar="🧬"):
            st.markdown(f"**SparkHelix**\n\n{msg['content']}")
    else:
        with st.chat_message("user"):
            st.markdown(msg['content'])

# Pinned Interactive Chat Box
user_input = st.chat_input("Ask SparkHelix anything...")

if user_input:
    st.session_state.messages.append({"role": "user", "content": user_input})
    
    try:
        client = InferenceClient()
        response = client.chat.completions.create(
            model="Qwen/Qwen2.5-7B-Instruct",
            messages=[{"role": "user", "content": user_input}],
            max_tokens=512,
            temperature=0.4
        )
        ai_reply = response.choices[0].message.content
        st.session_state.messages.append({"role": "assistant", "content": ai_reply})
    except Exception as e:
        st.session_state.messages.append({"role": "assistant", "content": f"System Alert: Routing congestion. Details: {e}"})
        
    st.rerun()