import streamlit as st
import pandas as pd
import json
@st.cache_resource
def get_run_engine():
    from main import run_engine
    return run_engine

run_engine = get_run_engine()

st.set_page_config(page_title="Portfolio Engine Dashboard", layout="wide")

st.title("Quantitative Portfolio Production Engine")

st.sidebar.header("Portfolio Inputs")
tickers_input = st.sidebar.text_input("Tickers (comma separated)", "SPY, TLT, GLD")
capital = st.sidebar.number_input("Capital ($)", value=100000.0)
risk = st.sidebar.slider("Risk Aversion (1=Aggressive, 10=Conservative)", min_value=1, max_value=10, value=5)
model = st.sidebar.selectbox("Return Model", options=[
    (1, "CAPM"),
    (2, "Black-Litterman"),
    (3, "James-Stein"),
    (4, "Fama-French"),
    (5, "ML Stacking/BSTS")
], format_func=lambda x: x[1])[0]

allocation_engine = st.sidebar.selectbox("Allocation Engine", options=[
    (1, "Mean-Variance"),
    (2, "Hierarchical Risk Parity")
], format_func=lambda x: x[1])[0]

st.sidebar.subheader("Advanced Settings")
monthly = st.sidebar.checkbox("Monthly Frequency", value=False)
tax = st.sidebar.checkbox("Enable Tax Calculation", value=False)
with_futures = st.sidebar.checkbox("Enable Futures Overlay", value=False)

import requests
import time

def get_risk_factor(risk_level: int) -> float:
    risk_map = {
        1: 0.1,  2: 0.5,  3: 1.0,  4: 2.0,  5: 3.0,
        6: 5.0,  7: 7.5,  8: 10.0, 9: 15.0, 10: 25.0
    }
    return risk_map.get(risk_level, 3.0)

if st.button("Run Optimization via API"):
    with st.spinner("Calling remote API to run global quantitative optimization..."):
        tickers_list = [t.strip() for t in tickers_input.split(",") if t.strip()]
        
        payload = {
            "tickers": tickers_list,
            "capital": float(capital),
            "risk_level": int(risk),
            "model": int(model),
            "engine": int(allocation_engine),
            "currency": "$",
            "days": 252,
            "current_weights": {}
        }
        
        try:
            import os
            api_key = os.getenv("API_KEY", "SECRET_API_KEY_123")
            headers = {"X-API-Key": api_key}
            res = requests.post("http://127.0.0.1:8080/run_optimization", json=payload, headers=headers, timeout=300)
            res.raise_for_status()
            st.success("Optimization complete! Connect to live feed below.")
        except Exception as e:
            st.error(f"Error during API call: {e}")

st.header("Live Risk Dashboard")
if st.button("Start Live Data Feed"):
    import websocket
    import json
    
    col1, col2, col3 = st.columns(3)
    pnl_metric = col1.empty()
    capital_metric = col2.empty()
    var_breach_metric = col3.empty()
    
    chart_holder = st.empty()
    positions_holder = st.empty()
    
    pnl_history = []
    breaches = 0
    var_limit = - (float(capital) * 0.01) # 1% 1-Day VaR limit for display
    
    try:
        ws = websocket.WebSocket()
        import os
        api_key = os.getenv("API_KEY", "SECRET_API_KEY_123")
        ws.connect(f"ws://127.0.0.1:8080/ws?api_key={api_key}")
        
        while True:
            data = json.loads(ws.recv())
            if data.get("type") == "live_update":
                pnl = data["pnl"]
                cap = data["capital"]
                prices = data["prices"]
                
                pnl_history.append(pnl)
                
                if pnl < var_limit:
                    breaches += 1
                    
                pnl_metric.metric("Live P&L", f"${pnl:,.2f}", delta=f"${pnl - (pnl_history[-2] if len(pnl_history) > 1 else 0):,.2f}")
                capital_metric.metric("Total Capital", f"${cap:,.2f}")
                var_breach_metric.metric("VaR Limit Breaches", breaches)
                
                chart_holder.line_chart(pnl_history)
                
                pdf = pd.DataFrame(list(prices.items()), columns=["Ticker", "Live Price ($)"])
                positions_holder.dataframe(pdf, use_container_width=True)
                
    except Exception as e:
        st.error(f"WebSocket disconnected or failed: {e}")