import streamlit as st
import tensorflow as tf
import pandas as pd
import numpy as np
from src.data_fetcher import DataFetcher
from src.processor import Processor
from src.strategy import get_market_regime

st.set_page_config(page_title="Alpha Predict", page_icon="🏹", layout="wide")

@st.cache_resource
def load_alpha_model():
    return tf.keras.models.load_model("models/gru_booster_model.keras")

@st.cache_resource
def load_processor():
    return Processor()

def main():
    st.title("🏹 Alpha Predict")
    st.markdown("---")
    
    with st.sidebar:
        st.header("📖 Strategy Logic")
        st.markdown("""
        **Objective:** Directional probability for the next session.
        - **🟢 GREEN (≥ 57.8%)**: 3x Leverage (SPXL/UPRO)
        - **🟡 YELLOW (53.0% - 57.7%)**: 1x Exposure (SPY/VOO)
        - **🔴 RED (< 53.0%)**: Cash (0x)
        """)
        st.divider()
        st.caption("Alpha Predict")

    fetcher = DataFetcher()
    processor = load_processor()
    model = load_alpha_model()

    if st.button("Generate Today's Signal", type="primary"):
        with st.spinner("🔭 Analyzing Market Nervous System..."):
            
            # 1. Fetch data (Fetch 60 days to allow for the 26-day MACD EMA dropping NaNs)
            market_df = fetcher.fetch_market_data(days=60)
            news_df = fetcher.fetch_market_news(days=45)

            st.warning(f"Earliest news fetched: {news_df['Date'].min()} | Total Headlines: {len(news_df)}")

            # 2. Process - Now unpacking 4 items (df_features gives us the history!)
            input_tensor, metrics, df_features, scored_news = processor.process(market_df, news_df)
            
            # 3. Predict
            prediction_prob = float(model.predict(input_tensor)[0][0])
            
            # 4. Get Strategy Regime
            regime = get_market_regime(prediction_prob)
            
            # 5. UI Metrics
            latest_vix = market_df['VIX'].iloc[-1]
            prev_vix = market_df['VIX'].iloc[-2]
            current_price = market_df['Close'].iloc[-1]
            
            col1, col2, col3 = st.columns(3)
            col1.metric("S&P 500 Baseline", f"${current_price:,.2f}")
            col2.metric("VIX (Fear Gauge)", f"{latest_vix:.2f}", 
                        delta=f"{latest_vix - prev_vix:+.2f} Fear", delta_color="inverse")
            col3.metric("FinBERT Sentiment", f"{metrics['Sent_Mean']:+.2f}", 
                        delta=f"{int(metrics['News_Volume'])} Headlines")

            with st.expander("📊 How to interpret these values?"):
                c1, c2 = st.columns(2)
                c1.markdown("**VIX:** <15 Calm, 15-25 Normal, >25 Panic.")
                c2.markdown("**Sentiment:** >+0.1 Bullish, ±0.1 Neutral, <-0.1 Bearish.")

            st.divider()

            # --- REGIME DISPLAY ---
            st.subheader(f"{regime['icon']} Current Regime: :{regime['color']}[{regime['zone']}]")
            
            res1, res2 = st.columns([1, 2])
            res1.metric("Bullish Probability", f"{prediction_prob:.2%}")
            res2.info(f"**Recommended Action:** {regime['action']}")

            # --- LOGIC BREAKDOWN ---
            st.write("### 🧠 Logic Breakdown (Last 30 Days)")
            
            e_col1, e_col2 = st.columns(2)

            with e_col1:
                st.write("**Volatility Regime (VIX)**")
                vix_trend = market_df['VIX'].tail(30)
                st.line_chart(vix_trend)
                
                vix_slope = vix_trend.iloc[-1] - vix_trend.iloc[0]
                if vix_slope > 2:
                    st.warning(f"⚠️ Volatility is **trending up** (+{vix_slope:.1f}pts). The AI sees rising instability.")
                elif vix_slope < -2:
                    st.success(f"✅ Volatility is **cooling off** ({vix_slope:.1f}pts). This supports the bullish case.")
                else:
                    st.info("⚖️ Volatility is sideways. The model is focused on other factors.")

            with e_col2:
                st.write("**Sentiment Momentum (FinBERT)**")
                
                # FIX: We now pull the historical trend from df_features!
                sent_trend = df_features['Sent_Mean'].tail(30)
                st.area_chart(sent_trend)
                
                avg_30d = sent_trend.mean()
                st.write(f"30-Day Avg Sentiment: **{avg_30d:+.2f}**")
                
                if metrics['Sent_Mean'] > avg_30d:
                    st.write("📈 Today's news is **stronger** than the monthly average.")
                else:
                    st.write("📉 Today's news is **weaker** than the monthly average.")

if __name__ == "__main__":
    main()