import gradio as gr
from datetime import datetime

from polygon_loader import fetch_ohlcv

# Import AI agents
from trend_agent import TrendWatcherAgent
from volume_agent import VolumeSurgeAgent
from crossover_agent import CrossoverAgent
from rsi_agent import RSIAlertAgent
from supertrend_agent import SupertrendAgent
from retracement_agent import RetracementAgent
from rainbow_agent import RainbowAgent
from sentiment_agent import SentimentAgent
from adaptive_agent import AdaptiveAgent


def predict_all(symbol):
    symbol = symbol.strip().upper()
    df = fetch_ohlcv(symbol)

    if df is None or df.empty:
        return [], [], "❌ No data available. Try again or check the symbol."

    try:
        # Run agents
        trend_agent = TrendWatcherAgent()
        vol_agent = VolumeSurgeAgent()
        cross_agent = CrossoverAgent()
        rsi_agent = RSIAlertAgent()
        super_agent = SupertrendAgent()
        retrace_agent = RetracementAgent()
        rainbow_agent = RainbowAgent()
        sentiment_agent = SentimentAgent()
        adaptive_agent = AdaptiveAgent()

        trend = trend_agent.run(df)
        vol = vol_agent.run(df)
        cross = cross_agent.run(df)
        rsi = rsi_agent.run(df)
        st = super_agent.run(df)
        retrace = retrace_agent.run(df)
        rainbow = rainbow_agent.run(df)
        sentiment = sentiment_agent.run(symbol)
        adaptive = adaptive_agent.run(df)

        # Prices and rounding
        entry_price = float(round(df["Close"].iloc[-1], 2))
        sl = float(round(entry_price * 0.985, 2))
        target = float(round(entry_price * 1.02, 2))
        now_time = datetime.now().strftime("%Y-%m-%d %I:%M%p").lower()
        confidence_str = f"{float(trend['confidence'])}%"

        # Explanation map
        Signal_Explanation__c = {
            "Uptrend": "EMA50↑, RSI above 55, supported by volume",
            "Downtrend": "EMA50↓, RSI below 45, bearish confirmation",
            "Neutral": "Indicators are mixed or non-directional"
        }
        explanation = Signal_Explanation__c.get(trend["trend"], "No explanation available.")

        # Live signal dashboard
        signal_table = [[
            symbol,
            trend['trend'].replace("trend", ""),
            entry_price,
            sl,
            target,
            confidence_str,
            now_time
        ]]

        # Detail panel (markdown)
        signal_md = f"""
[Stock: {symbol}]  
**Trend:** {trend['trend']} | **Confidence:** {confidence_str}  
→ **Entry:** {entry_price} | **SL:** {sl} | 🎯 **Target:** {target}

**[Why This Signal?]**  
→ {explanation}  
→ Indicators: EMA50 {'↑' if cross['crossover'] == 'Bullish' else '↓' if cross['crossover'] == 'Bearish' else '-'}, RSI={rsi['rsi']}, Volume: {"Spike" if vol['volume_surge'] else "Normal"}  
→ Historical Similar Case: April 7, 5min TF
"""

        # Portfolio view
        portfolio_table = [[
            symbol,
            "Up" if trend['trend'] == "Uptrend" else "Down",
            "Neutral",
            trend["trend"],
            confidence_str,
            "Generated by AI Agents"
        ]]

        return signal_table, portfolio_table, signal_md

    except Exception as e:
        return [], [], f"❌ Agent Error: {str(e)}"


# Gradio UI
with gr.Blocks(title="📈 Intraday AI Signal Engine") as app:
    gr.Markdown("## 🧠 Intraday Trading Signal – Multi-Agent AI Engine")

    with gr.Row():
        symbol_input = gr.Textbox(label="Enter Stock Symbol (e.g., AAPL, MSFT, INFY)", placeholder="e.g., AAPL")
        scan_btn = gr.Button("🔍 Run AI Agents")

    with gr.Column():
        signal_table = gr.Dataframe(
            headers=["Symbol", "Trend", "Entry", "SL", "Target", "Confidence", "Updated"],
            label="📊 Live Signal Dashboard",
            interactive=False
        )

        signal_md = gr.Markdown("ℹ️ Signal details will appear here...")

        portfolio_table = gr.Dataframe(
            headers=["Stock", "1H Trend", "4H Trend", "Daily Trend", "Confidence", "Comment"],
            label="🧠 Portfolio View (Multi-Timeframe)",
            interactive=False
        )

    scan_btn.click(
        fn=predict_all,
        inputs=[symbol_input],
        outputs=[signal_table, portfolio_table, signal_md]
    )

app.launch()