app.py

import gradio as gr
import yfinance as yf
from utils import (
    calculate_technical_indicators,
    generate_trading_signals,
    get_fundamental_data,
    create_price_chart,
    create_technical_chart,
    create_prediction_chart,
    predict_prices,
)
import numpy as np


def analyze_stock(symbol, mode, pred_days):
    try:
        stock = yf.Ticker(symbol)
        data = stock.history(period="1y")

        if data.empty:
            msg = f"No data found for {symbol}. Check the symbol or try adding .JK (e.g., ADRO.JK)"
            return (
                {"name": "N/A", "current_price": 0, "market_cap": 0, "pe_ratio": 0, "dividend_yield": 0, "volume": 0},
                {"overall": "N/A", "strength": 0, "support": 0, "resistance": 0, "stop_loss": 0, "details": msg},
                None, None, None, None
            )

        indicators = calculate_technical_indicators(data)
        signals = generate_trading_signals(data, indicators)
        fundamentals = get_fundamental_data(stock)

        fig_price = create_price_chart(data, indicators)
        fig_technical = create_technical_chart(data, indicators)

        if mode == "AI Prediction":
            prediction = predict_prices(data, prediction_days=pred_days)
            fig_prediction = create_prediction_chart(data, prediction)
            return fundamentals, signals, fig_price, fig_technical, fig_prediction, prediction
        else:
            return fundamentals, signals, fig_price, fig_technical, None, None

    except Exception as e:
        msg = f"Error analyzing {symbol}: {e}"
        return (
            {"name": "N/A", "current_price": 0, "market_cap": 0, "pe_ratio": 0, "dividend_yield": 0, "volume": 0},
            {"overall": "Error", "strength": 0, "support": 0, "resistance": 0, "stop_loss": 0, "details": msg},
            None, None, None, None
        )


def format_fundamental_output(f):
    return f"""
    <div class="card">
    <h3>COMPANY FUNDAMENTALS</h3>
    <p><b>Name:</b> {f['name']}</p>
    <p><b>Current Price:</b> Rp{f['current_price']:,.2f}</p>
    <p><b>Market Cap:</b> {f['market_cap']:,}</p>
    <p><b>P/E Ratio:</b> {f['pe_ratio']:.2f}</p>
    <p><b>Dividend Yield:</b> {f['dividend_yield']:.2f}%</p>
    <p><b>Volume:</b> {f['volume']:,}</p>
    </div>
    """


def format_signal_output(s):
    details = s.get("details", "")
    detail_list = details.split("\n") if details else []
    formatted_details = "<ul>" + "".join(f"<li>{d}</li>" for d in detail_list) + "</ul>"
    return f"""
    <div class="card">
    <h3>TECHNICAL SIGNAL SUMMARY</h3>
    <p><b>Overall Trend:</b> {s.get('overall','N/A')}</p>
    <p><b>Signal Strength:</b> {s.get('strength',0):.2f}%</p>
    <p><b>Support:</b> Rp{s.get('support',0):,.2f}</p>
    <p><b>Resistance:</b> Rp{s.get('resistance',0):,.2f}</p>
    <p><b>Stop Loss:</b> Rp{s.get('stop_loss',0):,.2f}</p>
    <h4>Detailed Signals:</h4>
    {formatted_details}
    </div>
    """


def format_ai_output(p):
    if p is None or not isinstance(p, dict) or "values" not in p or len(p["values"]) == 0:
        return """
        <div class="card">
        <h3>30-DAY AI FORECAST (CHRONOS-BOLT)</h3>
        <p>No AI prediction data available.</p>
        </div>
        """
    tp1 = p["mean_30d"] * 0.97
    tp2 = p["mean_30d"] * 1.02
    sl = p["low_30d"] * 0.95
    return f"""
    <div class="card">
    <h3>30-DAY AI FORECAST (CHRONOS-BOLT)</h3>
    <p><b>Predicted High:</b> Rp{p['high_30d']:,.2f}</p>
    <p><b>Predicted Low:</b> Rp{p['low_30d']:,.2f}</p>
    <p><b>Expected Change:</b> {p['change_pct']:.2f}%</p>
    <p><b>TP1:</b> Rp{tp1:,.2f}</p>
    <p><b>TP2:</b> Rp{tp2:,.2f}</p>
    <p><b>Stop Loss:</b> Rp{sl:,.2f}</p>
    <h4>Model Insight:</h4>
    <p style="font-size:13px;line-height:1.4;">{p['summary']}</p>
    </div>
    """


with gr.Blocks(css="""
    body { font-family: 'Inter', sans-serif; background-color: #f9fafc; color: #222; }
    .gradio-container { max-width: 1300px; margin: auto; }
    h1 { text-align:center; color:#003366; margin-bottom:20px; }
    h3 { color:#003366; margin-bottom:8px; }
    .card {
        background: #ffffff;
        border-radius: 10px;
        box-shadow: 0 2px 4px rgba(0,0,0,0.08);
        padding: 18px;
        margin: 5px;
        flex: 1;
        min-width: 0;
    }
    .row-flex {
        display: flex;
        flex-wrap: wrap;
        justify-content: space-between;
        gap: 10px;
    }
    ul { margin: 6px 0 0 20px; padding: 0; }
    li { margin-bottom: 4px; font-size: 14px; }
""") as demo:

    gr.HTML("<h1>STOCK ANALYSIS DASHBOARD</h1>")

    with gr.Row():
        stock_input = gr.Textbox(label="Enter Stock Symbol (e.g. BBCA.JK, ADRO.JK)", placeholder="Type stock symbol...")
        mode_input = gr.Radio(["Technical Analysis", "AI Prediction"], label="Select Analysis Mode", value="Technical Analysis")
        pred_days_input = gr.Slider(7, 60, value=30, step=1, label="Prediction Days (AI only)")
        analyze_button = gr.Button("Analyze", variant="primary")

    gr.HTML("<hr style='margin:20px 0;'>")

    with gr.Row(elem_classes="row-flex"):
        fundamentals_output = gr.HTML()
        signal_output = gr.HTML()
        ai_output = gr.HTML()

    gr.HTML("<hr style='margin:20px 0;'>")

    with gr.Row():
        chart_price = gr.Plot(label="Price Chart")
        chart_technical = gr.Plot(label="Technical Chart")
        chart_prediction = gr.Plot(label="AI Prediction Chart")

    def run_analysis(symbol, mode, pred_days):
        fundamentals, signals, fig_price, fig_technical, fig_prediction, prediction = analyze_stock(symbol, mode, pred_days)
        return (
            format_fundamental_output(fundamentals),
            format_signal_output(signals),
            format_ai_output(prediction) if mode == "AI Prediction" else "",
            fig_price,
            fig_technical,
            fig_prediction if mode == "AI Prediction" else None,
        )

    analyze_button.click(
        fn=run_analysis,
        inputs=[stock_input, mode_input, pred_days_input],
        outputs=[fundamentals_output, signal_output, ai_output, chart_price, chart_technical, chart_prediction]
    )

demo.launch(server_name="0.0.0.0", server_port=7860)