app.py

import gradio as gr
import pandas as pd
import yfinance as yf
from utils import (
    calculate_technical_indicators,
    generate_trading_signals,
    get_fundamental_data,
    predict_prices,
    create_price_chart,
    create_technical_chart,
    create_prediction_chart,
)
import warnings

warnings.filterwarnings("ignore")

def analyze_stock(symbol, prediction_days=30):
    try:
        if not symbol.strip():
            raise ValueError("Please enter a valid stock symbol.")

        if not symbol.endswith(".JK"):
            symbol = symbol.upper() + ".JK"

        stock = yf.Ticker(symbol)
        data = stock.history(period="6mo", interval="1d")

        if data.empty:
            raise ValueError("No price data available for this stock.")

        indicators = calculate_technical_indicators(data)
        signals = generate_trading_signals(data, indicators)
        fundamental_info = get_fundamental_data(stock)
        predictions = predict_prices(data, prediction_days=prediction_days)
        fig_price = create_price_chart(data, indicators)
        fig_technical = create_technical_chart(data, indicators)
        fig_prediction = create_prediction_chart(data, predictions)

        return fundamental_info, indicators, signals, fig_price, fig_technical, fig_prediction, predictions
    except Exception as e:
        print(f"Error analyzing {symbol}: {e}")
        empty_fig = gr.Plot.update(value=None)
        empty_predictions = {
            'high_30d': 0, 'low_30d': 0, 'change_pct': 0, 'summary': 'Prediction unavailable.'
        }
        return {}, {}, {}, empty_fig, empty_fig, empty_fig, empty_predictions

def update_analysis(symbol, prediction_days):
    (
        fundamental_info,
        indicators,
        signals,
        fig_price,
        fig_technical,
        fig_prediction,
        predictions,
    ) = analyze_stock(symbol, prediction_days)

    if not fundamental_info:
        return (
            "❌ Unable to fetch stock data.",
            gr.Plot.update(value=None),
            gr.Plot.update(value=None),
            gr.Plot.update(value=None),
            "",
            "0", "0", "0", ""
        )

    summary_text = f"""
### 🏢 {fundamental_info.get('name', 'N/A')} ({symbol.upper()})
**Current Price:** Rp{fundamental_info.get('current_price', 0):,.2f}  
**Market Cap:** {fundamental_info.get('market_cap', 0):,}  
**P/E Ratio:** {fundamental_info.get('pe_ratio', 0):.2f}  
**Dividend Yield:** {fundamental_info.get('dividend_yield', 0):.2f}%  
**Volume:** {fundamental_info.get('volume', 0):,}  
"""

    signal_text = f"""
### 📈 Technical Trading Signal
**Overall Signal:** {signals.get('overall', 'N/A')}  
**Strength:** {signals.get('strength', 0):.2f}%  
**Support:** {signals.get('support', 0):,.2f}  
**Resistance:** {signals.get('resistance', 0):,.2f}  
**Stop Loss:** {signals.get('stop_loss', 0):,.2f}  

**Signal Details:**
{signals.get('details', '')}
"""

    prediction_text = f"""
### 🤖 AI Forecast (Amazon Chronos-Bolt)
**Predicted High (30d):** Rp{predictions.get('high_30d', 0):,.2f}  
**Predicted Low (30d):** Rp{predictions.get('low_30d', 0):,.2f}  
**Expected Change:** {predictions.get('change_pct', 0):.2f}%  

🧠 **Model Insight:**  
{predictions.get('summary', 'No analysis available')}
"""

    return (
        summary_text,
        fig_price,
        fig_technical,
        fig_prediction,
        signal_text,
        f"{predictions.get('high_30d', 0):,.2f}",
        f"{predictions.get('low_30d', 0):,.2f}",
        f"{predictions.get('change_pct', 0):.2f}%",
        prediction_text,
    )

with gr.Blocks(
    title="AI Stock Forecast Dashboard",
    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray"),
    css="""
        #dashboard {padding: 20px; max-width: 1300px; margin: auto;}
        .gradio-container {font-family: 'Inter', sans-serif;}
        h1, h2, h3 {color: #1e293b;}
        .gr-button {font-weight: 600; border-radius: 8px;}
        .gr-markdown {background: #f8fafc; border-radius: 10px; padding: 15px;}
    """
) as app:
    gr.Markdown("# ⚡ AI Stock Analysis Dashboard — Chronos-Bolt Edition", elem_id="dashboard")
    gr.Markdown("Enter any **Indonesian stock ticker** (e.g., `BBCA`, `ADRO`, `TLKM`, `BMRI`) to get live market insights and AI-based 30-day forecasts.", elem_id="dashboard")

    with gr.Row():
        symbol = gr.Textbox(
            label="Stock Symbol",
            value="BBCA",
            placeholder="Type e.g. BBCA, ADRO, TLKM ...",
            interactive=True,
            lines=1
        )
        prediction_days = gr.Slider(
            label="Prediction Period (Days)",
            minimum=5,
            maximum=60,
            step=5,
            value=30,
            interactive=True,
        )
        analyze_button = gr.Button("🚀 Analyze Stock")

    gr.Markdown("---")

    with gr.Row():
        with gr.Column():
            fundamentals_output = gr.Markdown(label="Fundamentals")
        with gr.Column():
            signal_output = gr.Markdown(label="Trading Signals")

    gr.Markdown("---")

    with gr.Tab("📊 Charts Overview"):
        with gr.Row():
            price_chart = gr.Plot(label="Price & Moving Averages")
            technical_chart = gr.Plot(label="Technical Indicators")
        gr.Markdown("---")
        prediction_chart = gr.Plot(label="AI Forecast Projection")

    with gr.Tab("🤖 AI Forecast Results"):
        with gr.Row():
            predicted_high = gr.Textbox(label="Predicted High (30d)")
            predicted_low = gr.Textbox(label="Predicted Low (30d)")
            predicted_change = gr.Textbox(label="Expected Change (%)")
        gr.Markdown("---")
        prediction_summary = gr.Markdown(label="Prediction Analysis")

    analyze_button.click(
        fn=update_analysis,
        inputs=[symbol, prediction_days],
        outputs=[
            fundamentals_output,
            price_chart,
            technical_chart,
            prediction_chart,
            signal_output,
            predicted_high,
            predicted_low,
            predicted_change,
            prediction_summary,
        ],
    )

if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=True)