Update app.py

app.py

@@ -1,230 +1,125 @@
 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,
+    predict_prices,
 )
-import warnings
-
-warnings.filterwarnings("ignore")
-
+import pandas as pd
 
-def analyze_stock(symbol, prediction_days=30):
+def analyze_stock(symbol, mode, pred_days):
     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")
-
+        data = stock.history(period="1y")
         if data.empty:
-            raise ValueError("No price data available for this stock.")
+            return "No data found", None, None, None
 
         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)
+        fundamentals = get_fundamental_data(stock)
 
         fig_price = create_price_chart(data, indicators)
         fig_technical = create_technical_chart(data, indicators)
-        fig_prediction = create_prediction_chart(data, predictions)
-
-        # kalkulasi TP1, TP2, SL
-        last_price = data['Close'].iloc[-1]
-        tp1 = last_price * (1 + (predictions.get("change_pct", 0) / 200))
-        tp2 = last_price * (1 + (predictions.get("change_pct", 0) / 100))
-        sl = last_price * 0.95
-
-        predictions["tp1"] = tp1
-        predictions["tp2"] = tp2
-        predictions["sl"] = sl
-
-        return fundamental_info, indicators, signals, fig_price, fig_technical, fig_prediction, predictions
 
+        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:
-        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.",
-            "No technical signals available.",
-            "No prediction data available.",
-            gr.Plot.update(value=None),
-            gr.Plot.update(value=None),
-            gr.Plot.update(value=None),
-        )
-
-    fundamentals = f"""
-    <h4>COMPANY FUNDAMENTALS</h4>
-    <b>Name:</b> {fundamental_info.get('name', 'N/A')} ({symbol.upper()})<br>
-    <b>Current Price:</b> Rp{fundamental_info.get('current_price', 0):,.2f}<br>
-    <b>Market Cap:</b> {fundamental_info.get('market_cap', 0):,}<br>
-    <b>P/E Ratio:</b> {fundamental_info.get('pe_ratio', 0):.2f}<br>
-    <b>Dividend Yield:</b> {fundamental_info.get('dividend_yield', 0):.2f}%<br>
-    <b>Volume:</b> {fundamental_info.get('volume', 0):,}<br>
+        return f"Error analyzing {symbol}: {e}", None, None, None, None, None
+
+
+def format_fundamental_output(f):
+    return f"""
+    <h3>COMPANY FUNDAMENTALS</h3>
+    <b>Name:</b> {f['name']}<br>
+    <b>Current Price:</b> Rp{f['current_price']:,.2f}<br>
+    <b>Market Cap:</b> {f['market_cap']:,}<br>
+    <b>P/E Ratio:</b> {f['pe_ratio']:.2f}<br>
+    <b>Dividend Yield:</b> {f['dividend_yield']:.2f}%<br>
+    <b>Volume:</b> {f['volume']:,}<br>
     """
 
-    details_list = "".join(
-        [f"<li>{line.strip()}</li>" for line in signals.get("details", "").split("\n") if line.strip()]
-    )
 
-    trading_signal = f"""
-    <h4>TECHNICAL SIGNAL SUMMARY</h4>
-    <b>Overall Trend:</b> {signals.get('overall', 'N/A')}<br>
-    <b>Signal Strength:</b> {signals.get('strength', 0):.2f}%<br>
-    <b>Support:</b> Rp{signals.get('support', 0):,.2f}<br>
-    <b>Resistance:</b> Rp{signals.get('resistance', 0):,.2f}<br>
-    <b>Stop Loss:</b> Rp{signals.get('stop_loss', 0):,.2f}<br><br>
-    <b>Detailed Signals:</b>
-    <ul style="margin-top: 8px; padding-left: 20px; line-height: 1.6;">
-    {details_list}
-    </ul>
+def format_signal_output(s):
+    details = s["details"].split("\n")
+    formatted_details = "<ul>" + "".join(f"<li>{d}</li>" for d in details) + "</ul>"
+    return f"""
+    <h3>TECHNICAL SIGNAL SUMMARY</h3>
+    <b>Overall Trend:</b> {s['overall']}<br>
+    <b>Signal Strength:</b> {s['strength']:.2f}%<br>
+    <b>Support:</b> Rp{s['support']:,.2f}<br>
+    <b>Resistance:</b> Rp{s['resistance']:,.2f}<br>
+    <b>Stop Loss:</b> Rp{s['stop_loss']:,.2f}<br>
+    <br><b>Detailed Signals:</b>{formatted_details}
     """
 
-    prediction = f"""
-    <h4>30-DAY AI FORECAST (CHRONOS-BOLT)</h4>
-    <b>Predicted High:</b> Rp{predictions.get('high_30d', 0):,.2f}<br>
-    <b>Predicted Low:</b> Rp{predictions.get('low_30d', 0):,.2f}<br>
-    <b>Expected Change:</b> {predictions.get('change_pct', 0):.2f}%<br><br>
-    <b>TP1:</b> Rp{predictions.get('tp1', 0):,.2f}<br>
-    <b>TP2:</b> Rp{predictions.get('tp2', 0):,.2f}<br>
-    <b>Stop Loss:</b> Rp{predictions.get('sl', 0):,.2f}<br><br>
-    <b>Model Insight:</b><br>{predictions.get('summary', 'No analysis available')}
-    """
 
-    return (
-        f"""
-        <div class='triple-panel'>
-            <div class='panel-box'>{fundamentals}</div>
-            <div class='panel-box'>{trading_signal}</div>
-            <div class='panel-box'>{prediction}</div>
-        </div>
-        """,
-        fig_price,
-        fig_technical,
-        fig_prediction,
-    )
+def format_ai_output(p):
+    if not p:
+        return ""
+    tp1 = p["mean_30d"] * 0.97
+    tp2 = p["mean_30d"] * 1.02
+    sl = p["low_30d"] * 0.95
+    return f"""
+    <h3>30-DAY AI FORECAST (CHRONOS-BOLT)</h3>
+    <b>Predicted High:</b> Rp{p['high_30d']:,.2f}<br>
+    <b>Predicted Low:</b> Rp{p['low_30d']:,.2f}<br>
+    <b>Expected Change:</b> {p['change_pct']:.2f}%<br><br>
+    <b>TP1:</b> Rp{tp1:,.2f}<br>
+    <b>TP2:</b> Rp{tp2:,.2f}<br>
+    <b>Stop Loss:</b> Rp{sl:,.2f}<br><br>
+    <b>Model Insight:</b><br>{p['summary']}
+    """
 
 
-with gr.Blocks(
-    title="REXPRO FINANCIAL AI DASHBOARD",
-    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray"),
-    css="""
-        @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
-        body {
-            background-color: #f9fafb;
-            color: #1e293b;
-            font-family: 'Inter', 'Segoe UI', Arial, sans-serif;
-        }
-        h1 {
-            color: #1e40af;
-            text-align: center;
-            font-weight: 700;
-            letter-spacing: 0.5px;
-        }
-        h2, h3, h4 {
-            color: #1e3a8a;
-            font-weight: 600;
-            margin-bottom: 6px;
-        }
-        .gr-button {
-            background-color: #2563eb !important;
-            color: white !important;
-            font-weight: 600;
-            border-radius: 8px;
-            padding: 10px 18px;
-        }
-        .panel-box {
-            background-color: #ffffff;
-            border-radius: 10px;
-            border: 1px solid #e2e8f0;
-            padding: 16px;
-            flex: 1;
-            min-width: 30%;
-        }
-        .triple-panel {
-            display: flex;
-            flex-direction: row;
-            justify-content: space-between;
-            gap: 16px;
-            width: 100%;
-        }
-        ul { margin: 0; padding: 0 0 0 18px; }
-        li { margin-bottom: 4px; }
-        .gr-plot {
-            background-color: #ffffff;
-            border: 1px solid #e2e8f0;
-            border-radius: 10px;
-        }
-    """,
-) as app:
-    gr.Markdown("# REXPRO FINANCIAL AI DASHBOARD")
-    gr.Markdown(
-        "Comprehensive stock analytics powered by **AI forecasting and technical analysis.**"
-    )
+with gr.Blocks(css="""
+    body { font-family: 'Inter', sans-serif; background-color: #f9fafc; color: #222; }
+    .gradio-container { max-width: 1200px; margin: auto; }
+    h3 { color: #003366; border-bottom: 2px solid #d3d3d3; padding-bottom: 5px; }
+    .panel-box { background: white; padding: 20px; border-radius: 10px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); }
+""") as demo:
+    gr.HTML("<h1 style='text-align:center;color:#003366;'>📈 STOCK ANALYSIS DASHBOARD</h1>")
 
     with gr.Row():
-        symbol = gr.Textbox(
-            label="STOCK SYMBOL (IDX)",
-            value="BBCA",
-            placeholder="Example: BBCA, TLKM, ADRO, BMRI",
-            interactive=True,
-        )
-        prediction_days = gr.Slider(
-            label="FORECAST PERIOD (DAYS)",
-            minimum=5,
-            maximum=60,
-            step=5,
-            value=30,
-            interactive=True,
-        )
-        analyze_button = gr.Button("RUN ANALYSIS")
+        stock_input = gr.Textbox(label="Enter Stock Symbol (e.g. BBCA.JK, ADRO.JK)", placeholder="Type your stock symbol here...")
+        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 (for AI mode only)")
+        analyze_button = gr.Button("Analyze")
 
-    gr.Markdown("---")
-    report_section = gr.HTML()
-    gr.Markdown("---")
+    with gr.Row():
+        fundamentals_output = gr.HTML()
+        signal_output = gr.HTML()
+        ai_output = gr.HTML()
 
-    with gr.Tab("MARKET CHARTS"):
-        with gr.Row():
-            price_chart = gr.Plot(label="PRICE & MOVING AVERAGES")
-            technical_chart = gr.Plot(label="TECHNICAL INDICATORS OVERVIEW")
-        gr.Markdown("---")
-        prediction_chart = gr.Plot(label="AI FORECAST PROJECTION")
+    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)
+        if isinstance(fundamentals, str):
+            return fundamentals, "", "", None, None, None
+        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=update_analysis,
-        inputs=[symbol, prediction_days],
-        outputs=[report_section, price_chart, technical_chart, prediction_chart],
+        fn=run_analysis,
+        inputs=[stock_input, mode_input, pred_days_input],
+        outputs=[fundamentals_output, signal_output, ai_output, chart_price, chart_technical, chart_prediction]
     )
 
-if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=True)
+demo.launch(server_name="0.0.0.0", server_port=7860)