Update Gradio app with multiple files

app.py

@@ -35,7 +35,11 @@ def create_chart_analysis(ticker, interval):
         # Buat chart menggunakan MPLFINANCE (dikembalikan sebagai HTML)
         chart_html = create_mplfinance_chart(
             df, 
-            ticker=f'{ticker} ({interval})', 
+            ticker=f'{
+ticker
+} ({
+interval
+})', 
             predictions=predictions
         )
 
@@ -52,20 +56,35 @@ def create_chart_analysis(ticker, interval):
         # Create metrics display
         metrics = {
             "Ticker": ticker,
-            "Current Price": f"${current_price:.2f}",
+                        "Current Price": f"${current_price:.2f
+}",
             "Signal": signal.upper(),
-            "Confidence": f"{confidence:.1%}",
-            "Take Profit": f"${tp:.2f}" if tp else "N/A",
-            "Stop Loss": f"${sl:.2f}" if sl else "N/A",
-            "RSI": f"{df['RSI'].iloc[-1]:.1f}",
-            "MACD": f"{df['MACD'].iloc[-1]:.4f}",
-            "Volume": f"{df['Volume'].iloc[-1]:,.0f}"
+            "Confidence": f"{
+confidence:.1%
+}",
+            "Take Profit": f"${
+tp:.2f
+}" if tp else "N/A",
+            "Stop Loss": f"${
+sl:.2f
+}" if sl else "N/A",
+            "RSI": f"{
+df['RSI'].iloc[-1]:.1f
+}",
+            "MACD": f"{
+df['MACD'].iloc[-1]:.4f
+}",
+            "Volume": f"{
+df['Volume'].iloc[-1]:,.0f
+}"
         }
         
         return chart_html, metrics
         
     except Exception as e:
-        return f"Error creating chart: {str(e)}", None
+        return f"Error creating chart: {
+e
+}", None
 
 def analyze_sentiment(ticker):
     """Analyze gold/crypto market sentiment"""
@@ -77,19 +96,33 @@ def analyze_sentiment(ticker):
     fig = go.Figure(go.Indicator(
         mode="gauge+number+delta",
         value=sentiment_score,
-        domain={'x': [0, 1], 'y': [0, 1]},
-        title={'text': f"{ticker} Market Sentiment (Simulated)"},
-        delta={'reference': 0},
+        domain={
+'x': [0, 1], 'y': [0, 1]
+},
+        title={
+'text': f"{ticker
+} Market Sentiment (Simulated)"},
+        delta={
+'reference': 0
+},
         gauge={
-            'axis': {'range': [-1, 1]},
-            'bar': {'color': "#FFD700"},
+            'axis': {'range': [-1, 1]
+},
+            'bar': {
+'color': "#FFD700"
+},
             'steps': [
-                {'range': [-1, -0.5], 'color': "rgba(255,0,0,0.5)"},
-                {'range': [-0.5, 0.5], 'color': "rgba(100,100,100,0.3)"},
+                {
+'range': [-1, -0.5], 'color': "rgba(255,0,0,0.5)"
+},
+                {
+'range': [gauge_range[1] * 0.7, gauge_range[1]], 'color': "rgba(0,255,0,0.5)"
+},
                 {'range': [0.5, 1], 'color': "rgba(0,255,0,0.5)"}
             ],
             'threshold': {
-                'line': {'color': "black", 'width': 4},
+                'line': {'color': "black", 'width': 4
+},
                 'thickness': 0.75,
                 'value': 0
             }
@@ -132,12 +165,19 @@ def get_fundamentals(ticker):
         fig = go.Figure(go.Indicator(
             mode="gauge+number",
             value=gauge_value,
-            title={'text': gauge_title},
+            title={
+'text': gauge_title
+},
             gauge={
-                'axis': {'range': gauge_range},
-                'bar': {'color': "#FFD700"},
+                'axis': {'range': gauge_range
+},
+                'bar': {
+'color': "#FFD700"
+},
                 'steps': [
-                    {'range': [gauge_range[0], gauge_range[1] * 0.3], 'color': "rgba(255,0,0,0.5)"},
+                    {
+'range': [gauge_range[0], gauge_range[1] * 0.3], 'color': "rgba(255,0,0,0.5)"
+},
                     {'range': [gauge_range[1] * 0.3, gauge_range[1] * 0.7], 'color': "rgba(100,100,100,0.3)"},
                     {'range': [gauge_range[1] * 0.7, gauge_range[1]], 'color': "rgba(0,255,0,0.5)"}
                 ]
@@ -162,22 +202,40 @@ with gr.Blocks(
     theme=gr.themes.Default(primary_hue="yellow", secondary_hue="yellow"),
     title="Ultimate Market Analysis & Prediction",
     css="""
-        .gradio-container {background-color: #f0f4f9; color: black}
-        .gr-button-primary {background-color: #FFD700 !important; color: #000000 !important}
-        .gr-button-secondary {border-color: #FFD700 !important; color: #000000 !important}
-        .gr-tab button {color: black !important}
-        .gr-tab button.selected {background-color: #FFD700 !important; color: #000000 !important}
-        .gr-highlighted {background-color: #CCCCCC !important}
-        .anycoder-link {color: #FFD700 !important; text-decoration: none; font-weight: bold}
+        .gradio-container {
+background-color: #f0f4f9; color: black
+}
+        .gr-button-primary {
+background-color: #FFD700 !important; color: #000000 !important
+}
+        .gr-button-secondary {
+border-color: #FFD700 !important; color: #000000 !important
+}
+        .gr-tab button {
+color: black !important
+}
+        .gr-tab button.selected {
+background-color: #FFD700 !important; color: #000000 !important
+}
+        .gr-highlighted {
+background-color: #CCCCCC !important
+}
+        .anycoder-link {
+color: #FFD700 !important; text-decoration: none; font-weight: bold
+}
         .mpl-chart-container {
             border: 1px solid #CCCCCC; 
-            border-radius: 5px; 
-            overflow: hidden; 
-            background: white; 
-            width: 100%;
-        }
-        .chart-title {color: black !important;}
-        .metric-label {color: black !important;}
+                        border-radius: 5px; 
+                        overflow: hidden; 
+                        background: white; 
+                        width: 100%;
+}
+        .chart-title {
+color: black !important;
+}
+        .metric-label {
+color: black !important;
+}
     """
 ) as demo:
     

data_processor.py

@@ -113,7 +113,9 @@ class DataProcessor:
                     "Dominance Index": f"{np.random.uniform(40, 60):.2f}%",
                     "Fear & Greed Index": np.random.choice(["Extreme Fear", "Fear", "Neutral", "Greed", "Extreme Greed"]),
                     "Hash Rate Trend": np.random.choice(["Increasing", "Stable", "Decreasing"]),
-                    "Institutional Flow (Net)": f"{np.random.uniform(-100, 100):,.0f}M USD",
+                    "Institutional Flow (Net)": f"{
+np.random.uniform(-100, 100):,.0f
+}M USD",
                     "Market Sentiment": np.random.choice(["Bullish", "Neutral", "Bearish"]),
                 }
             else: # Default Gold (GC=F)
@@ -121,17 +123,27 @@ class DataProcessor:
                     "Gold Strength Index": round(np.random.uniform(30, 80), 1),
                     "Dollar Index (DXY)": round(np.random.uniform(90, 110), 1),
                     "Real Interest Rate": f"{np.random.uniform(-2, 5):.2f}%",
-                    "Gold Volatility": f"{np.random.uniform(10, 40):.1f}%",
-                    "Commercial Hedgers (Net)": f"{np.random.uniform(-50000, 50000):,.0f}",
-                    "Managed Money (Net)": f"{np.random.uniform(-100000, 100000):,.0f}",
+                    "Gold Volatility": f"{
+np.random.uniform(10, 40):.1f
+}%",
+                    "Commercial Hedgers (Net)": f"{
+np.random.uniform(-50000, 50000):,.0f
+}",
+                    "Managed Money (Net)": f"{
+np.random.uniform(-100000, 100000):,.0f
+}",
                     "Market Sentiment": np.random.choice(["Bullish", "Neutral", "Bearish"]),
                 }
             
             return fundamentals
             
         except Exception as e:
-            print(f"Error fetching fundamentals: {e}")
-            return {"Error": str(e)}
+            print(f"Error fetching fundamentals: {
+e
+}")
+            return {
+"Error": str(e)
+}
     
     # prepare_for_chronos tetap sama, tidak perlu perubahan
     def prepare_for_chronos(self, df, lookback=100):

requirements.txt

@@ -10,4 +10,4 @@ scikit-learn
 safetensors
 huggingface-hub
 chronos-forecasting
-mplfinance
+mplfinance

sentiment_analyzer.py

@@ -50,17 +50,25 @@ class SentimentAnalyzer:
             
             # Tampilan News (menggunakan background terang #E0E0E0 agar terlihat di tema putih)
             news_html = "<div style='max-height: 200px; overflow-y: auto; color: black;'>"
-            news_html += f"<h4 style='color: {title_color};'>Latest {ticker} News (Simulated)</h4>"
+            news_html += f"<h4 style='color: {
+title_color
+};'>Latest {
+ticker
+} News (Simulated)</h4>"
             
             for news in selected_news:
                 sentiment_label = "🟢" if "positive" in news or "rising" in news or "support" in news or "bullish" in news or "accumulation" in news else \
                                   "🔴" if "sell-off" in news or "weighs" in news or "outflows" in news or "Profit-taking" in news or "fear" in news else \
                                   "🟡"
-                news_html += f"<p style='margin: 10px 0; padding: 10px; background: #E0E0E0; border-radius: 5px; color: black;'>{sentiment_label} {news}</p>"
+                news_html += f"<p style='margin: 10px 0; padding: 10px; background: #E0E0E0; border-radius: 5px; color: black;'>{
+sentiment_label
+} {news}</p>"
             
             news_html += "</div>"
             
             return sentiment, news_html
             
         except Exception as e:
-            return 0, f"<p>Error analyzing sentiment: {str(e)}</p>"
+            return 0, f"<p>Error analyzing sentiment: {
+str(e)
+}</p>"

trading_logic.py

@@ -80,7 +80,9 @@ class TradingLogic:
             return signal, confidence
             
         except Exception as e:
-            print(f"Signal generation error: {e}")
+            print(f"Signal generation error: {
+e
+}")
             return "hold", 0.0
     
     def calculate_tp_sl(self, current_price, atr, signal):