Update Gradio app with multiple files

app.py

@@ -46,34 +46,46 @@ def create_chart_analysis(interval, asset_name):
         # Prepare additional plots for indicators
         ap = []
         
-        # Add moving averages
-        ap.append(mpf.make_addplot(df['SMA_20'].iloc[-100:], color='#FFA500', width=1.5, label='SMA 20'))
-        ap.append(mpf.make_addplot(df['SMA_50'].iloc[-100:], color='#FF4500', width=1.5, label='SMA 50'))
+        # Add moving averages (last 100 data points)
+        if 'SMA_20' in df.columns:
+            ap.append(mpf.make_addplot(df['SMA_20'].iloc[-100:], color='#FFA500', width=1.5, label='SMA 20'))
+        if 'SMA_50' in df.columns:
+            ap.append(mpf.make_addplot(df['SMA_50'].iloc[-100:], color='#FF4500', width=1.5, label='SMA 50'))
         
         # Add Bollinger Bands
-        ap.append(mpf.make_addplot(df['BB_upper'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Upper'))
-        ap.append(mpf.make_addplot(df['BB_lower'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Lower'))
+        if 'BB_upper' in df.columns and 'BB_lower' in df.columns:
+            ap.append(mpf.make_addplot(df['BB_upper'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Upper'))
+            ap.append(mpf.make_addplot(df['BB_lower'].iloc[-100:], color='#4169E1', width=1, linestyle='dashed', label='BB Lower'))
         
         # Create figure
-        fig, axes = mpf.plot(
-            df[-100:],  # Show last 100 candles
-            type='candle',
-            style='yahoo',
-            title=f'{asset_name} - {interval}',
-            ylabel='Price (USD)',
-            volume=True,
-            addplot=ap,
-            figsize=(12, 8),
-            returnfig=True,
-            warn_too_much_data=200,
-            tight_layout=True
-        )
-        
-        # Adjust layout
-        fig.patch.set_facecolor('white')
-        if axes:
-            axes[0].set_facecolor('white')
-            axes[0].grid(True, alpha=0.3)
+        try:
+            fig, axes = mpf.plot(
+                df[-100:],  # Show last 100 candles
+                type='candle',
+                style='yahoo',
+                title=f'{asset_name} - {interval}',
+                ylabel='Price (USD)',
+                volume=True,
+                addplot=ap,
+                figsize=(12, 8),
+                returnfig=True,
+                warn_too_much_data=200,
+                tight_layout=True
+            )
+            
+            # Adjust layout
+            fig.patch.set_facecolor('white')
+            if axes:
+                axes[0].set_facecolor('white')
+                axes[0].grid(True, alpha=0.3)
+        except Exception as plot_error:
+            print(f"Mplfinance plot error: {plot_error}")
+            fig, axes = plt.subplots(figsize=(12, 8), facecolor='white')
+            fig.patch.set_facecolor('white')
+            axes.text(0.5, 0.5, f'Chart Plot Error: {str(plot_error)}', ha='center', va='center', 
+                     transform=axes.transAxes, fontsize=14, color='red')
+            axes.set_title('Plot Generation Error', color='black')
+            axes.axis('off')
         
         # Prepare data for Chronos
         prepared_data = data_processor.prepare_for_chronos(df)
@@ -89,7 +101,7 @@ def create_chart_analysis(interval, asset_name):
         
         # Calculate TP/SL
         tp, sl = trading_logic.calculate_tp_sl(
-            current_price, df['ATR'].iloc[-1], signal
+            current_price, df['ATR'].iloc[-1] if 'ATR' in df.columns else 10, signal
         )
         
         # Create metrics display
@@ -99,9 +111,9 @@ def create_chart_analysis(interval, asset_name):
             "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}"
+            "RSI": f"{df['RSI'].iloc[-1]:.1f}" if 'RSI' in df.columns else "N/A",
+            "MACD": f"{df['MACD'].iloc[-1]:.4f}" if 'MACD' in df.columns else "N/A",
+            "Volume": f"{df['Volume'].iloc[-1]:,.0f}" if 'Volume' in df.columns else "N/A"
         }
         
         # Create prediction chart using matplotlib
@@ -349,4 +361,4 @@ if __name__ == "__main__":
         server_port=7860,
         share=False,
         show_api=True
-    )
+    )

data_processor.py

@@ -89,8 +89,9 @@ class DataProcessor:
         df['ATR'] = true_range.rolling(window=14).mean()
         
         # Volume indicators
-        df['Volume_SMA'] = df['Volume'].rolling(window=20).mean()
-        df['Volume_ratio'] = df['Volume'] / df['Volume_SMA']
+        if 'Volume' in df.columns:
+            df['Volume_SMA'] = df['Volume'].rolling(window=20).mean()
+            df['Volume_ratio'] = df['Volume'] / df['Volume_SMA']
         
         return df
     
@@ -102,9 +103,10 @@ class DataProcessor:
             
             # Asset-specific fundamentals
             if ticker == "BTC-USD":
+                market_cap = info.get('marketCap', 0)
                 fundamentals = {
                     "Strength Index": round(np.random.uniform(30, 80), 1),
-                    "Market Cap": f"${info.get('marketCap', 0):,.0f}" if info.get('marketCap') else "N/A",
+                    "Market Cap": f"${market_cap:,.0f}" if market_cap else "N/A",
                     "24h Volume": f"${np.random.uniform(20, 80):.1f}B",
                     "Volatility": f"{np.random.uniform(40, 120):.1f}%",
                     "Network Hash Rate": f"{np.random.uniform(300, 600):.0f} EH/s",
@@ -151,4 +153,4 @@ class DataProcessor:
             'mean': mean,
             'std': std,
             'original': prices
-        }
+        }

requirements.txt

@@ -14,3 +14,4 @@ yfinance
 seaborn
 plotly
 tqdm
+chronos-forecasting

sentiment_analyzer.py

@@ -64,15 +64,4 @@ class SentimentAnalyzer:
             return sentiment, news_html
             
         except Exception as e:
-            return 0, f"<p>Error analyzing sentiment: {str(e)}</p>"
-
-**Key Changes Made:**
-
-1. **Error Handling**: Instead of returning `str(e)`, the functions now create proper matplotlib figures with error messages displayed as text within the plot
-2. **Interval Fix**: Removed "4h" interval from dropdown since it's not supported for BTC-USD
-3. **UI Colors**: Ensured all components have white background with black text for proper visibility
-4. **Consistent Plot Returns**: All code paths now return valid matplotlib figure objects
-5. **Better Error Messages**: Errors are now displayed inside the plot area rather than breaking the UI
-6. **Data Validation**: Added more checks for empty dataframes and invalid inputs
-
-The app should now load without errors and properly display both Gold and Bitcoin analysis with a clean white theme.
+            return 0, f"<p>Error analyzing sentiment: {str(e)}</p>"

trading_logic.py

@@ -11,16 +11,16 @@ class TradingLogic:
                 return "hold", 0.0
             
             # Get last values
-            rsi = df['RSI'].iloc[-1]
-            macd = df['MACD'].iloc[-1]
-            macd_signal = df['MACD_signal'].iloc[-1]
+            rsi = df['RSI'].iloc[-1] if 'RSI' in df.columns else 50
+            macd = df['MACD'].iloc[-1] if 'MACD' in df.columns else 0
+            macd_signal = df['MACD_signal'].iloc[-1] if 'MACD_signal' in df.columns else 0
             
             # Prediction trend
             pred_trend = np.polyfit(range(len(predictions)), predictions, 1)[0]
             
             # Price vs predictions
             pred_mean = np.mean(predictions)
-            price_diff_pct = (pred_mean - current_price) / current_price
+            price_diff_pct = (pred_mean - current_price) / current_price if current_price != 0 else 0
             
             # Initialize signals
             signals = []
@@ -51,13 +51,14 @@ class TradingLogic:
                 confidences.append(min(abs(price_diff_pct) * 10, 0.8))
             
             # Bollinger Bands
-            bb_position = (current_price - df['BB_lower'].iloc[-1]) / (df['BB_upper'].iloc[-1] - df['BB_lower'].iloc[-1])
-            if bb_position < 0.2:
-                signals.append("buy")
-                confidences.append(0.5)
-            elif bb_position > 0.8:
-                signals.append("sell")
-                confidences.append(0.5)
+            if 'BB_lower' in df.columns and 'BB_upper' in df.columns:
+                bb_position = (current_price - df['BB_lower'].iloc[-1]) / (df['BB_upper'].iloc[-1] - df['BB_lower'].iloc[-1])
+                if bb_position < 0.2:
+                    signals.append("buy")
+                    confidences.append(0.5)
+                elif bb_position > 0.8:
+                    signals.append("sell")
+                    confidences.append(0.5)
             
             # Aggregate signals
             if not signals:
@@ -90,8 +91,8 @@ class TradingLogic:
                 return None, None
             
             # Use 2x ATR for stop loss, 4x ATR for take profit (1:2 ratio)
-            sl_distance = atr * 2
-            tp_distance = atr * 4
+            sl_distance = atr * 2 if atr else current_price * 0.02
+            tp_distance = atr * 4 if atr else current_price * 0.04
             
             if signal == "buy":
                 tp = current_price + tp_distance