Update app.py

app.py

@@ -31,28 +31,31 @@ COMPANIES = {
     'Netflix (NFLX)': 'NFLX'
 }
 
-def calculate_metrics(data: pd.DataFrame) -> pd.DataFrame:
-    df = data.copy()
-    
-    # Basic metrics
-    df['Returns'] = df['Close'].pct_change()
-    df['SMA_20'] = df['Close'].rolling(window=20).mean()
-    df['SMA_50'] = df['Close'].rolling(window=50).mean()
-    
-    # RSI
-    delta = df['Close'].diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
-    rs = gain / loss
-    df['RSI'] = 100 - (100 / (1 + rs))
-    
-    # Bollinger Bands
-    df['BB_middle'] = df['Close'].rolling(window=20).mean()
-    bb_std = df['Close'].rolling(window=20).std()
-    df['BB_upper'] = df['BB_middle'] + (2 * bb_std)
-    df['BB_lower'] = df['BB_middle'] - (2 * bb_std)
-    
-    return df
+def calculate_metrics(df: pd.DataFrame) -> pd.DataFrame:
+    data = df.copy()
+    
+    # Calculate basic metrics
+    data['Returns'] = data['Close'].pct_change()
+    data['SMA_20'] = data['Close'].rolling(window=20).mean()
+    data['SMA_50'] = data['Close'].rolling(window=50).mean()
+    
+    # Calculate RSI
+    delta = data['Close'].diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    avg_gain = gain.rolling(window=14).mean()
+    avg_loss = loss.rolling(window=14).mean()
+    rs = avg_gain / avg_loss
+    data['RSI'] = 100 - (100 / (1 + rs))
+    
+    # Calculate Bollinger Bands
+    rolling_mean = data['Close'].rolling(window=20).mean()
+    rolling_std = data['Close'].rolling(window=20).std()
+    data['BB_middle'] = rolling_mean
+    data['BB_upper'] = rolling_mean + (rolling_std * 2)
+    data['BB_lower'] = rolling_mean - (rolling_std * 2)
+    
+    return data
 
 def create_analysis_plots(data: pd.DataFrame) -> list:
     # Price and Volume Plot
@@ -122,7 +125,7 @@ def create_analysis_plots(data: pd.DataFrame) -> list:
     
     return [fig1, fig2]
 
-def generate_summary(data: pd.DataFrame) -> str:
+def generate_summary(data: pd.DataFrame, symbol: str) -> str:
     current_price = data['Close'].iloc[-1]
     prev_price = data['Close'].iloc[-2]
     daily_return = ((current_price - prev_price) / prev_price) * 100
@@ -131,7 +134,7 @@ def generate_summary(data: pd.DataFrame) -> str:
     sma_20 = data['SMA_20'].iloc[-1]
     sma_50 = data['SMA_50'].iloc[-1]
     
-    summary = f"""Market Analysis Summary:
+    summary = f"""Analysis Summary for {symbol}:
     
 • Current Price: ${current_price:.2f}
 • Daily Change: {daily_return:+.2f}%
@@ -164,7 +167,7 @@ def analyze_stock(company: str, lookback_days: int = 180) -> tuple:
         data = calculate_metrics(data)
         
         # Generate analysis
-        summary = generate_summary(data)
+        summary = generate_summary(data, symbol)
         plots = create_analysis_plots(data)
         
         return summary, plots[0], plots[1]
@@ -192,7 +195,7 @@ def create_gradio_interface():
                 step=1,
                 label="Lookback Period (days)"
             )
-            refresh_btn = gr.Button("Refresh Analysis")
+            refresh_btn = gr.Button("🔄 Refresh")
         
         with gr.Row():
             summary = gr.Textbox(label="Analysis Summary", lines=10)
@@ -207,7 +210,7 @@ def create_gradio_interface():
             outputs=[summary, plot1, plot2]
         )
         
-        # Also trigger analysis when company or lookback period changes
+        # Auto-update on selection changes
         company.change(
             fn=analyze_stock,
             inputs=[company, lookback],