Update app.py

app.py

@@ -45,7 +45,7 @@ df_multi['Predicted'] = 0.0
 
 for i, row in df_multi.iterrows():
     company = row['Company']
-    
+
     # Sentiment for all companies
     try:
         res = sentiment_pipeline(row['Riassunto'])[0]
@@ -54,7 +54,7 @@ for i, row in df_multi.iterrows():
     except:
         df_multi.at[i,'Sentiment'] = 'ERROR'
         df_multi.at[i,'Confidence'] = 0.0
-    
+
     # Regression only for Tesla & Microsoft
     if company == "Tesla":
         try:
@@ -75,8 +75,10 @@ for company, ticker in TICKERS.items():
     start_date = df_multi[df_multi['Company']==company]['date'].min()
     end_date = pd.Timestamp.today()
     df_prices = yf.download(ticker, start=start_date, end=end_date)[['Close']].reset_index()
-    df_prices['date_merge'] = pd.to_datetime(df_prices['Date']).dt.normalize()
-    df_prices['PctChangeDaily'] = df_prices['Close'].pct_change().shift(-1)
+    # Flatten the columns to a single level
+    df_prices.columns = ['_'.join(col).strip() if isinstance(col, tuple) else col for col in df_prices.columns]
+    df_prices['date_merge'] = pd.to_datetime(df_prices['Date_']).dt.normalize()
+    df_prices['PctChangeDaily'] = df_prices[f'Close_{ticker}'].pct_change().shift(-1)
     prices[company] = df_prices
 
 # --- MERGE & CALCULATE STRATEGIES ---
@@ -84,7 +86,7 @@ dfs_final = {}
 for company in companies:
     df_c = df_multi[df_multi['Company']==company].copy()
     df_c = pd.merge(df_c, prices[company], on='date_merge', how='inner')
-    
+
     # Strategy A: Sentiment
     df_c['StrategyA_Daily'] = 0
     df_c['StrategyA_Cumulative'] = 0
@@ -92,15 +94,16 @@ for company in companies:
         score = df_c.loc[i,'Confidence']
         pct = df_c.loc[i,'PctChangeDaily'] if pd.notnull(df_c.loc[i,'PctChangeDaily']) else 0
         if df_c.loc[i,'Sentiment']=="UP" and score>0.8:
-            df_c.loc[i,'StrategyA_Daily'] = df_c.loc[i,'Close']*pct
+            df_c.loc[i,'StrategyA_Daily'] = df_c.loc[i,f'Close_{TICKERS[company]}']*pct
         elif df_c.loc[i,'Sentiment']=="DOWN" and score>0.8:
-            df_c.loc[i,'StrategyA_Daily'] = df_c.loc[i,'Close']*pct*-1
+            df_c.loc[i,'StrategyA_Daily'] = df_c.loc[i,f'Close_{TICKERS[company]}']*pct*-1
         df_c.loc[i,'StrategyA_Cumulative'] = df_c.loc[i-1,'StrategyA_Cumulative'] + df_c.loc[i,'StrategyA_Daily']
-    
+
+
     # Strategy B: Regression (only Tesla & Microsoft)
-    df_c['StrategyB_Daily'] = df_c['Predicted']*df_c['PctChangeDaily']*df_c['Close']
+    df_c['StrategyB_Daily'] = df_c['Predicted']*df_c['PctChangeDaily']*df_c[f'Close_{TICKERS[company]}']
     df_c['StrategyB_Cumulative'] = df_c['StrategyB_Daily'].cumsum()
-    
+
     dfs_final[company] = df_c
 
 # --- PLOT STRATEGIES ---
@@ -114,16 +117,34 @@ fig_strategy_B = px.line(df_plot_B, x='Data', y='StrategyB_Cumulative', color='C
                          title="Portfolio Evolution Following Strategy B (Regression)",
                          labels={'StrategyB_Cumulative':'Cumulative Value','Data':'Date'})
 
-# --- GRADIO INTERFACE ---
-outputs = [gr.Dataframe(label=company) for company in dfs_final.keys()]
-outputs += [gr.Plot(label="Strategy A: Sentiment"), gr.Plot(label="Strategy B: Regression")]
+# --- FUNCTION FOR GRADIO ---
+def show_company_data(selected_companies):
+    if not selected_companies:
+        return "Select at least one company to visualize.", None, None
+    
+    # DataFrames
+    tables = [dfs_final[c].head(10) for c in selected_companies if c in dfs_final]
+    
+    # Strategy plots
+    df_plot_A = pd.concat([dfs_final[c][['Data','StrategyA_Cumulative']].assign(Company=c) for c in selected_companies if c in dfs_final], ignore_index=True)
+    df_plot_B = pd.concat([dfs_final[c][['Data','StrategyB_Cumulative']].assign(Company=c) for c in selected_companies if c in dfs_final], ignore_index=True)
+    
+    fig_A = px.line(df_plot_A, x='Data', y='StrategyA_Cumulative', color='Company', title="Portfolio Evolution - Strategy A (Sentiment)")
+    fig_B = px.line(df_plot_B, x='Data', y='StrategyB_Cumulative', color='Company', title="Portfolio Evolution - Strategy B (Regression)")
+    
+    return tables, fig_A, fig_B
 
+# --- GRADIO INTERFACE ---
 demo = gr.Interface(
-    fn=lambda: [df.head(10) for df in dfs_final.values()] + [fig_strategy_A, fig_strategy_B],
-    inputs=[],
-    outputs=outputs,
-    title="Portfolio Evolution: Strategy A vs Strategy B",
-    description="Visualizes portfolio evolution based on sentiment (Strategy A) and regression predictions (Strategy B). Regression applies only to Tesla and Microsoft."
+    fn=show_company_data,
+    inputs=[gr.CheckboxGroup(choices=companies, label="Select Companies")],
+    outputs=[
+        gr.Dataframe(label="Top 10 Rows per Company", type="pandas"),
+        gr.Plot(label="Strategy A: Sentiment"),
+        gr.Plot(label="Strategy B: Regression")
+    ],
+    title="Interactive Portfolio Evolution",
+    description="Select one or more companies to visualize portfolio evolution based on Strategy A (Sentiment) and Strategy B (Regression). Regression applies only to Tesla and Microsoft."
 )
 
-demo.launch()
+demo.launch()