Update app.py

app.py

@@ -24,45 +24,45 @@ TICKERS = {
     "NASDAQ": "^IXIC"  # indice NASDAQ per confronto aggregato
 }
 
-# --- FETCH STOCK PRICES ---
+# --- FETCH STOCK PRICES ROBUSTLY ---
 prices = {}
 for company, ticker in TICKERS.items():
     start_date = df['Date'].min()
     end_date = pd.Timestamp.today()
-    df_prices = yf.download(ticker, start=start_date, end=end_date)[['Close']].reset_index()
-    # Flatten multiindex columns if present
-    df_prices.columns = [col if not isinstance(col, tuple) else '_'.join(col) for col in df_prices.columns]
-    df_prices['Date'] = pd.to_datetime(df_prices['Date_'])
+    df_prices = yf.download(ticker, start=start_date, end=end_date)
+
+    # Ensure 'Close' column exists
+    if 'Close' not in df_prices.columns:
+        if 'Adj Close' in df_prices.columns:
+            df_prices['Close'] = df_prices['Adj Close']
+        else:
+            df_prices['Close'] = None
+
+    df_prices = df_prices.reset_index()[['Date', 'Close']]
     prices[company] = df_prices
 
-# --- MERGE PRICES IN DATASET ---
+# --- MERGE PRICES INTO DATASET ---
 df_merged = df.copy()
 for company, df_price in prices.items():
-    mask = df_merged['Company'] == company
     if company == "NASDAQ":
-        continue  # NASDAQ lo useremo solo per confronto aggregato
-    df_merged.loc[mask, 'Close'] = df_merged[mask].merge(
-        df_price[['Date','Close']],
-        on='Date',
-        how='left'
-    )['Close'].values
+        continue  # NASDAQ only for comparison
+    mask = df_merged['Company'] == company
+    temp = df_merged[mask].merge(df_price, on='Date', how='left', suffixes=('', '_tmp'))
+    df_merged.loc[mask, 'Close'] = temp['Close'].values
 
 # --- GRADIO FUNCTION ---
 def show_sentiment(selected_companies, aggregation="Day"):
-    # filtro aziende selezionate
+    # Filter selected companies
     if selected_companies:
         df_filtered = df_merged[df_merged['Company'].isin(selected_companies)].copy()
     else:
         df_filtered = df_merged.copy()
-    
-    # aggiungi NASDAQ se non selezionata nessuna azienda
-    if not selected_companies:
+        # Add NASDAQ data for general sentiment
         df_nasdaq = prices['NASDAQ'].copy()
         df_nasdaq['Company'] = "NASDAQ"
-        df_nasdaq.rename(columns={'Close':'Close'}, inplace=True)
         df_filtered = pd.concat([df_filtered, df_nasdaq], ignore_index=True, sort=False)
 
-    # scelta colonna per aggregazione
+    # Determine aggregation column
     if aggregation == "Day":
         group_col = "Day"
     elif aggregation == "Month":
@@ -73,7 +73,7 @@ def show_sentiment(selected_companies, aggregation="Day"):
     else:
         group_col = "Day"
 
-    # raggruppamento per sentiment
+    # Aggregate sentiment
     if selected_companies:
         df_grouped = df_filtered.groupby([group_col, 'Company'])['Score'].sum().reset_index()
     else:
@@ -85,15 +85,14 @@ def show_sentiment(selected_companies, aggregation="Day"):
     # --- PLOT ---
     fig = px.line(df_grouped, x=group_col, y='Score', color='Company',
                   title=f"Sentiment Score by {aggregation} per Company")
-    
-    # aggiungi linea prezzi secondaria se disponibile
+
+    # Add stock price as secondary y-axis if available
     if 'Close' in df_grouped.columns:
         for c in df_grouped['Company'].unique():
-            df_c = df_grouped[df_grouped['Company']==c]
+            df_c = df_grouped[df_grouped['Company'] == c]
             if df_c['Close'].notnull().any():
                 fig.add_scatter(x=df_c[group_col], y=df_c['Close'], mode='lines',
                                 name=f"{c} Price", yaxis="y2", line=dict(dash='dot'))
-
         fig.update_layout(
             yaxis2=dict(
                 title="Stock Price",
@@ -130,6 +129,4 @@ demo = gr.Interface(
     description="Shows sentiment scores aggregated by day, month, or year. Positive = +score, Negative = -score, Neutral = 0."
 )
 
-demo.launch()
-
-
+demo.launch()