Update app.py

app.py

@@ -14,54 +14,55 @@ df['Year'] = df['Date'].dt.year
 df['Month'] = df['Date'].dt.to_period('M')
 df['Day'] = df['Date'].dt.date
 
-# --- TICKERS YAHOO FINANCE ---
+# --- TICKERS ---
 TICKERS = {
     "Tesla": "TSLA",
     "Microsoft": "MSFT",
     "Apple": "AAPL",
-    "Facebook": "META",  # ex FB
+    "Facebook": "META",
     "Google": "GOOGL",
-    "NASDAQ": "^IXIC"
+    "NASDAQ": "^IXIC"  # indice NASDAQ per confronto aggregato
 }
 
 # --- FETCH STOCK PRICES ---
 prices = {}
 for company, ticker in TICKERS.items():
-    start_date = df['Date'].min()  # prendi la data minima del dataset per tutti
+    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'])
     prices[company] = df_prices
 
-# --- MERGE PRICES INTO DATASET ---
+# --- MERGE PRICES IN 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
-    mask = df_merged['Company'] == company
-    df_merged.loc[mask, 'Close'] = pd.merge(
-        df_merged[mask],
-        df_price,
+    df_merged.loc[mask, 'Close'] = df_merged[mask].merge(
+        df_price[['Date','Close']],
         on='Date',
         how='left'
     )['Close'].values
 
 # --- GRADIO FUNCTION ---
 def show_sentiment(selected_companies, aggregation="Day"):
+    # filtro aziende selezionate
     if selected_companies:
         df_filtered = df_merged[df_merged['Company'].isin(selected_companies)].copy()
     else:
-        # Se non viene selezionata alcuna azienda, usa tutte le aziende per sentiment aggregato
         df_filtered = df_merged.copy()
-        selected_companies = ["NASDAQ"]  # per mostrare anche NASDAQ
-        # Creiamo un dataframe NASDAQ per unire al grafico
-        df_nasdaq = prices["NASDAQ"].copy()
-        df_nasdaq = df_nasdaq.rename(columns={'Close': 'Score'})
-        df_nasdaq['Company'] = 'NASDAQ'
-        df_nasdaq[group_col := 'Date'] = df_nasdaq['Date']
+    
+    # aggiungi NASDAQ se non selezionata nessuna azienda
+    if not selected_companies:
+        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)
 
-    # Determina colonna di aggregazione
+    # scelta colonna per aggregazione
     if aggregation == "Day":
         group_col = "Day"
     elif aggregation == "Month":
@@ -72,54 +73,34 @@ def show_sentiment(selected_companies, aggregation="Day"):
     else:
         group_col = "Day"
 
-    # Raggruppamento con sentiment e prezzo
-    if "NASDAQ" in selected_companies:
-        df_grouped = df_filtered.groupby([group_col, 'Company']).agg({
-            'Score': 'sum',
-            'Close': 'last'
-        }).reset_index()
+    # raggruppamento per sentiment
+    if selected_companies:
+        df_grouped = df_filtered.groupby([group_col, 'Company'])['Score'].sum().reset_index()
     else:
         df_grouped = df_filtered.groupby([group_col, 'Company']).agg({
-            'Score': 'sum',
-            'Close': 'last'
+            'Score':'sum',
+            'Close':'last'
         }).reset_index()
 
-    # --- CREAZIONE FIGURA ---
+    # --- PLOT ---
     fig = px.line(df_grouped, x=group_col, y='Score', color='Company',
                   title=f"Sentiment Score by {aggregation} per Company")
-
-    # Aggiungi linea prezzo sul secondary y
-    for company in selected_companies:
-        if company == "NASDAQ":
-            df_c = df_grouped[df_grouped['Company'] == 'NASDAQ']
-            fig.add_scatter(
-                x=df_c[group_col],
-                y=df_c['Score'],
-                mode='lines',
-                name="NASDAQ Index",
-                yaxis="y2",
-                line=dict(dash='dot', color='yellow')
+    
+    # aggiungi linea prezzi secondaria se disponibile
+    if 'Close' in df_grouped.columns:
+        for c in df_grouped['Company'].unique():
+            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",
+                overlaying="y",
+                side="right"
             )
-        else:
-            df_c = df_grouped[df_grouped['Company'] == company]
-            fig.add_scatter(
-                x=df_c[group_col],
-                y=df_c['Close'],
-                mode='lines',
-                name=f"{company} Price",
-                yaxis="y2",
-                line=dict(dash='dot')
-            )
-
-    fig.update_layout(
-        yaxis_title="Sentiment Score",
-        yaxis2=dict(
-            title="Stock Price / NASDAQ Index",
-            overlaying="y",
-            side="right"
-        ),
-        hovermode="x unified"
-    )
+        )
 
     return df_grouped.tail(30), fig
 
@@ -132,7 +113,7 @@ demo = gr.Interface(
         gr.Dropdown(
             choices=companies,
             value=None,
-            label="Select Companies (leave empty for general sentiment)",
+            label="Select Companies (leave empty for general sentiment with NASDAQ)",
             multiselect=True
         ),
         gr.Radio(
@@ -143,11 +124,12 @@ demo = gr.Interface(
     ],
     outputs=[
         gr.Dataframe(label="Sentiment Table", type="pandas"),
-        gr.Plot(label="Sentiment & Stock Price Trend"),
+        gr.Plot(label="Sentiment Trend"),
     ],
-    title="Dynamic Sentiment & Stock Price Dashboard",
-    description="Shows sentiment scores aggregated by day, month, or year. Overlay stock prices from Yahoo Finance and NASDAQ index for comparison."
+    title="Dynamic Sentiment Dashboard",
+    description="Shows sentiment scores aggregated by day, month, or year. Positive = +score, Negative = -score, Neutral = 0."
 )
 
 demo.launch()
 
+