Update app.py

app.py

@@ -13,7 +13,7 @@ df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
 df['Year'] = df['Date'].dt.year
 df['Month'] = df['Date'].dt.to_period('M')
 df['Day'] = df['Date'].dt.date
-df = df[df['Year'] >= 2015]  # filtro anno
+df = df[df['Year'] >= 2015]
 
 # --- TICKERS ---
 TICKERS = {
@@ -38,16 +38,13 @@ for company, ticker in TICKERS.items():
         df_prices = df_prices.rename(columns={f'Close_{ticker}': 'Close_NASDAQ'})
     prices[company] = df_prices
 
-# --- FUNZIONE PER PREZZI INTERPOLATI ---
+# --- INTERPOLATE PRICES FOR DAY/MONTH ---
 def get_prices_for_agg(agg_col):
     df_prices_agg = {}
     for company, df_price in prices.items():
         df_temp = df_price.copy()
-        if company == "NASDAQ":
-            col = "Close_NASDAQ"
-        else:
-            col = f"Close_{TICKERS[company]}"
-            df_temp = df_temp.rename(columns={f"Close_{TICKERS[company]}": col})
+        col = 'Close_NASDAQ' if company == 'NASDAQ' else f"Close_{TICKERS[company]}"
+        df_temp = df_temp.rename(columns={df_temp.columns[1]: col})
 
         if agg_col == "Day":
             df_temp = df_temp.set_index('Date').resample('D').mean().interpolate('linear').reset_index()
@@ -57,6 +54,7 @@ def get_prices_for_agg(agg_col):
         elif agg_col == "Year":
             df_temp['Year'] = df_temp['Date'].dt.year
             df_temp = df_temp.groupby('Year')[col].last().reset_index()
+
         df_prices_agg[company] = df_temp
     return df_prices_agg
 
@@ -70,29 +68,23 @@ for company in df['Company'].unique():
 df_merged = df_merged.merge(prices['NASDAQ'][['Date', 'Close_NASDAQ']], on='Date', how='left')
 
 # --- GRADIO FUNCTION ---
-def show_sentiment(selected_companies=None, aggregation="Day", selected_year=None):
+def show_sentiment(selected_companies=None, aggregation="Day", selected_year="All"):
     if not selected_companies:
         selected_companies = ["NASDAQ"]
 
     df_filtered = df_merged.copy()
-    if selected_year != "All":
+    if selected_year != "All" and selected_year is not None:
         df_filtered = df_filtered[df_filtered['Year'] == int(selected_year)]
 
-
     # colonna aggregazione
-    if aggregation == "Day":
-        group_col = "Day"
-    elif aggregation == "Month":
-        group_col = "Month"
+    group_col = aggregation
+    if aggregation == "Month":
         df_filtered['Month'] = df_filtered['Month'].dt.to_timestamp()
-    elif aggregation == "Year":
-        group_col = "Year"
-    else:
-        group_col = "Day"
-    
+    elif aggregation == "Day":
+        df_filtered['Day'] = df_filtered['Date']
 
-    # prezzi interpolati per il livello di aggregazione
-    prices_agg = get_prices_for_agg(group_col)
+    # prezzi interpolati
+    prices_agg = get_prices_for_agg(aggregation)
 
     include_nasdaq = "NASDAQ" in selected_companies
     companies_to_plot = [c for c in selected_companies if c != "NASDAQ"]
@@ -104,6 +96,8 @@ def show_sentiment(selected_companies=None, aggregation="Day", selected_year=Non
         df_sent = df_filtered[df_filtered['Company'].isin(companies_to_plot)]
         df_tmp = df_sent.groupby([group_col, 'Company']).agg({'Score':'sum'}).reset_index()
         for c in companies_to_plot:
+            if c not in TICKERS:
+                continue
             ticker_col = f"Close_{TICKERS[c]}"
             df_price_col = prices_agg[c][[group_col, ticker_col]]
             df_tmp = df_tmp.merge(df_price_col, on=group_col, how='left')
@@ -128,14 +122,20 @@ def show_sentiment(selected_companies=None, aggregation="Day", selected_year=Non
         ticker_col = f"Close_{TICKERS[c]}"
         df_c = df_grouped[df_grouped['Company'] == c]
         if ticker_col in df_c.columns and df_c[ticker_col].notnull().any():
-            fig.add_scatter(x=df_c[group_col], y=df_c[ticker_col],
-                            mode='lines', name=f"{c} Price", yaxis="y2", line=dict(dash='dot'))
+            fig.add_scatter(
+                x=df_c[group_col], y=df_c[ticker_col],
+                mode='lines', name=f"{c} Price", yaxis="y2",
+                line=dict(dash='dot')
+            )
 
     if include_nasdaq:
         df_c = df_grouped[df_grouped['Company'] == 'General']
         if 'Close_NASDAQ' in df_c.columns and df_c['Close_NASDAQ'].notnull().any():
-            fig.add_scatter(x=df_c[group_col], y=df_c['Close_NASDAQ'],
-                            mode='lines', name="NASDAQ Price", yaxis="y2", line=dict(dash='dot'))
+            fig.add_scatter(
+                x=df_c[group_col], y=df_c['Close_NASDAQ'],
+                mode='lines', name="NASDAQ Price", yaxis="y2",
+                line=dict(dash='dot')
+            )
 
     fig.update_layout(
         yaxis2=dict(
@@ -158,11 +158,11 @@ demo = gr.Interface(
             choices=companies,
             value=None,
             label="Select Companies (NASDAQ compares with general sentiment)",
-            multiselect=False
+            multiselect=True
         ),
         gr.Radio(
             choices=["Month", "Year"],
-            value="Year",
+            value="Month",
             label="Aggregation Level"
         ),
         gr.Dropdown(
@@ -176,7 +176,10 @@ demo = gr.Interface(
         gr.Plot(label="Sentiment Trend"),
     ],
     title="Dynamic Sentiment Dashboard",
-    description="Shows sentiment scores aggregated by month or year. NASDAQ compares with general sentiment if selected."
+    description="Shows sentiment scores aggregated by month, or year. NASDAQ compares with general sentiment if selected."
+
+    
 )
 
 demo.launch()
+