Update index.py

index.py

@@ -247,9 +247,10 @@ def update_output(selected_topic, selected_domain, start_date, end_date):
         bar_fig_1.update_xaxes(tickformat="%b %d<br>%Y")
         # Bar Graph ends
                            
+        # line-fig 2 starts
         # filter dataframes based on updated data range
         # Filtering data...
-        df_filtered = counts[(counts['Topic'] == selected_topic) &
+        df_filtered_2 = counts[(counts['Topic'] == selected_topic) &
                              (counts['domain_folder_name'] == selected_domain) &
                              (counts['date'] >= start_date) &
                              (counts['date'] <= end_date)]
@@ -266,7 +267,7 @@ def update_output(selected_topic, selected_domain, start_date, end_date):
         df_all_combinations = pd.DataFrame(index=all_combinations).reset_index()
     
         # Merge filtered DataFrame with DataFrame of all combinations
-        merged_df = pd.merge(df_all_combinations, df_filtered, on=['FinBERT_label', 'Topic', 'domain_folder_name', 'date'], how='left')
+        merged_df = pd.merge(df_all_combinations, df_filtered_2, on=['FinBERT_label', 'Topic', 'domain_folder_name', 'date'], how='left')
     
         # Fill missing values with zeros
         merged_df['count'].fillna(0, inplace=True)
@@ -281,6 +282,7 @@ def update_output(selected_topic, selected_domain, start_date, end_date):
         # Update layout...
         line_fig_2.update_layout(xaxis_title='Date', yaxis_title='Number of News Articles',
                                xaxis=dict(tickformat="%b %d<br>%Y"))
+        # line-fig 2 ends
         
         # Map original labels to their translated versions
         label_translation = {'positive': 'positivo', 'neutral': 'neutro', 'negative': 'negativo'}
@@ -307,7 +309,7 @@ def update_output(selected_topic, selected_domain, start_date, end_date):
         label_colors = {'positivo': '#039a4d', 'neutro': '#3c03f4', 'negativo': '#ca3919'}
                          
         # Filter DataFrame for current media category
-        media_df = df_filtered[df_filtered['Veículos de notícias'] == media]
+        media_df = df_filtered[df_filtered['Veículos de notícias'] == selected_domain]
     
         # Group by FinBERT_label and count occurrences
         label_counts = media_df['FinBERT_label_transformed'].value_counts()
@@ -331,9 +333,9 @@ def update_output(selected_topic, selected_domain, start_date, end_date):
                                                       ordered=True)
 
         # Sort DataFrame by sentiment label and date
-        df_filtered = df_filtered.sort_values(by=['FinBERT_label', 'date'])
-
-        return line_fig_1, bar_fig_1, pie_chart_1, line_fig_2, pie_chart_2, df_filtered.to_dict('records')
+        # df_filtered = df_filtered.sort_values(by=['FinBERT_label', 'date'])
+        # df_filtered.to_dict('records')
+        return line_fig_1, bar_fig_1, pie_chart_1, line_fig_2, pie_chart_2, {'data': []}
     else:
         return {'data': []},{'data': []} ,{'data': []} ,{'data': []} , {'data': []}, {'data': []}