Update app.py

app.py

@@ -184,16 +184,15 @@ def generate_report(tpb_sentiment_df):
 
 def search_company(keyword):
     if not keyword:
-        return None, None, None, None
-    
+        return None, None, None, None, None, None, None, None, None, None
+
     filtered_df = df[df['text'].str.contains(keyword, case=False)]
     
     if filtered_df.empty:
-        return None, None, None, None
-    
+        return None, None, None, None, None, None, None, None, None, None
+
     # Calculate sentiment distribution
     sentiment_counts = filtered_df['label'].value_counts(normalize=True) * 100
-
     colors = ['red' if sentiment == 'negative' else 'gray' if sentiment == 'neutral' else 'blue' for sentiment in sentiment_counts.index]
 
     # Create the bar plot
@@ -204,7 +203,6 @@ def search_company(keyword):
         textposition='auto',
         marker_color=colors 
     )])
-
     sentiment_fig.update_layout(
         title='Overall Sentiment Distribution',
         xaxis_title='Sentiment',
@@ -224,7 +222,6 @@ def search_company(keyword):
     tpb_sentiment_df = filtered_df.groupby(['tpb_label', 'label']).size().unstack(fill_value=0)
     tpb_sentiment_df = tpb_sentiment_df.div(tpb_sentiment_df.sum(axis=1), axis=0) * 100
 
-    # Define colors for each sentiment
     color_map = {
         'negative': 'red',
         'neutral': 'gray',
@@ -258,23 +255,19 @@ def search_company(keyword):
         wordclouds[label] = generate_wordcloud(text)
         bigrams_data[label] = generate_bigrams(text)
 
-    # Extract only the words
     words_only = {
         key: [word_pair for word_pair, _ in value]
         for key, value in bigrams_data.items()
     }
-    # Create a single DataFrame for bigrams, with only the bigram text (no frequency)
     bigram_df = pd.DataFrame({
         label: data for label, data in words_only.items()
     })
 
-    print(bigrams_data.items())
     bigram_df.index = [f"Top {i+1}" for i in range(len(bigram_df))]
 
     return (sentiment_fig, tpb_fig, tpb_sentiment_fig, filtered_df[filtered_df['text'].str.len() < 300].head(5), 
             report, wordclouds.get('attitude'), wordclouds.get('religious knowledge'), 
-            wordclouds.get('subjective norms'), wordclouds.get('perceived behavioural control'),bigram_df)
-
+            wordclouds.get('subjective norms'), wordclouds.get('perceived behavioural control'), bigram_df)
 
 
 def text_classification_and_sentiment(text, keywords_df):