Update app.py

app.py

@@ -40,48 +40,16 @@ def classify_comments(categories):
     return df[['customer_id', 'customer_comment', 'comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment']].to_html(index=False)
 
 def visualize_output():
-    # Debug: Print the columns in the DataFrame
-    print("Columns in DataFrame:", df.columns.tolist())
-
     # Ensure the required columns exist
     if 'comment_sentiment' not in df.columns or 'comment_category' not in df.columns:
-        print("Error: Required columns missing. Please classify comments first.")
-        return None, None, None, "Error: Please classify comments before visualizing.", None
-
-    # Debug: Print the first few rows of the DataFrame
-    print("First few rows of DataFrame:")
-    print(df.head())
+        return None, "Error: Please classify comments before visualizing."
 
     try:
-        # Pie Chart of Sentiment
-        sentiment_counts = df['comment_sentiment'].value_counts()
-        print("Sentiment counts:", sentiment_counts)
-        sentiment_pie = px.pie(
-            values=sentiment_counts.values,
-            names=sentiment_counts.index,
-            title="Sentiment Distribution",
-            hover_data=[sentiment_counts.values],
-            labels={'value': 'Count', 'names': 'Sentiment'}
-        )
-        sentiment_pie.update_traces(textinfo='percent+label', hovertemplate="Sentiment: %{label}<br>Count: %{value}<br>Percentage: %{percent}")
-
-        # Pie Chart of Comment Categories
-        category_counts = df['comment_category'].value_counts()
-        print("Category counts:", category_counts)
-        category_pie = px.pie(
-            values=category_counts.values,
-            names=category_counts.index,
-            title="Comment Category Distribution",
-            hover_data=[category_counts.values],
-            labels={'value': 'Count', 'names': 'Category'}
-        )
-        category_pie.update_traces(textinfo='percent+label', hovertemplate="Category: %{label}<br>Count: %{value}<br>Percentage: %{percent}")
-
-        # Stacked Bar Chart of Sentiment by Category
+        # Bar Chart of Sentiment by Category
         sentiment_by_category = df.groupby(['comment_category', 'comment_sentiment']).size().unstack()
         print("Sentiment by Category:")
         print(sentiment_by_category)
-        stacked_bar = px.bar(
+        bar_chart = px.bar(
             sentiment_by_category,
             barmode='stack',
             title="Sentiment by Comment Category",
@@ -90,37 +58,20 @@ def visualize_output():
 
         # KPI Visualizations
         avg_nps = df['customer_nps'].mean()
-        avg_nps_positive = df[df['comment_sentiment'] == 'POSITIVE']['customer_nps'].mean()
-        avg_nps_negative = df[df['comment_sentiment'] == 'NEGATIVE']['customer_nps'].mean()
-        avg_nps_by_category = df.groupby('comment_category')['customer_nps'].mean().reset_index()
         avg_nps_by_segment = df.groupby('customer_segment')['customer_nps'].mean().reset_index()
 
         kpi_visualization = f"""
         **Average NPS Scores:**
         - Overall: {avg_nps:.2f}
-        - Positive Sentiment: {avg_nps_positive:.2f}
-        - Negative Sentiment: {avg_nps_negative:.2f}
-        **Average NPS by Category:**
-        {avg_nps_by_category.to_markdown(index=False)}
         **Average NPS by Segment:**
         {avg_nps_by_segment.to_markdown(index=False)}
         """
 
-        # Pie Chart of Sentiment by Customer Segment
-        sentiment_by_segment = df.groupby(['customer_segment', 'comment_sentiment']).size().unstack()
-        print("Sentiment by Segment:")
-        print(sentiment_by_segment)
-        sentiment_by_segment_pie = px.pie(
-            sentiment_by_segment,
-            title="Sentiment by Customer Segment",
-            labels={'value': 'Count', 'customer_segment': 'Segment', 'comment_sentiment': 'Sentiment'}
-        )
-
-        return sentiment_pie, category_pie, stacked_bar, kpi_visualization, sentiment_by_segment_pie
+        return bar_chart, kpi_visualization
 
     except Exception as e:
         print(f"Error in visualize_output: {e}")
-        return None, None, None, f"Error: {str(e)}", None
+        return None, f"Error: {str(e)}"
 
 # Gradio Interface
 with gr.Blocks() as nps: