Update app.py

app.py

@@ -37,7 +37,7 @@ def classify_comments(categories):
         sentiments.append(sentiment)
     df['comment_sentiment'] = sentiments
     df['comment_category'] = assigned_categories
-    return df[['customer_comment', 'comment_sentiment', 'comment_category']].to_html(index=False)
+    return df[['customer_id', 'customer_comment', 'comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment']].to_html(index=False)
 
 # Function to generate visualizations
 def visualize_output():
@@ -81,6 +81,7 @@ def visualize_output():
     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:**
@@ -89,9 +90,19 @@ def visualize_output():
     - 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)}
     """
 
-    return sentiment_pie, category_pie, stacked_bar, kpi_visualization
+    # Pie Chart of Sentiment by Customer Segment
+    sentiment_by_segment = df.groupby(['customer_segment', 'comment_sentiment']).size().unstack()
+    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
 
 # Gradio Interface
 with gr.Blocks() as nps:
@@ -130,6 +141,7 @@ with gr.Blocks() as nps:
     category_pie = gr.Plot(label="Comment Category Distribution")
     stacked_bar = gr.Plot(label="Sentiment by Comment Category")
     kpi_visualization = gr.Markdown()
+    sentiment_by_segment_pie = gr.Plot(label="Sentiment by Customer Segment")
 
     # Function to load data from uploaded CSV
     def load_data(file):
@@ -141,7 +153,7 @@ with gr.Blocks() as nps:
             else:
                 return "Error: Uploaded file is not a CSV."
             # Check for required columns
-            required_columns = ['customer_comment', 'customer_nps']
+            required_columns = ['customer_id', 'customer_comment', 'customer_nps', 'customer_segment']
             if not all(col in custom_df.columns for col in required_columns):
                 return f"Error: Uploaded CSV must contain the following columns: {', '.join(required_columns)}"
             df = custom_df
@@ -180,7 +192,7 @@ with gr.Blocks() as nps:
     )
     visualize_btn.click(
         fn=visualize_output,
-        outputs=[sentiment_pie, category_pie, stacked_bar, kpi_visualization]
+        outputs=[sentiment_pie, category_pie, stacked_bar, kpi_visualization, sentiment_by_segment_pie]
     )
 
 nps.launch(share=True)