revert

app.py

@@ -26,10 +26,6 @@ def classify_comments(categories):
     global df  # Ensure we're modifying the global DataFrame
     sentiments = []
     assigned_categories = []
-
-    # Debugging output
-    print("Classifying comments...")
-
     for comment in df['customer_comment']:
         # Classify sentiment
         sentiment = classifier(comment)[0]['label']
@@ -39,60 +35,38 @@ def classify_comments(categories):
         category = generator(prompt, max_length=30)[0]['generated_text']
         assigned_categories.append(category)
         sentiments.append(sentiment)
-    
     df['comment_sentiment'] = sentiments
     df['comment_category'] = assigned_categories
-    
-    # Debugging output
-    print(df.head())  
-    print(df['comment_sentiment'].value_counts())  
-    print(df['comment_category'].value_counts())  
-
     return df[['customer_id', 'customer_comment', 'comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment']].to_html(index=False)
 
 # Function to generate visualizations
-@spaces.GPU
 def visualize_output():
-    global df
-
-    # Check if DataFrame is empty
-    if df.empty:
-        return None, None, None, "Error: DataFrame is empty. Please check the data or classification step.", None
-
-    # Check for required columns
-    required_columns = ['comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment']
-    if not all(col in df.columns for col in required_columns):
-        return None, None, None, "Error: Required columns are missing. Please classify comments first.", None
-
-    # Explicitly convert data types
-    df['comment_sentiment'] = df['comment_sentiment'].astype(str)
-    df['comment_category'] = df['comment_category'].astype(str)
-    df['customer_nps'] = pd.to_numeric(df['customer_nps'], errors='coerce')
-    df['customer_segment'] = df['customer_segment'].astype(str)
-
-    # Drop NaN values
-    df = df.dropna(subset=['comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment'])
-
-    # Debugging output
-    print(df.head())  
-    print(df['comment_sentiment'].value_counts())  
-    print(df['comment_category'].value_counts())  
+    # Ensure the required columns exist
+    if 'comment_sentiment' not in df.columns or 'comment_category' not in df.columns:
+        # Return 5 values (None for plots and an error message for markdown)
+        return None, None, None, "Error: Please classify comments before visualizing.", None
 
     # Pie Chart of Sentiment
     sentiment_counts = df['comment_sentiment'].value_counts()
     sentiment_pie = px.pie(
         values=sentiment_counts.values,
         names=sentiment_counts.index,
-        title="Sentiment Distribution"
+        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()
     category_pie = px.pie(
         values=category_counts.values,
         names=category_counts.index,
-        title="Comment Category Distribution"
+        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
     sentiment_by_category = df.groupby(['comment_category', 'comment_sentiment']).size().unstack()
@@ -125,36 +99,44 @@ def visualize_output():
     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"
+        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:
+    # State to store categories
     categories = gr.State([])
 
+    # Function to add a category
     def add_category(categories, new_category):
-        if new_category.strip() != "" and len(categories) < 5:
+        if new_category.strip() != "" and len(categories) < 5:  # Limit to 5 categories
             categories.append(new_category.strip())
         return categories, "", f"**Categories:**\n" + "\n".join([f"- {cat}" for cat in categories])
 
+    # Function to reset categories
     def reset_categories():
         return [], "**Categories:**\n- None"
 
+    # UI for adding categories
     with gr.Row():
         category_input = gr.Textbox(label="New Category", placeholder="Enter category name")
         add_category_btn = gr.Button("Add Category")
         reset_btn = gr.Button("Reset Categories")
         category_status = gr.Markdown("**Categories:**\n- None")
 
+    # File upload and template buttons
     uploaded_file = gr.File(label="Upload CSV", type="filepath")
     template_btn = gr.Button("Use Template")
     gr.Markdown("# NPS Comment Categorization")
 
+    # Classify button
     classify_btn = gr.Button("Classify Comments")
     output = gr.HTML()
 
+    # Visualize button
     visualize_btn = gr.Button("Visualize Output")
     sentiment_pie = gr.Plot(label="Sentiment Distribution")
     category_pie = gr.Plot(label="Comment Category Distribution")
@@ -162,9 +144,56 @@ with gr.Blocks() as nps:
     kpi_visualization = gr.Markdown()
     sentiment_by_segment_pie = gr.Plot(label="Sentiment by Customer Segment")
 
-    add_category_btn.click(fn=add_category, inputs=[categories, category_input], outputs=[categories, category_input, category_status])
-    reset_btn.click(fn=reset_categories, outputs=[categories, category_status])
-    classify_btn.click(fn=classify_comments, inputs=categories, outputs=output)
-    visualize_btn.click(fn=visualize_output, outputs=[sentiment_pie, category_pie, stacked_bar, kpi_visualization, sentiment_by_segment_pie])
+    # Function to load data from uploaded CSV
+    def load_data(file):
+        global df  # Ensure we're modifying the global DataFrame
+        if file is not None:
+            file.seek(0)  # Reset file pointer
+            if file.name.endswith('.csv'):
+                custom_df = pd.read_csv(file, encoding='utf-8')
+            else:
+                return "Error: Uploaded file is not a CSV."
+            # Check for required columns
+            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
+            return "Custom CSV loaded successfully!"
+        else:
+            return "No file uploaded."
+
+    # Function to use template categories
+    def use_template():
+        template_categories = ["Product Experience", "Customer Support", "Price of Service", "Other"]
+        return template_categories, f"**Categories:**\n" + "\n".join([f"- {cat}" for cat in template_categories])
+
+    # Event handlers
+    add_category_btn.click(
+        fn=add_category,
+        inputs=[categories, category_input],
+        outputs=[categories, category_input, category_status]
+    )
+    reset_btn.click(
+        fn=reset_categories,
+        outputs=[categories, category_status]
+    )
+    uploaded_file.change(
+        fn=load_data,
+        inputs=uploaded_file,
+        outputs=output
+    )
+    template_btn.click(
+        fn=use_template,
+        outputs=[categories, category_status]
+    )
+    classify_btn.click(
+        fn=classify_comments,
+        inputs=categories,
+        outputs=output
+    )
+    visualize_btn.click(
+        fn=visualize_output,
+        outputs=[sentiment_pie, category_pie, stacked_bar, kpi_visualization, sentiment_by_segment_pie]
+    )
 
-nps.launch(share=True)
+nps.launch(share=True)