remove visualizations for now

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from transformers import pipeline
 import pandas as pd
 import spaces
-import plotly.express as px
 
 # Load dataset
 from datasets import load_dataset
@@ -39,40 +38,6 @@ def classify_comments(categories):
     df['comment_category'] = assigned_categories
     return df[['customer_id', 'customer_comment', 'comment_sentiment', 'comment_category', 'customer_nps', 'customer_segment']].to_html(index=False)
 
-def visualize_output():
-    # Ensure the required columns exist
-    if 'comment_sentiment' not in df.columns or 'comment_category' not in df.columns:
-        return None, "Error: Please classify comments before visualizing."
-
-    try:
-        # 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)
-        bar_chart = px.bar(
-            sentiment_by_category,
-            barmode='stack',
-            title="Sentiment by Comment Category",
-            labels={'value': 'Count', 'comment_category': 'Category', 'comment_sentiment': 'Sentiment'}
-        )
-
-        # KPI Visualizations
-        avg_nps = df['customer_nps'].mean()
-        avg_nps_by_segment = df.groupby('customer_segment')['customer_nps'].mean().reset_index()
-
-        kpi_visualization = f"""
-        **Average NPS Scores:**
-        - Overall: {avg_nps:.2f}
-        **Average NPS by Segment:**
-        {avg_nps_by_segment.to_markdown(index=False)}
-        """
-
-        return bar_chart, kpi_visualization
-
-    except Exception as e:
-        print(f"Error in visualize_output: {e}")
-        return None, f"Error: {str(e)}"
-
 # Gradio Interface
 with gr.Blocks() as nps:
     # State to store categories
@@ -104,14 +69,6 @@ with gr.Blocks() as nps:
     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")
-    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):
         global df  # Ensure we're modifying the global DataFrame
@@ -159,9 +116,5 @@ with gr.Blocks() as nps:
         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)