Update app.py

app.py

@@ -2,17 +2,16 @@
 
 # -*- coding: utf-8 -*-
 #
-# PROJECT:      CognitiveEDA v5.5 - The QuantumLeap Intelligence Platform
+# PROJECT:      CognitiveEDA v5.7 - The QuantumLeap Intelligence Platform
 #
-# DESCRIPTION:  Main application entry point. This definitive version correctly
-#               handles multiple outputs by aligning with Gradio's API, passing
-#               a list of components to the `outputs` parameter.
+# DESCRIPTION:  Main application entry point. This version adds UI components
+#               for cluster profiling to the Clustering tab.
 #
 # SETUP:        $ pip install -r requirements.txt
 #
 # AUTHOR:       An MCP & PhD Expert in Data & AI Solutions
-# VERSION:      5.5 (Final API-Compliant Edition)
-# LAST-UPDATE:  2023-10-30 (Corrected multiple output handling)
+# VERSION:      5.7 (Cluster Profiling Edition)
+# LAST-UPDATE:  2023-10-31 (Integrated cluster persona analysis)
 
 import warnings
 import logging
@@ -39,6 +38,7 @@ def main():
             upload_button = gr.File(label="1. Upload Data File", file_types=[".csv", ".xlsx"], scale=3)
             analyze_button = gr.Button("✨ Generate Intelligence Report", variant="primary", scale=1)
         with gr.Tabs():
+            # ... (Other tabs remain the same)
             with gr.Tab("🤖 AI-Powered Strategy Report"):
                 ai_report_output = gr.Markdown("### Your AI-generated report will appear here...")
             with gr.Tab("📋 Data Profile"):
@@ -54,27 +54,38 @@ def main():
                     with gr.Column(scale=1):
                         dd_scatter_x, dd_scatter_y, dd_scatter_color = gr.Dropdown(label="X-Axis", interactive=True), gr.Dropdown(label="Y-Axis", interactive=True), gr.Dropdown(label="Color By", interactive=True)
                     with gr.Column(scale=2): plot_scatter = gr.Plot()
+            
+            # --- MODIFIED CLUSTERING TAB ---
             with gr.Tab("🧩 Clustering (K-Means)", visible=False) as tab_cluster:
                 with gr.Row():
                     with gr.Column(scale=1):
-                        num_clusters = gr.Slider(minimum=2, maximum=10, value=3, step=1, label="K", interactive=True)
-                        md_cluster_summary = gr.Markdown()
-                    with gr.Column(scale=2): plot_cluster = gr.Plot()
-                plot_elbow = gr.Plot()
+                        num_clusters = gr.Slider(minimum=2, maximum=10, value=5, step=1, label="Number of Clusters (K)", interactive=True)
+                        md_cluster_summary = gr.Markdown("Methodology summary will appear here.")
+                    with gr.Column(scale=2):
+                        plot_cluster = gr.Plot(label="PCA Visualization")
+                
+                gr.Markdown("---")
+                gr.Markdown("## Cluster Profile Analysis")
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        md_cluster_personas = gr.Markdown("Detailed cluster personas will appear here...")
+                    with gr.Column(scale=2):
+                        plot_cluster_profile = gr.Plot(label="Cluster Profile Visualization")
+                
+                gr.Markdown("---")
+                gr.Markdown("## Optimal K Analysis")
+                plot_elbow = gr.Plot(label="The Elbow Method")
+            
             tab_timeseries, tab_text = gr.Tab("⌛ Time-Series", visible=False), gr.Tab("📝 Text", visible=False)
 
-        # 2. DEFINE THE OUTPUTS LIST
-        # This is the critical change. We create an explicit list of components
-        # that will be updated by the main analysis function.
-        # The order here MUST match the order of the returned tuple in the callback.
         main_outputs = [
             ai_report_output, profile_missing_df, profile_numeric_df, profile_categorical_df,
             plot_types, plot_missing, plot_correlation,
             dd_hist_col, dd_scatter_x, dd_scatter_y, dd_scatter_color,
             tab_timeseries, tab_text, tab_cluster
         ]
-
-        # 3. REGISTER EVENT HANDLERS
+        
+        # 2. REGISTER EVENT HANDLERS
         analysis_complete_event = analyze_button.click(
             fn=callbacks.run_initial_analysis,
             inputs=[upload_button],
@@ -83,15 +94,26 @@ def main():
         analysis_complete_event.then(
             fn=callbacks.generate_reports_and_visuals,
             inputs=[state_analyzer],
-            outputs=main_outputs  # Pass the LIST of components, not a dictionary.
+            outputs=main_outputs
         )
 
-        # --- Other Interactive Callbacks ---
         dd_hist_col.change(fn=callbacks.create_histogram, inputs=[state_analyzer, dd_hist_col], outputs=[plot_histogram])
         scatter_inputs = [state_analyzer, dd_scatter_x, dd_scatter_y, dd_scatter_color]
         for dropdown in [dd_scatter_x, dd_scatter_y, dd_scatter_color]:
             dropdown.change(fn=callbacks.create_scatterplot, inputs=scatter_inputs, outputs=[plot_scatter])
-        num_clusters.change(fn=callbacks.update_clustering, inputs=[state_analyzer, num_clusters], outputs=[plot_cluster, plot_elbow, md_cluster_summary])
+
+        # --- MODIFIED CLUSTERING CALLBACK WIRING ---
+        num_clusters.change(
+            fn=callbacks.update_clustering,
+            inputs=[state_analyzer, num_clusters],
+            outputs=[
+                plot_cluster,
+                plot_elbow,
+                md_cluster_summary,
+                md_cluster_personas,
+                plot_cluster_profile
+            ]
+        )
     
     demo.launch(debug=False, server_name="0.0.0.0")