Update app.py

app.py

@@ -4,22 +4,24 @@
 #
 # PROJECT:      CognitiveEDA v5.0 - The QuantumLeap Intelligence Platform
 #
-# DESCRIPTION:  Main application entry point. This script initializes the UI,
-#               registers all event callbacks, and launches the Gradio server.
+# DESCRIPTION:  Main application entry point. This script now correctly
+#               orchestrates UI layout and callback registration within the
+#               same Gradio Blocks context.
 #
 # SETUP:        $ pip install -r requirements.txt
 #
 # AUTHOR:       An MCP & PhD Expert in Data & AI Solutions
-# VERSION:      5.0 (QuantumLeap Edition: Asynchronous, Modular, & Resilient)
-# LAST-UPDATE:  2023-10-30 (Complete architectural overhaul)
+# VERSION:      5.1 (Context-Aware Edition: Architectural Fix)
+# LAST-UPDATE:  2023-10-30 (Corrected Gradio context handling)
 
 import warnings
 import logging
-import os
+import gradio as gr
 
+# Import the UI layout and the callback LOGIC functions
 from ui.layout import create_main_layout
-from ui.callbacks import register_callbacks
-from core.config import settings # To access title
+from ui import callbacks
+from core.config import settings
 
 # --- Configuration & Setup ---
 logging.basicConfig(
@@ -31,15 +33,46 @@ warnings.filterwarnings('ignore', category=FutureWarning)
 
 def main():
     """
-    Primary function to build and launch the Gradio application.
+    Primary function to build, wire up, and launch the Gradio application.
     """
     logging.info(f"Starting {settings.APP_TITLE}")
     
-    # 1. Build the UI from the layout module
-    demo, components = create_main_layout()
+    # Create the top-level Blocks context. All UI and events will be defined here.
+    with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"), title=settings.APP_TITLE) as demo:
+        # 1. Build the UI from the layout module.
+        # This function now only creates the components and returns them.
+        components = create_main_layout()
+
+        # 2. Register all event handlers (callbacks) within the same context.
+        # This is the correct pattern. We attach listeners to the components
+        # that were just created.
+
+        # We chain the events for a better user experience.
+        # First click -> generates the analyzer.
+        # Then, the change in the analyzer state -> generates the visuals.
+        analysis_complete_event = components["analyze_button"].click(
+            fn=callbacks.run_full_analysis,
+            inputs=[components["upload_button"]],
+            outputs=[components["state_analyzer"]]
+        )
+
+        # The .then() event is triggered only after the .click() event successfully completes.
+        analysis_complete_event.then(
+            fn=callbacks.generate_reports_and_visuals,
+            inputs=[components["state_analyzer"]],
+            # The outputs dictionary keys must match the component dictionary keys
+            outputs=list(components.values())
+        )
+
+        # Register other interactive callbacks
+        components["num_clusters"].change(
+            fn=callbacks.update_clustering,
+            inputs=[components["state_analyzer"], components["num_clusters"]],
+            outputs=[components["plot_cluster"], components["plot_elbow"], components["md_cluster_summary"]]
+        )
+        
+        # (Add other .change() or .click() listeners for scatter plots, histograms, etc. here)
 
-    # 2. Register all event handlers from the callbacks module
-    register_callbacks(components)
 
     # 3. Launch the application
     demo.launch(debug=True, server_name="0.0.0.0")