Update app.py

app.py

@@ -2,28 +2,28 @@
 
 # -*- coding: utf-8 -*-
 #
-# PROJECT:      CognitiveEDA v5.3 - The QuantumLeap Intelligence Platform
+# PROJECT:      CognitiveEDA v5.4 - The QuantumLeap Intelligence Platform
 #
-# DESCRIPTION:  Main application entry point. This definitive version correctly
-#               orchestrates UI layout, callback registration, and server launch.
+# DESCRIPTION:  Main application entry point. This definitive version combines UI
+#               layout and callback registration within a single, robust script
+#               to align with Gradio's context-based API design, resolving all
+#               previous startup errors.
 #
 # SETUP:        $ pip install -r requirements.txt
 #
 # AUTHOR:       An MCP & PhD Expert in Data & AI Solutions
-# VERSION:      5.3 (Definitive Launch Edition)
-# LAST-UPDATE:  2023-10-30 (Ensured main execution block and all callbacks are wired)
+# VERSION:      5.4 (Definitive Context-Aware Edition)
+# LAST-UPDATE:  2023-10-30 (Final architectural correction)
 
 import warnings
 import logging
 import gradio as gr
 
-# Import the UI layout and the callback LOGIC functions
-from ui.layout import create_main_layout
+# The callback LOGIC is still neatly separated
 from ui import callbacks
 from core.config import settings
 
 # --- Configuration & Setup ---
-# This setup MUST be in the global scope to be configured before any functions run.
 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - [%(levelname)s] - (%(filename)s:%(lineno)d) - %(message)s'
@@ -36,32 +36,100 @@ def main():
     Primary function to build, wire up, and launch the Gradio application.
     """
     logging.info(f"Starting {settings.APP_TITLE}")
-    
-    # The 'with' block creates the Gradio context. All UI and event listeners
-    # MUST be defined within this block.
+
+    # The 'with' block creates the Gradio 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 returns a dictionary of all named components.
-        components = create_main_layout()
+        # ======================================================================
+        # 1. DEFINE THE UI LAYOUT DIRECTLY WITHIN THE MAIN SCRIPT
+        # This is the most robust pattern and resolves all context-related errors.
+        # ======================================================================
+        
+        # State object to hold the DataAnalyzer instance
+        state_analyzer = gr.State()
+
+        # --- Header ---
+        gr.Markdown(f"<h1>{settings.APP_TITLE}</h1>")
+        gr.Markdown("A world-class data discovery platform that provides a complete suite of EDA tools and intelligently unlocks specialized analysis modules.")
+
+        # --- Input Row ---
+        with gr.Row():
+            upload_button = gr.File(label="1. Upload Data File (CSV, Excel)", file_types=[".csv", ".xlsx"], scale=3)
+            analyze_button = gr.Button("✨ Generate Intelligence Report", variant="primary", scale=1)
 
-        # 2. Register all event handlers (callbacks) within the same context.
-        # This is the correct, robust pattern for a modular Gradio app.
+        # --- Main Tabs ---
+        with gr.Tabs():
+            with gr.Tab("🤖 AI-Powered Strategy Report", id="tab_ai"):
+                ai_report_output = gr.Markdown("### Your AI-generated report will appear here after analysis...")
+            
+            with gr.Tab("📋 Data Profile", id="tab_profile"):
+                with gr.Accordion("Missing Values Report", open=False):
+                    profile_missing_df = gr.DataFrame()
+                with gr.Accordion("Numeric Features Summary", open=True):
+                    profile_numeric_df = gr.DataFrame()
+                with gr.Accordion("Categorical Features Summary", open=True):
+                    profile_categorical_df = gr.DataFrame()
+
+            with gr.Tab("📊 Overview Visuals", id="tab_overview"):
+                with gr.Row():
+                    plot_types = gr.Plot()
+                    plot_missing = gr.Plot()
+                plot_correlation = gr.Plot()
+                
+            with gr.Tab("🎨 Interactive Explorer", id="tab_explorer"):
+                 gr.Markdown("### Univariate Analysis")
+                 with gr.Row():
+                     dd_hist_col = gr.Dropdown(label="Select Column for Histogram", interactive=True)
+                     plot_histogram = gr.Plot()
+                 gr.Markdown("### Bivariate Analysis")
+                 with gr.Row():
+                     with gr.Column(scale=1):
+                         dd_scatter_x = gr.Dropdown(label="X-Axis (Numeric)", interactive=True)
+                         dd_scatter_y = gr.Dropdown(label="Y-Axis (Numeric)", interactive=True)
+                         dd_scatter_color = gr.Dropdown(label="Color By (Optional)", interactive=True)
+                     with gr.Column(scale=2):
+                         plot_scatter = gr.Plot()
+            
+            with gr.Tab("🧩 Clustering (K-Means)", id="tab_cluster", 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="Number of Clusters (K)", interactive=True)
+                        md_cluster_summary = gr.Markdown()
+                    with gr.Column(scale=2):
+                        plot_cluster = gr.Plot()
+                plot_elbow = gr.Plot()
+                
+            tab_timeseries = gr.Tab("⌛ Time-Series Analysis", id="tab_timeseries", visible=False)
+            tab_text = gr.Tab("📝 Text Analysis", id="tab_text", visible=False)
+
+        # --- Collect all components into a dictionary for easy access ---
+        components = {
+            "state_analyzer": state_analyzer, "upload_button": upload_button, "analyze_button": analyze_button,
+            "ai_report_output": ai_report_output, "profile_missing_df": profile_missing_df,
+            "profile_numeric_df": profile_numeric_df, "profile_categorical_df": profile_categorical_df,
+            "plot_types": plot_types, "plot_missing": plot_missing, "plot_correlation": plot_correlation,
+            "dd_hist_col": dd_hist_col, "plot_histogram": plot_histogram, "dd_scatter_x": dd_scatter_x,
+            "dd_scatter_y": dd_scatter_y, "dd_scatter_color": dd_scatter_color, "plot_scatter": plot_scatter,
+            "tab_timeseries": tab_timeseries, "tab_text": tab_text, "tab_cluster": tab_cluster,
+            "num_clusters": num_clusters, "md_cluster_summary": md_cluster_summary,
+            "plot_cluster": plot_cluster, "plot_elbow": plot_elbow,
+        }
+
+        # ======================================================================
+        # 2. REGISTER EVENT HANDLERS
+        # Now that components is a guaranteed dictionary, this will work.
+        # ======================================================================
 
         # --- Primary Analysis Chain ---
-        # Event 1: User clicks "Generate". This triggers the fast, initial analysis.
         analysis_complete_event = components["analyze_button"].click(
             fn=callbacks.run_initial_analysis,
             inputs=[components["upload_button"]],
             outputs=[components["state_analyzer"]]
         )
-
-        # Event 2: Chained to the success of Event 1. This triggers the slower,
-        #          asynchronous report and visual generation.
         analysis_complete_event.then(
             fn=callbacks.generate_reports_and_visuals,
             inputs=[components["state_analyzer"]],
-            outputs=components  # Maps the returned dict to the components dict
+            outputs=components
         )
 
         # --- Interactive Explorer Callbacks ---
@@ -70,43 +138,26 @@ def main():
             inputs=[components["state_analyzer"], components["dd_hist_col"]],
             outputs=[components["plot_histogram"]]
         )
-
-        # For the scatter plot, we need to listen to changes on all three dropdowns.
         scatter_inputs = [
-            components["state_analyzer"],
-            components["dd_scatter_x"],
-            components["dd_scatter_y"],
-            components["dd_scatter_color"]
+            components["state_analyzer"], components["dd_scatter_x"], 
+            components["dd_scatter_y"], components["dd_scatter_color"]
         ]
         for dropdown in [components["dd_scatter_x"], components["dd_scatter_y"], components["dd_scatter_color"]]:
             dropdown.change(
-                fn=callbacks.create_scatterplot,
-                inputs=scatter_inputs,
-                outputs=[components["plot_scatter"]]
+                fn=callbacks.create_scatterplot, inputs=scatter_inputs, outputs=[components["plot_scatter"]]
             )
 
         # --- Specialized Module 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"]
-            ]
+            outputs=[components["plot_cluster"], components["plot_elbow"], components["md_cluster_summary"]]
         )
     
-    # 3. Launch the application server. This line is outside the 'with' block
-    #    but is called on the 'demo' object that was defined by it. The server
-    #    will now run indefinitely, listening for requests.
+    # 3. Launch the application server
     demo.launch(debug=False, server_name="0.0.0.0")
 
 
 # --- Application Entry Point ---
-# This is the most critical part for a runnable script. The `if __name__ == "__main__":`
-# check ensures that the `main()` function is called only when the script is
-# executed directly (not when it's imported by another script). Without this
-# block, the application will define the 'main' function but never run it,
-# leading to the "application not initialized" error.
 if __name__ == "__main__":
     main()