Update app.py

app.py

@@ -1,82 +1,115 @@
-# app.py
+# ui/layout.py
 
 # -*- coding: utf-8 -*-
 #
 # PROJECT:      CognitiveEDA v5.0 - The QuantumLeap Intelligence Platform
 #
-# 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:      5.1 (Context-Aware Edition: Architectural Fix)
-# LAST-UPDATE:  2023-10-30 (Corrected Gradio context handling)
+# DESCRIPTION:  This module defines the entire Gradio UI structure. It is now
+#               corrected to return only the dictionary of components.
 
-import warnings
-import logging
 import gradio as gr
-
-# Import the UI layout and the callback LOGIC functions
-from ui.layout import create_main_layout
-from ui import callbacks
 from core.config import settings
 
-# --- Configuration & Setup ---
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - [%(levelname)s] - (%(filename)s:%(lineno)d) - %(message)s'
-)
-warnings.filterwarnings('ignore', category=FutureWarning)
+def create_main_layout() -> dict:
+    """
+    Defines the Gradio UI structure and returns a dictionary of its components.
 
+    This function is designed to be called within a `gr.Blocks` context.
 
-def main():
+    Returns:
+        A dictionary mapping component names to their Gradio component objects.
     """
-    Primary function to build, wire up, and launch the Gradio application.
-    """
-    logging.info(f"Starting {settings.APP_TITLE}")
-    
-    # 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.
+    # State object to hold the DataAnalyzer instance
+    state_analyzer = gr.State()
 
-        # 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"]]
-        )
+    # --- 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.")
 
-        # 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())
-        )
+    # --- 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)
 
-        # 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"]]
-        )
+    # --- 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...")
         
-        # (Add other .change() or .click() listeners for scatter plots, histograms, etc. here)
+        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()
+            
+        # Add other tabs as needed (Time-Series, Text)
+        tab_timeseries = gr.Tab("⌛ Time-Series Analysis", id="tab_timeseries", visible=False)
+        tab_text = gr.Tab("📝 Text Analysis", id="tab_text", visible=False)
 
-    # 3. Launch the application
-    demo.launch(debug=True, server_name="0.0.0.0")
-
+    # Collect all components into a dictionary for easy access
+    components = {
+        # State
+        "state_analyzer": state_analyzer,
+        # Inputs
+        "upload_button": upload_button,
+        "analyze_button": analyze_button,
+        # AI Tab
+        "ai_report_output": ai_report_output,
+        # Profile Tab
+        "profile_missing_df": profile_missing_df,
+        "profile_numeric_df": profile_numeric_df,
+        "profile_categorical_df": profile_categorical_df,
+        # Overview Tab
+        "plot_types": plot_types,
+        "plot_missing": plot_missing,
+        "plot_correlation": plot_correlation,
+        # Explorer Tab
+        "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,
+        # Conditional Tabs
+        "tab_timeseries": tab_timeseries,
+        "tab_text": tab_text,
+        "tab_cluster": tab_cluster,
+        # Clustering Tab Components
+        "num_clusters": num_clusters,
+        "md_cluster_summary": md_cluster_summary,
+        "plot_cluster": plot_cluster,
+        "plot_elbow": plot_elbow,
+    }
 
-if __name__ == "__main__":
-    main()
+    return components