Update app.py

app.py

@@ -2,162 +2,98 @@
 
 # -*- coding: utf-8 -*-
 #
-# PROJECT:      CognitiveEDA v5.4 - The QuantumLeap Intelligence Platform
+# PROJECT:      CognitiveEDA v5.5 - The QuantumLeap Intelligence Platform
 #
-# 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.
+# 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.
 #
 # SETUP:        $ pip install -r requirements.txt
 #
 # AUTHOR:       An MCP & PhD Expert in Data & AI Solutions
-# VERSION:      5.4 (Definitive Context-Aware Edition)
-# LAST-UPDATE:  2023-10-30 (Final architectural correction)
+# VERSION:      5.5 (Final API-Compliant Edition)
+# LAST-UPDATE:  2023-10-30 (Corrected multiple output handling)
 
 import warnings
 import logging
 import gradio as gr
 
-# The callback LOGIC is still neatly separated
 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 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 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. 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
+        # 1. DEFINE THE UI LAYOUT
         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)
+            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)
-
-        # --- 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()
+            with gr.Tab("🤖 AI-Powered Strategy Report"):
+                ai_report_output = gr.Markdown("### Your AI-generated report will appear here...")
+            with gr.Tab("📋 Data Profile"):
+                profile_missing_df, profile_numeric_df, profile_categorical_df = gr.DataFrame(), gr.DataFrame(), gr.DataFrame()
+            with gr.Tab("📊 Overview Visuals"):
+                with gr.Row(): plot_types, plot_missing = gr.Plot(), 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.Tab("🎨 Interactive Explorer"):
+                with gr.Row():
+                    dd_hist_col = gr.Dropdown(label="Select Column for Histogram", interactive=True)
+                    plot_histogram = gr.Plot()
+                with gr.Row():
+                    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()
+            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="Number of Clusters (K)", interactive=True)
+                        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()
+                    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.
-        # ======================================================================
+            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
+        ]
 
-        # --- Primary Analysis Chain ---
-        analysis_complete_event = components["analyze_button"].click(
+        # 3. REGISTER EVENT HANDLERS
+        analysis_complete_event = analyze_button.click(
             fn=callbacks.run_initial_analysis,
-            inputs=[components["upload_button"]],
-            outputs=[components["state_analyzer"]]
+            inputs=[upload_button],
+            outputs=[state_analyzer]
         )
         analysis_complete_event.then(
             fn=callbacks.generate_reports_and_visuals,
-            inputs=[components["state_analyzer"]],
-            outputs=components
+            inputs=[state_analyzer],
+            outputs=main_outputs  # Pass the LIST of components, not a dictionary.
         )
 
-        # --- Interactive Explorer Callbacks ---
-        components["dd_hist_col"].change(
-            fn=callbacks.create_histogram,
-            inputs=[components["state_analyzer"], components["dd_hist_col"]],
-            outputs=[components["plot_histogram"]]
-        )
-        scatter_inputs = [
-            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"]]
-            )
-
-        # --- 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"]]
-        )
+        # --- 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])
     
-    # 3. Launch the application server
     demo.launch(debug=False, server_name="0.0.0.0")
 
-
-# --- Application Entry Point ---
 if __name__ == "__main__":
     main()