app.py

# app.py

# -*- coding: utf-8 -*-
#
# PROJECT:      CognitiveEDA v5.7 - The QuantumLeap Intelligence Platform
#
# 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:      5.7 (Cluster Profiling Edition)
# LAST-UPDATE:  2023-10-31 (Integrated cluster persona analysis)

import warnings
import logging
import gradio as gr

from ui import callbacks
from core.config import settings

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - [%(levelname)s] - (%(filename)s:%(lineno)d) - %(message)s'
)
warnings.filterwarnings('ignore', category=FutureWarning)

def main():
    logging.info(f"Starting {settings.APP_TITLE}")

    with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"), title=settings.APP_TITLE) as demo:
        
        # 1. DEFINE THE UI LAYOUT
        state_analyzer = gr.State()
        gr.Markdown(f"<h1>{settings.APP_TITLE}</h1>")
        with gr.Row():
            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"):
                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"):
                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()
            
            # --- 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=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)

        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
        ]
        
        # 2. REGISTER EVENT HANDLERS
        analysis_complete_event = analyze_button.click(
            fn=callbacks.run_initial_analysis,
            inputs=[upload_button],
            outputs=[state_analyzer]
        )
        analysis_complete_event.then(
            fn=callbacks.generate_reports_and_visuals,
            inputs=[state_analyzer],
            outputs=main_outputs
        )

        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])

        # --- 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")

if __name__ == "__main__":
    main()