app.py

# app.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)

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 main():
    """
    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.

        # 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)


    # 3. Launch the application
    demo.launch(debug=True, server_name="0.0.0.0")


if __name__ == "__main__":
    main()