# app.py

# -*- coding: utf-8 -*-
#
# PROJECT:      CognitiveEDA v5.4 - 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.
#
# 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)

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
        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)

        # --- 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 ---
        analysis_complete_event = components["analyze_button"].click(
            fn=callbacks.run_initial_analysis,
            inputs=[components["upload_button"]],
            outputs=[components["state_analyzer"]]
        )
        analysis_complete_event.then(
            fn=callbacks.generate_reports_and_visuals,
            inputs=[components["state_analyzer"]],
            outputs=components
        )

        # --- 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"]]
        )
    
    # 3. Launch the application server
    demo.launch(debug=False, server_name="0.0.0.0")


# --- Application Entry Point ---
if __name__ == "__main__":
    main()