app.py

# ui/layout.py

# -*- coding: utf-8 -*-
#
# PROJECT:      CognitiveEDA v5.0 - The QuantumLeap Intelligence Platform
#
# DESCRIPTION:  This module defines the entire Gradio UI structure. It is now
#               corrected to return only the dictionary of components.

import gradio as gr
from core.config import settings

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.

    Returns:
        A dictionary mapping component names to their Gradio component objects.
    """
    # 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()
            
        # 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)

    # 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,
    }

    return components