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# 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 |