Delete ui/layout.py

ui/layout.py

@@ -1,91 +0,0 @@
-# ui/layout.py
-import gradio as gr
-from core.config import settings
-
-def create_main_layout():
-    """Defines and returns the entire Gradio UI structure."""
-    with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"), title=settings.APP_TITLE) as demo:
-        # 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 for Time-Series, Text, and Clustering.")
-
-        # --- 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():
-            # Tab 1: AI Narrative
-            with gr.Tab("🤖 AI-Powered Strategy Report", id="tab_ai"):
-                ai_report_output = gr.Markdown("### Your AI-generated report will appear here after analysis...")
-            
-            # Tab 2: Data Profile
-            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()
-
-            # Tab 3: Overview Visuals
-            with gr.Tab("📊 Overview Visuals", id="tab_overview"):
-                with gr.Row():
-                    plot_types = gr.Plot()
-                    plot_missing = gr.Plot()
-                plot_correlation = gr.Plot()
-
-            # Tab 4: Interactive Explorer
-            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()
-
-            # Tab 5: Time-Series Analysis (Conditional)
-            with gr.Tab("⌛ Time-Series Analysis", id="tab_timeseries", visible=False) as tab_timeseries:
-                # ... layout for time series ...
-                pass # Placeholder for brevity
-                
-            # Tab 6: Text Analysis (Conditional)
-            with gr.Tab("📝 Text Analysis", id="tab_text", visible=False) as tab_text:
-                # ... layout for text ...
-                pass # Placeholder for brevity
-
-            # Tab 7: Clustering Analysis (Conditional)
-            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()
-
-        # Collect all components that need to be updated
-        # This is a bit verbose but necessary for Gradio's output mapping
-        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,
-        }
-
-        return demo, components