app.py

import gradio as gr
import pandas as pd
import vlai_template

# Import AdaBoost core module
try:
    from src import adaboost_core
    ADABOOST_AVAILABLE = True
except ImportError as e:
    print(f"❌ AdaBoost module failed to load: {str(e)}")
    print("The demo requires scikit-learn to be installed. Please run: pip install scikit-learn>=1.3.0")
    ADABOOST_AVAILABLE = False
    adaboost_core = None

vlai_template.configure(
    project_name="AdaBoost Demo",
    year="2025",
    module="03",
    description="Interactive demonstration of AdaBoost algorithms for classification and regression tasks. Explore adaptive boosting with sequential weak learner training through dynamic parameter adjustment and comprehensive visualizations.",
    colors={
        "primary": "#FF6B35",     # Vibrant orange - represents energy and adaptability
        "accent": "#F7931E",      # Bright orange - adaptive learning accent
        "bg1": "#FFF8F0",         # Warm cream - soft, inviting background
        "bg2": "#FFE4CC",         # Light peach - gentle gradient step
        "bg3": "#FFAB73",         # Medium orange - stronger gradient element
    },
    font_family="'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif"
)

current_dataframe = None

def load_sample_data_fallback(dataset_choice="Iris"):
    """Fallback data loading function when AdaBoost is not available"""
    from sklearn.datasets import load_iris, load_wine, load_diabetes, load_breast_cancer
    import pandas as pd
    
    def sklearn_to_df(data):
        df = pd.DataFrame(data.data, columns=getattr(data, "feature_names", None))
        if df.columns.isnull().any():
            df.columns = [f"f{i}" for i in range(df.shape[1])]
        df["target"] = data.target
        return df
    
    def load_titanic_fallback():
        # Create a simple fallback Titanic dataset
        import numpy as np
        np.random.seed(42)
        n_samples = 150
        
        data = {
            'age': np.random.normal(30, 10, n_samples),
            'sex': np.random.choice([0, 1], n_samples),
            'pclass': np.random.choice([1, 2, 3], n_samples),
            'fare': np.random.exponential(20, n_samples),
            'embarked': np.random.choice([0, 1, 2], n_samples),
            'survived': np.random.choice([0, 1], n_samples)
        }
        return pd.DataFrame(data)
    
    datasets = {
        "Iris": lambda: sklearn_to_df(load_iris()),
        "Wine": lambda: sklearn_to_df(load_wine()),
        "Breast Cancer": lambda: sklearn_to_df(load_breast_cancer()),
        "Diabetes": lambda: sklearn_to_df(load_diabetes()),
        "Titanic": lambda: load_titanic_fallback(),
    }
    
    if dataset_choice not in datasets:
        raise ValueError(f"Unknown dataset: {dataset_choice}")
    return datasets[dataset_choice]()

def create_input_components_fallback(df, target_col):
    """Fallback input components creation when AdaBoost is not available"""
    feature_cols = [c for c in df.columns if c != target_col]
    components = []
    for col in feature_cols:
        data = df[col]
        if data.dtype == "object":
            uniq = sorted(map(str, data.dropna().unique()))
            if not uniq:
                uniq = ["N/A"]
            components.append(
                {"name": col, "type": "dropdown", "choices": uniq, "value": uniq[0]}
            )
        else:
            val = pd.to_numeric(data, errors="coerce").dropna().mean()
            val = 0.0 if pd.isna(val) else float(val)
            components.append(
                {
                    "name": col,
                    "type": "number",
                    "value": round(val, 3),
                    "minimum": None,
                    "maximum": None,
                }
            )
    return components

SAMPLE_DATA_CONFIG = {
    "Iris": {"target_column": "target", "problem_type": "classification"},
    "Wine": {"target_column": "target", "problem_type": "classification"},
    "Breast Cancer": {"target_column": "target", "problem_type": "classification"},
    "Diabetes": {"target_column": "target", "problem_type": "regression"},
    "Titanic": {"target_column": "survived", "problem_type": "classification"},
}

force_light_theme_js = """
() => {
  const params = new URLSearchParams(window.location.search);
  if (!params.has('__theme')) {
    params.set('__theme', 'light');
    window.location.search = params.toString();
  }
}
"""

def validate_config(df, target_col):
    if not target_col or target_col not in df.columns:
        return False, "❌ Please select a valid target column from the dropdown.", None

    target_series = df[target_col]
    unique_vals = target_series.nunique()

    if target_series.dtype == "object" or unique_vals <= min(20, len(target_series) * 0.1):
        problem_type = "classification"
        if unique_vals > 50:
            return False, f"⚠️ Too many classes ({unique_vals}). Consider another target.", None
        if target_series.isnull().any():
            return False, "⚠️ Target column has missing values. Please clean your data.", None
    else:
        problem_type = "regression"
        if unique_vals < 5:
            return False, f"⚠️ Too few unique values ({unique_vals}). Consider another target.", None

    return True, f"\n✅ Configuration is valid! Ready for {unique_vals} {'classes' if problem_type=='classification' else 'values'}.", problem_type


def get_status_message(is_sample, dataset_choice, target_col, problem_type, is_valid, validation_msg):
    if is_sample:
        return f"✅ **Selected Dataset**: {dataset_choice} | **Target**: {target_col} | **Type**: {problem_type.title()}"
    elif target_col and problem_type:
        status_icon = "✅" if is_valid else "⚠️"
        return f"{status_icon} **Custom Data** | **Target**: {target_col} | **Type**: {problem_type.title()} | {validation_msg}"
    else:
        return "📁 **Custom data uploaded!** 👆 Please select target column above to continue."


def load_and_configure_data_simple(dataset_choice="Iris"):
    global current_dataframe
    try:
        if not ADABOOST_AVAILABLE:
            # Fallback data loading without AdaBoost
            df = load_sample_data_fallback(dataset_choice)
        else:
            df = adaboost_core.load_data(None, dataset_choice)
        
        current_dataframe = df
        
        target_options = df.columns.tolist()
        cfg = SAMPLE_DATA_CONFIG.get(dataset_choice, {})
        target_col = cfg.get("target_column")
        problem_type = cfg.get("problem_type")
        
        if target_col and target_col in target_options:
            is_valid, validation_msg, detected = validate_config(df, target_col)
            if detected:
                problem_type = detected
            status_msg = get_status_message(True, dataset_choice, target_col, problem_type, is_valid, validation_msg)
        else:
            # If target_col not in options, use first column as fallback
            target_col = target_options[0] if target_options else None
            status_msg = get_status_message(True, dataset_choice, target_col, problem_type, False, "")
            
        return [df.head(5).round(2), gr.Dropdown(choices=target_options, value=target_col), status_msg]
        
    except Exception as e:
        current_dataframe = None
        return [pd.DataFrame(), gr.Dropdown(choices=[], value=None), f"❌ **Error loading data**: {str(e)} | Please try a different dataset."]


def load_and_configure_data(file_obj=None, dataset_choice="Iris"):
    global current_dataframe
    try:
        if not ADABOOST_AVAILABLE:
            # Fallback data loading without AdaBoost
            if file_obj is not None:
                # Handle file upload fallback
                if file_obj.name.endswith(".csv"):
                    df = pd.read_csv(file_obj.name)
                elif file_obj.name.endswith((".xlsx", ".xls")):
                    df = pd.read_excel(file_obj.name)
                else:
                    raise ValueError("Unsupported format. Upload CSV or Excel files.")
            else:
                df = load_sample_data_fallback(dataset_choice)
        else:
            df = adaboost_core.load_data(file_obj, dataset_choice)
        
        current_dataframe = df

        target_options = df.columns.tolist()
        is_sample = file_obj is None

        if is_sample:
            cfg = SAMPLE_DATA_CONFIG.get(dataset_choice, {})
            target_col = cfg.get("target_column")
            problem_type = cfg.get("problem_type")
        else:
            target_col, problem_type = None, None

        if target_col:
            is_valid, validation_msg, detected = validate_config(df, target_col)
            if detected:
                problem_type = detected
            status_msg = get_status_message(is_sample, dataset_choice, target_col, problem_type, is_valid, validation_msg)
        else:
            status_msg = get_status_message(is_sample, dataset_choice, target_col, problem_type, False, "")

        input_updates = [gr.update(visible=False)] * 40
        inputs_visible = gr.update(visible=False)
        input_status = "⚙️ Configure target column above to enable feature inputs."

        if target_col and problem_type and (not is_sample or is_valid):
            try:
                if ADABOOST_AVAILABLE:
                    components_info = adaboost_core.create_input_components(df, target_col)
                else:
                    components_info = create_input_components_fallback(df, target_col)
                for i in range(min(20, len(components_info))):
                    comp = components_info[i]
                    number_idx, dropdown_idx = i * 2, i * 2 + 1
                    if comp["type"] == "number":
                        upd = {"visible": True, "label": comp["name"], "value": comp["value"]}
                        if comp["minimum"] is not None:
                            upd["minimum"] = comp["minimum"]
                        if comp["maximum"] is not None:
                            upd["maximum"] = comp["maximum"]
                        input_updates[number_idx] = gr.update(**upd)
                        input_updates[dropdown_idx] = gr.update(visible=False)
                    else:
                        input_updates[number_idx] = gr.update(visible=False)
                        input_updates[dropdown_idx] = gr.update(
                            visible=True, label=comp["name"], choices=comp["choices"], value=comp["value"]
                        )
                inputs_visible = gr.update(visible=True)
                input_status = f"📝 **Ready!** Enter values for {len(components_info)} features below, then click Run prediction. | {validation_msg}"
            except Exception as e:
                input_status = f"❌ Error generating inputs: {str(e)}"

        return [df.head(5).round(2), gr.Dropdown(choices=target_options, value=target_col), status_msg] + input_updates + [inputs_visible, input_status]

    except Exception as e:
        current_dataframe = None
        empty = [pd.DataFrame(), gr.Dropdown(choices=[], value=None), f"❌ **Error loading data**: {str(e)} | Please try a different file or dataset."]
        return empty + [gr.update(visible=False)] * 40 + [gr.update(visible=False), "No data loaded."]


def update_configuration(df_preview, target_col):
    global current_dataframe
    df = current_dataframe

    if df is None or df.empty:
        return [gr.update(visible=False)] * 40 + [gr.update(visible=False), "No data available.", "No data available."]
    if not target_col:
        return [gr.update(visible=False)] * 40 + [gr.update(visible=False), "Select target column.", "Select target column."]

    try:
        is_valid, validation_msg, problem_type = validate_config(df, target_col)
        if not is_valid:
            return [gr.update(visible=False)] * 40 + [gr.update(visible=False), f"⚠️ {validation_msg}", f"⚠️ {validation_msg}"]

        if ADABOOST_AVAILABLE:
            components_info = adaboost_core.create_input_components(df, target_col)
        else:
            components_info = create_input_components_fallback(df, target_col)
        input_updates = [gr.update(visible=False)] * 40
        for i in range(min(20, len(components_info))):
            comp = components_info[i]
            number_idx, dropdown_idx = i * 2, i * 2 + 1
            if comp["type"] == "number":
                upd = {"visible": True, "label": comp["name"], "value": comp["value"]}
                if comp["minimum"] is not None:
                    upd["minimum"] = comp["minimum"]
                if comp["maximum"] is not None:
                    upd["maximum"] = comp["maximum"]
                input_updates[number_idx] = gr.update(**upd)
                input_updates[dropdown_idx] = gr.update(visible=False)
            else:
                input_updates[number_idx] = gr.update(visible=False)
                input_updates[dropdown_idx] = gr.update(
                    visible=True, label=comp["name"], choices=comp["choices"], value=comp["value"]
                )
        input_status = f"📝 Enter values for {len(components_info)} features | {validation_msg}"
        status_msg = f"✅ **Selected Dataset**: Custom Data | **Target**: {target_col} | **Type**: {problem_type.title()}"
        return input_updates + [gr.update(visible=True), input_status, status_msg]

    except Exception as e:
        return [gr.update(visible=False)] * 40 + [gr.update(visible=False), f"❌ Error: {str(e)}", f"❌ Error: {str(e)}"]


# AdaBoost-specific functions


def execute_prediction(df_preview, target_col, n_estimators, max_depth, learning_rate, train_test_split_ratio, show_split_info, *input_values):
    global current_dataframe
    df = current_dataframe

    EMPTY_PLOT = None
    error_style = "<div style='background:#FFF4F4;border-left:6px solid #C4314B;padding:14px 16px;border-radius:10px;'><strong>🚀 AdaBoost Process</strong><br><br>{}</div>"
    default_dropdown = gr.Dropdown(choices=["Estimator 1"], value="Estimator 1")

    # Check if AdaBoost is available
    if not ADABOOST_AVAILABLE:
        return (EMPTY_PLOT, EMPTY_PLOT, EMPTY_PLOT, error_style.format("❌ AdaBoost module is not available!<br><br>Please ensure scikit-learn is installed:<br><code>pip install scikit-learn>=1.3.0</code><br><br>Then restart the application."), default_dropdown)

    if df is None or df.empty:
        return (EMPTY_PLOT, EMPTY_PLOT, EMPTY_PLOT, error_style.format("No data available."), default_dropdown)
    if not target_col:
        return (EMPTY_PLOT, EMPTY_PLOT, EMPTY_PLOT, error_style.format("Configuration incomplete."), default_dropdown)

    is_valid, validation_msg, problem_type = validate_config(df, target_col)
    if not is_valid:
        return (EMPTY_PLOT, EMPTY_PLOT, EMPTY_PLOT, error_style.format("Configuration issue."), default_dropdown)

    try:
        if ADABOOST_AVAILABLE:
            components_info = adaboost_core.create_input_components(df, target_col)
        else:
            components_info = create_input_components_fallback(df, target_col)
        
        new_point_dict = {}
        for i, comp in enumerate(components_info):
            number_idx, dropdown_idx = i * 2, i * 2 + 1
            if comp["type"] == "number":
                v = input_values[number_idx] if number_idx < len(input_values) and input_values[number_idx] is not None else comp["value"]
            else:
                v = input_values[dropdown_idx] if dropdown_idx < len(input_values) and input_values[dropdown_idx] is not None else comp["value"]
            new_point_dict[comp["name"]] = v

        boosting_progress_fig, loss_chart_fig, importance_fig, prediction, pred_details, summary, aggregation_display = adaboost_core.run_adaboost_and_visualize(
            df, target_col, new_point_dict, n_estimators, max_depth, learning_rate, train_test_split_ratio, problem_type
        )

        feature_cols = [c for c in df.columns if c != target_col]
        first_tree_fig = adaboost_core.get_individual_tree_visualization(
            adaboost_core._get_current_model(), 0, feature_cols, problem_type
        )

        updated_tree_selector = update_tree_selector_choices(n_estimators)

        return (loss_chart_fig, first_tree_fig, importance_fig, aggregation_display, updated_tree_selector)

    except Exception as e:
        print(f"Execution error: {str(e)}")  # For debugging
        return (EMPTY_PLOT, EMPTY_PLOT, EMPTY_PLOT, error_style.format(f"Execution error: {str(e)}"), default_dropdown)


def update_tree_selector_choices(n_estimators):
    # Limit estimator visualization dropdown to 50 estimators for UI performance
    n_estimators_limited = min(int(n_estimators), 50)
    choices = [f"Estimator {i+1}" for i in range(n_estimators_limited)]
    return gr.Dropdown(choices=choices, value="Estimator 1")


def update_tree_visualization(tree_selector):
    global current_dataframe
    
    if current_dataframe is None or current_dataframe.empty:
        return None
    
    try:
        model = adaboost_core._get_current_model()
        if model is None:
            return None
        
        tree_index = int(tree_selector.split()[-1]) - 1
        _, _, problem_type = validate_config(current_dataframe, current_dataframe.columns[-1])
        feature_cols = [c for c in current_dataframe.columns if c != current_dataframe.columns[-1]]
        tree_fig = adaboost_core.get_individual_tree_visualization(model, tree_index, feature_cols, problem_type)
        
        return tree_fig
    except Exception as e:
        return None


with gr.Blocks(theme="gstaff/sketch", css=vlai_template.custom_css, fill_width=True, js=force_light_theme_js) as demo:
    vlai_template.create_header()
    
    gr.HTML(vlai_template.render_info_card(
        icon="🚀",
        title="About this AdaBoost Demo",
        description="This interactive demo showcases AdaBoost (Adaptive Boosting) algorithms for both classification and regression tasks. Explore sequential ensemble learning with adaptive reweighting of training examples through dynamic parameter adjustment and comprehensive visualizations."
    ))
    
    gr.Markdown("### 🚀 **How to Use**: Select data → Configure target → Set AdaBoost parameters → Enter new point → Run prediction!")

    with gr.Row(equal_height=False, variant="panel"):
        with gr.Column(scale=45):
            with gr.Accordion("📊 Data & Configuration", open=True):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("Start with sample datasets or upload your own CSV/Excel files.")
                        file_upload = gr.File(label="📁 Upload Your Data", file_types=[".csv", ".xlsx", ".xls"])
                    with gr.Column(scale=3):
                        sample_dataset = gr.Dropdown(choices=list(SAMPLE_DATA_CONFIG.keys()), value="Titanic", label="🗂️ Sample Datasets")

                with gr.Row():
                    target_column = gr.Dropdown(choices=[], label="🎯 Target Column", interactive=True)

                status_message = gr.Markdown("🔄 Loading sample data...")
                data_preview = gr.DataFrame(label="📋 Data Preview (First 5 Rows)", row_count=5, interactive=False, max_height=250)

            with gr.Accordion("🚀 AdaBoost Parameters & Input", open=True):
                gr.Markdown("**🚀 AdaBoost Parameters**")
                with gr.Row():
                    n_estimators = gr.Number(
                        label="Number of Estimators",
                        value=10, minimum=1, maximum=1000, precision=0,
                        info="Number of weak learners (up to 1000)"
                    )
                    learning_rate = gr.Slider(
                        label="Learning Rate",
                        value=1.0, minimum=0.0001, maximum=2.0, step=0.0001,
                        info="Step size shrinkage for each estimator"
                    )
                with gr.Row():
                    max_depth = gr.Number(
                        label="Max Depth (Base Estimator)",
                        value=1, minimum=1, maximum=10, precision=0,
                        info="Maximum depth of individual decision trees (1 = decision stumps, 2+ = deeper trees)"
                    )

                gr.Markdown("**📊 Data Split Configuration**")
                with gr.Row():
                    train_test_split_ratio = gr.Slider(
                        label="Train/Validation Split Ratio",
                        value=0.8, minimum=0.6, maximum=0.9, step=0.05,
                        info="Proportion of data used for training (e.g., 0.8 = 80% train, 20% validation)"
                    )
                    show_split_info = gr.Checkbox(
                        label="Show Split Details",
                        value=True,
                        info="Display train/validation set information"
                    )

                inputs_group = gr.Group(visible=False)
                with inputs_group:
                    input_status = gr.Markdown("Configure inputs above.")
                    gr.Markdown("**📝 New Data Point** - Enter feature values for prediction:")
                    input_components = []
                    for row in range(5):
                        with gr.Row():
                            for col in range(4):
                                idx = row * 4 + col
                                if idx < 20:
                                    number_comp = gr.Number(label=f"Feature {idx+1}", visible=False)
                                    dropdown_comp = gr.Dropdown(label=f"Feature {idx+1}", visible=False)
                                    input_components.extend([number_comp, dropdown_comp])

                run_prediction_btn = gr.Button("🚀 Run Prediction", variant="primary", size="lg")

        with gr.Column(scale=55):
            gr.Markdown("### 🚀 **AdaBoost Results & Visualization**")
            
            loss_chart = gr.Plot(label="Training/Validation Error Evolution", visible=True)
            
            with gr.Row():
                tree_selector = gr.Dropdown(
                    choices=["Estimator 1"],
                    value="Estimator 1",
                    label="🌳 Select Estimator to Visualize",
                    interactive=True
                )
            
            individual_tree_plot = gr.Plot(label="Individual Estimator Structure", visible=True)
            feature_importance_plot = gr.Plot(label="Feature Importance", visible=True)
            aggregation_display = gr.HTML("**🚀 AdaBoost Process**<br><br>AdaBoost details will appear here showing how the prediction builds up.", label="🚀 AdaBoost Process")

    gr.Markdown("""🚀 **AdaBoost Tips**:
- **📉 Error Evolution Chart**: Monitor training and validation error to understand model convergence across all estimators.
- **🌳 Individual Estimator Visualization**: Select any estimator to see its decision stump structure and contribution.
- **📊 Feature Importance**: Displays which features are most influential across all estimators.
- **🎯 Parameter Tuning**: Try different **number of estimators** (up to 1000) and **learning rate** (0.0001-2.0).
- **⚡ Learning Rate**: Default 1.0 works well; lower values create more conservative models with better generalization.
- **🌲 Decision Stumps**: Max depth 1 creates decision stumps (one split), which are ideal weak learners for AdaBoost.
- **🎯 Adaptive Reweighting**: AdaBoost focuses on misclassified examples by increasing their weights.
- **🔍 Estimator Analysis**: Use the estimator selector to understand how each decision stump contributes to predictions.
""")

    vlai_template.create_footer()

    load_evt = demo.load(
        fn=lambda: load_and_configure_data(None, "Titanic"),
        outputs=[data_preview, target_column, status_message] + input_components + [inputs_group, input_status],
    )
    upload_evt = file_upload.upload(
        fn=lambda file: load_and_configure_data(file, "Iris"),
        inputs=[file_upload],
        outputs=[data_preview, target_column, status_message] + input_components + [inputs_group, input_status],
    )

    sample_dataset.change(
        fn=lambda choice: load_and_configure_data_simple(choice),
        inputs=[sample_dataset],
        outputs=[data_preview, target_column, status_message],
    ).then(
        fn=update_configuration, inputs=[data_preview, target_column],
        outputs=input_components + [inputs_group, input_status, status_message],
    )

    target_column.change(
        fn=update_configuration, inputs=[data_preview, target_column],
        outputs=input_components + [inputs_group, input_status, status_message],
    )

    run_prediction_btn.click(
        fn=execute_prediction,
        inputs=[data_preview, target_column, n_estimators, max_depth, learning_rate, train_test_split_ratio, show_split_info] + input_components,
        outputs=[loss_chart, individual_tree_plot, feature_importance_plot, aggregation_display, tree_selector],
    )
    
    tree_selector.change(
        fn=update_tree_visualization,
        inputs=[tree_selector],
        outputs=[individual_tree_plot],
    )

if __name__ == "__main__":
    demo.launch(allowed_paths=["static/aivn_logo.png", "static/vlai_logo.png", "static"])