app.py

import gradio as gr
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd

from datasets import load_dataset
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix

matplotlib.use('Agg')  # Avoid issues in some remote environments

# Pre-populate a short list of "recommended" Hugging Face datasets
# (Replace "datasorg/iris" etc. with real dataset IDs you want to showcase)
SUGGESTED_DATASETS = [
    "datasorg/iris",         # hypothetical ID
    "uciml/wine_quality-red", # example from the HF Hub
    "SKIP/ENTER_CUSTOM"      # We'll treat this as a "separator" or "prompt" for custom
]

def load_and_prepare_dataset(dataset_id, label_column, feature_columns):
    """
    Loads a dataset from the Hugging Face Hub, 
    converts it to a pandas DataFrame,
    returns X, y as NumPy arrays for modeling.
    """
    # Load only the "train" split for simplicity
    # Many datasets have "train", "test", "validation" splits
    ds = load_dataset(dataset_id, split="train")
    
    # Convert to a DataFrame for easy manipulation
    df = pd.DataFrame(ds)
    
    # Subset to selected columns
    if label_column not in df.columns:
        raise ValueError(f"Label column '{label_column}' not in dataset columns: {df.columns.to_list()}")
    
    for col in feature_columns:
        if col not in df.columns:
            raise ValueError(f"Feature column '{col}' not in dataset columns: {df.columns.to_list()}")
    
    # Split into X and y
    X = df[feature_columns].values
    y = df[label_column].values
    
    return X, y, df.columns.tolist()

def train_model(dataset_id, custom_dataset_id, label_column, feature_columns, 
                learning_rate, n_estimators, max_depth, test_size):
    """
    1. Determine final dataset ID (either from dropdown or custom text).
    2. Load dataset -> DataFrame -> X, y.
    3. Train a GradientBoostingClassifier.
    4. Generate plots & metrics (accuracy and confusion matrix).
    """

    # Decide which dataset ID to use
    if dataset_id != "SKIP/ENTER_CUSTOM":
        final_id = dataset_id
    else:
        # Use the user-supplied "custom_dataset_id"
        final_id = custom_dataset_id.strip()

    # Prepare data
    X, y, columns_available = load_and_prepare_dataset(
        final_id,
        label_column,
        feature_columns
    )
    
    # Train/test split
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_size, random_state=42
    )
    
    # Train model
    clf = GradientBoostingClassifier(
        learning_rate=learning_rate,
        n_estimators=int(n_estimators),
        max_depth=int(max_depth),
        random_state=42
    )
    clf.fit(X_train, y_train)
    
    # Evaluate
    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    cm = confusion_matrix(y_test, y_pred)

    # Plot figure
    fig, axs = plt.subplots(1, 2, figsize=(10, 4))

    # Subplot 1: Feature Importances
    importances = clf.feature_importances_
    axs[0].barh(range(len(feature_columns)), importances, color='skyblue')
    axs[0].set_yticks(range(len(feature_columns)))
    axs[0].set_yticklabels(feature_columns)
    axs[0].set_xlabel("Importance")
    axs[0].set_title("Feature Importances")

    # Subplot 2: Confusion Matrix Heatmap
    im = axs[1].imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    axs[1].set_title("Confusion Matrix")
    plt.colorbar(im, ax=axs[1])
    # Labeling
    axs[1].set_xlabel("Predicted")
    axs[1].set_ylabel("True")

    # If you want to annotate each cell:
    thresh = cm.max() / 2.0
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            color = "white" if cm[i, j] > thresh else "black"
            axs[1].text(j, i, format(cm[i, j], "d"), ha="center", va="center", color=color)

    plt.tight_layout()

    output_text = f"**Dataset used:** {final_id}\n\n"
    output_text += f"**Accuracy:** {accuracy:.3f}\n\n"
    output_text += "**Confusion Matrix** (raw counts above)."

    return output_text, fig, columns_available

def update_columns(dataset_id, custom_dataset_id):
    """
    Callback to dynamically fetch the columns from the dataset
    so the user can pick which columns to use as features/labels.
    """
    if dataset_id != "SKIP/ENTER_CUSTOM":
        final_id = dataset_id
    else:
        final_id = custom_dataset_id.strip()

    # Try to load the dataset and return columns
    try:
        ds = load_dataset(final_id, split="train")
        df = pd.DataFrame(ds)
        cols = df.columns.tolist()
        # Return as list of selectable options
        return gr.update(choices=cols), gr.update(choices=cols), f"Columns found: {cols}"
    except Exception as e:
        return gr.update(choices=[]), gr.update(choices=[]), f"Error loading {final_id}: {e}"

with gr.Blocks() as demo:
    gr.Markdown("## Train GradientBoostingClassifier on a Hugging Face dataset of your choice")
    
    with gr.Row():
        dataset_dropdown = gr.Dropdown(
            choices=SUGGESTED_DATASETS,
            value=SUGGESTED_DATASETS[0],
            label="Choose a dataset"
        )
        custom_dataset_id = gr.Textbox(label="Or enter HF dataset (user/dataset)", value="",
                                       placeholder="e.g. 'username/my_custom_dataset'")
    
    # Button to load columns from the chosen dataset
    load_cols_btn = gr.Button("Load columns")
    load_cols_info = gr.Markdown()
    
    with gr.Row():
        label_col = gr.Dropdown(choices=[], label="Label column (choose 1)")
        feature_cols = gr.CheckboxGroup(choices=[], label="Feature columns (choose 1 or more)")

    # Once columns are chosen, we can set hyperparams
    learning_rate_slider = gr.Slider(0.01, 1.0, value=0.1, step=0.01, label="learning_rate")
    n_estimators_slider = gr.Slider(50, 300, value=100, step=50, label="n_estimators")
    max_depth_slider = gr.Slider(1, 10, value=3, step=1, label="max_depth")
    test_size_slider = gr.Slider(0.1, 0.9, value=0.3, step=0.1, label="test_size (fraction)")

    train_button = gr.Button("Train & Evaluate")
    
    output_text = gr.Markdown()
    output_plot = gr.Plot()
    # We might also want to show the columns for reference post-training
    columns_return = gr.Markdown()

    # When "Load columns" is clicked, we call update_columns to fetch the dataset columns
    load_cols_btn.click(
        fn=update_columns,
        inputs=[dataset_dropdown, custom_dataset_id],
        outputs=[label_col, feature_cols, load_cols_info]
    )

    # When "Train & Evaluate" is clicked, we train the model
    train_button.click(
        fn=train_model,
        inputs=[
            dataset_dropdown,
            custom_dataset_id,
            label_col,
            feature_cols,
            learning_rate_slider,
            n_estimators_slider,
            max_depth_slider,
            test_size_slider
        ],
        outputs=[output_text, output_plot, columns_return]
    )

demo.launch()