decision_tree

import gradio as gr
from sklearn.datasets import load_iris, load_wine, load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier, plot_tree
import matplotlib.pyplot as plt
import pandas as pd
import io

# Load datasets into a dictionary for easy selection
DATASETS = {
    "Iris": load_iris(),
    "Wine": load_wine(),
    "Breast Cancer": load_breast_cancer()
}

def train_and_plot(dataset_name, criterion, splitter, max_depth):
    # Load chosen dataset
    data = DATASETS[dataset_name]
    X = pd.DataFrame(data.data, columns=data.feature_names)
    y = data.target

    # Split into train/test
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Create Decision Tree model
    clf = DecisionTreeClassifier(
        criterion=criterion,
        splitter=splitter,
        max_depth=None if max_depth == 0 else max_depth,
        random_state=42
    )
    clf.fit(X_train, y_train)

    # Calculate accuracy
    accuracy = clf.score(X_test, y_test)

    # Plot decision tree
    fig, ax = plt.subplots(figsize=(12, 8))
    plot_tree(clf, feature_names=data.feature_names, class_names=data.target_names, filled=True, ax=ax)
    plt.tight_layout()

    # Save plot to a file-like object
    buf = io.BytesIO()
    plt.savefig(buf, format="png")
    buf.seek(0)
    plt.close(fig)

    return f"Model Accuracy: {accuracy:.2%}", buf

# Create Gradio interface
demo = gr.Interface(
    fn=train_and_plot,
    inputs=[
        gr.Dropdown(list(DATASETS.keys()), label="Choose Dataset"),
        gr.Dropdown(["gini", "entropy", "log_loss"], label="Criterion"),
        gr.Dropdown(["best", "random"], label="Splitter"),
        gr.Slider(0, 10, step=1, value=3, label="Max Depth (0 for unlimited)")
    ],
    outputs=[
        gr.Textbox(label="Accuracy"),
        gr.Image(type="file", label="Decision Tree Plot")
    ],
    title="Interactive Decision Tree Classifier",
    description="Adjust the parameters and see how the Decision Tree changes."
)

if __name__ == "__main__":
    demo.launch()