Create app.py

app.py

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+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()