Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from io import BytesIO
+from PIL import Image
+
+# Load available datasets
+dataset_names = ["iris", "wine", "breast_cancer", "digits"]
+datasets_dict = {
+    "iris": datasets.load_iris(),
+    "wine": datasets.load_wine(),
+    "breast_cancer": datasets.load_breast_cancer(),
+    "digits": datasets.load_digits(),
+}
+
+# Define the function to visualize the decision tree
+def visualize_decision_tree(dataset_name, max_depth, min_samples_split, min_samples_leaf, max_features, criterion, splitter, max_leaf_nodes, random_state):
+    dataset = datasets_dict[dataset_name]
+    X, y = dataset.data, dataset.target
+    clf = DecisionTreeClassifier(max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, max_features=max_features, criterion=criterion, splitter=splitter, max_leaf_nodes=max_leaf_nodes, random_state=random_state)
+    clf.fit(X, y)
+    fig, ax = plt.subplots(figsize=(10, 8))
+    plot_tree(clf, feature_names=dataset.feature_names, class_names=dataset.target_names, filled=True, ax=ax)
+    buf = BytesIO()
+    fig.savefig(buf, format='png')
+    buf.seek(0)
+    image_data = buf.getvalue()
+    image = Image.open(BytesIO(image_data))
+    return image
+
+# Define the hyperparameters and their ranges
+max_depth_range = [None, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+min_samples_split_range = [2, 3, 4, 5, 6, 7, 8, 9, 10]
+min_samples_leaf_range = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+max_features_range = [None, 'sqrt', 'log2', 0.1, 0.2, 0.3, 0.4, 0.5]
+criterion_range = ['gini', 'entropy']
+splitter_range = ['best', 'random']
+max_leaf_nodes_range = [None, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+random_state_range = [None, 42, 100, 200, 300, 400, 500, 600]
+
+# Create the Gradio interface
+dataset_dropdown = gr.components.Dropdown(choices=dataset_names, label="Dataset", value="iris")
+max_depth_dropdown = gr.components.Dropdown(choices=max_depth_range, label="Max Depth", value=None)
+min_samples_split_dropdown = gr.components.Dropdown(choices=min_samples_split_range, label="Min Samples Split", value=2)
+min_samples_leaf_dropdown = gr.components.Dropdown(choices=min_samples_leaf_range, label="Min Samples Leaf", value=1)
+max_features_dropdown = gr.components.Dropdown(choices=max_features_range, label="Max Features", value=None)
+criterion_dropdown = gr.components.Dropdown(choices=criterion_range, label="Criterion", value="gini")
+splitter_dropdown = gr.components.Dropdown(choices=splitter_range, label="Splitter", value="best")
+max_leaf_nodes_dropdown = gr.components.Dropdown(choices=max_leaf_nodes_range, label="Max Leaf Nodes", value=None)
+random_state_dropdown = gr.components.Dropdown(choices=random_state_range, label="Random State", value=None)
+
+iface = gr.Interface(
+    fn=visualize_decision_tree,
+    inputs=[dataset_dropdown, max_depth_dropdown, min_samples_split_dropdown, min_samples_leaf_dropdown, max_features_dropdown, criterion_dropdown, splitter_dropdown, max_leaf_nodes_dropdown, random_state_dropdown],
+    outputs=gr.Image(type="pil"),
+    title="Decision Tree Visualization",
+    description="Visualize a decision tree classifier on various datasets by adjusting hyperparameters."
+)
+
+iface.launch()