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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
from PIL import Image
import io
# Datasets
DATASETS = {
"Iris": load_iris(),
"Wine": load_wine(),
"Breast Cancer": load_breast_cancer()
}
def train_and_plot(dataset_name, criterion, splitter, max_depth):
data = DATASETS[dataset_name]
X = pd.DataFrame(data.data, columns=data.feature_names)
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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)
accuracy = clf.score(X_test, y_test)
# Plot 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()
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
img = Image.open(buf) # Convert to PIL
plt.close(fig)
return f"Model Accuracy: {accuracy:.2%}", img
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="pil", 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()
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