Create app.py

app.py

@@ -0,0 +1,99 @@
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.linear_model import SGDClassifier
+from sklearn.inspection import DecisionBoundaryDisplay
+
+def predict_class(x, y):
+    iris = datasets.load_iris()
+    X = iris.data[:, :2]
+    y = iris.target
+    colors = "bry"
+    idx = np.arange(X.shape[0])
+    np.random.seed(13)
+    np.random.shuffle(idx)
+    X = X[idx]
+    y = y[idx]
+    mean = X.mean(axis=0)
+    std = X.std(axis=0)
+    X = (X - mean) / std
+    clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
+    predicted_class = clf.predict(np.array([[x, y]]))[0]
+    return iris.target_names[predicted_class]
+
+def decision_boundary(x_min, x_max, y_min, y_max):
+    iris = datasets.load_iris()
+    X = iris.data[:, :2]
+    y = iris.target
+    colors = "bry"
+    idx = np.arange(X.shape[0])
+    np.random.seed(13)
+    np.random.shuffle(idx)
+    X = X[idx]
+    y = y[idx]
+    mean = X.mean(axis=0)
+    std = X.std(axis=0)
+    X = (X - mean) / std
+    clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
+    ax = plt.gca()
+    DecisionBoundaryDisplay.from_estimator(
+        clf,
+        X,
+        cmap=plt.cm.Paired,
+        ax=ax,
+        response_method="predict",
+        xlabel=iris.feature_names[0],
+        ylabel=iris.feature_names[1],
+    )
+    plt.axis([x_min, x_max, y_min, y_max])
+    plt.xticks(fontsize=8)
+    plt.yticks(fontsize=8)
+    plt.gcf().set_size_inches(5, 4)
+    return plt.gcf()
+
+iris = datasets.load_iris()
+
+inputs = [
+    gr.inputs.Slider(0, 8, label=iris.feature_names[0], default=5.8, decimal=1),
+    gr.inputs.Slider(0, 8, label=iris.feature_names[1], default=3.5, decimal=1),
+]
+
+output = gr.outputs.Label(num_top_classes=1)
+
+title = "Iris Dataset - Decision Boundary"
+description = "Predict the class of the given data point and show the decision boundary of the SGD classifier."
+article = "<p><a href='https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html'>More about the dataset and the example</a></p>"
+examples = [
+    [
+        5.8,
+        3.5,
+    ],
+    [
+        7.2,
+        3.2,
+    ],
+    [
+        5.1,
+        2.5,
+    ],
+    [
+        4.9,
+        3.1,
+    ],
+]
+
+gr.Interface(
+    predict_class,
+    inputs,
+    output,
+    title=title,
+    description=description,
+    examples=examples,
+    theme=theme,
+    article=article,
+    layout="vertical",
+    allow_flagging=False,
+    live=True,
+    outputs=[None, decision_boundary],
+).launch()