Created the demo

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs
+from sklearn.linear_model import LogisticRegression
+from sklearn.inspection import DecisionBoundaryDisplay
+
+import gradio as gr
+
+import matplotlib
+matplotlib.use('agg')
+
+def create_dataset(num_samples):
+    # make 3-class dataset for classification
+    centers = [[-5, 0], [0, 1.5], [5, -1]]
+    X, y = make_blobs(n_samples=num_samples, centers=centers, random_state=42)
+    transformation = [[0.4, 0.2], [-0.4, 1.2]]
+    X = np.dot(X, transformation)
+
+    return X, y
+
+def train_plot(multi_class, num_samples):
+
+    X, y = create_dataset(num_samples)
+    clf = LogisticRegression(
+            solver="sag", max_iter=100, random_state=42, multi_class=multi_class
+        ).fit(X, y)
+    
+    fig, ax = plt.subplots()
+    DecisionBoundaryDisplay.from_estimator(
+        clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
+    )
+    plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
+    plt.axis("tight")
+
+    colors = "bry"
+    for i, color in zip(clf.classes_, colors):
+        idx = np.where(y == i)
+        plt.scatter(
+            X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20
+        )
+
+    # Plot the three one-against-all classifiers
+    xmin, xmax = plt.xlim()
+    ymin, ymax = plt.ylim()
+    coef = clf.coef_
+    intercept = clf.intercept_
+
+    def plot_hyperplane(c, color):
+        def line(x0):
+            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
+
+        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
+
+    for i, color in zip(clf.classes_, colors):
+        plot_hyperplane(i, color)
+
+    return fig, clf.score(X, y)
+
+def plot_both(num_samples):
+    fig1, score1 = train_plot("multinomial", num_samples)
+    fig2, score2 = train_plot("ovr", num_samples)
+
+    return fig1, fig2, score1, score2
+
+title = "Plot multinomial and One-vs-Rest Logistic Regression "
+description = "Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines."
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
+    num_samples = gr.Slider(minimum=500, maximum=2000, step=500, value=500, label="Number of samples")
+
+    with gr.Row():
+        plot = gr.Plot()
+        plot2 = gr.Plot()
+    with gr.Row():
+        score1 = gr.Textbox(label="Multinomial score")
+        score2 = gr.Textbox(label="OVR score")
+
+    num_samples.change(fn=plot_both, inputs=[num_samples], outputs=[plot, plot2, score1, score2])
+    
+demo.launch(enable_queue=True)
+

requirements.txt

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+scikit-learn
+matplotlib
+numpy