Add application file

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import SGDClassifier
+from sklearn.datasets import make_blobs
+import gradio as gr 
+
+def plot_max_margin_hyperplane():
+    # we create 50 separable points
+    X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
+    # fit the model
+    clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)
+    clf.fit(X, Y)
+    # plot the line, the points, and the nearest vectors to the plane
+    xx = np.linspace(-1, 5, 10)
+    yy = np.linspace(-1, 5, 10)
+
+    X1, X2 = np.meshgrid(xx, yy)
+    Z = np.empty(X1.shape)
+    for (i, j), val in np.ndenumerate(X1):
+        x1 = val
+        x2 = X2[i, j]
+        p = clf.decision_function([[x1, x2]])
+        Z[i, j] = p[0]
+    levels = [-1.0, 0.0, 1.0]
+    linestyles = ["dashed", "solid", "dashed"]
+    colors = "k"
+    fig = plt.figure()
+    plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
+    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)
+
+    plt.axis("tight")
+    #plt.show()
+    return fig 
+
+heading = '🤗🧡🤍💙 SGD: Maximum Margin Separating Hyperplane'
+
+with gr.blocks(title = heading) as demo:
+    gr.Markdown("# {}".format(heading))
+    gr.Markdown(
+        """
+        ## This demo visualizes the maximum margin hyperplane that seperates\
+        a two-class separable dataset using a linear SVM classifier trained using SGD.
+        """
+    )
+    gr.Markdown('Demo is based on [this script](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)')
+
+    button = gr.Button(value = 'Visualize SGD Maximum Margin Hyperplane')
+    button.click(plot_max_margin_hyperplane, outputs = gr.Plot())
+
+demo.launch()