Add app.py

app.py

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+import matplotlib.pyplot as plt
+from sklearn import svm, datasets
+from sklearn.inspection import DecisionBoundaryDisplay
+
+def plot_svm_classifiers():
+    # import some data to play with
+    iris = datasets.load_iris()
+    # Take the first two features. We could avoid this by using a two-dim dataset
+    X = iris.data[:, :2]
+    y = iris.target
+
+    # we create an instance of SVM and fit out data. We do not scale our
+    # data since we want to plot the support vectors
+    C = 1.0  # SVM regularization parameter
+    models = (
+        svm.SVC(kernel="linear", C=C),
+        svm.LinearSVC(C=C, max_iter=10000),
+        svm.SVC(kernel="rbf", gamma=0.7, C=C),
+        svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
+    )
+    models = (clf.fit(X, y) for clf in models)
+
+    # title for the plots
+    titles = (
+        "SVC with linear kernel",
+        "LinearSVC (linear kernel)",
+        "SVC with RBF kernel",
+        "SVC with polynomial (degree 3) kernel",
+    )
+
+    # Set-up 2x2 grid for plotting.
+    fig, sub = plt.subplots(2, 2)
+    plt.subplots_adjust(wspace=0.4, hspace=0.4)
+
+    X0, X1 = X[:, 0], X[:, 1]
+
+    for clf, title, ax in zip(models, titles, sub.flatten()):
+        disp = DecisionBoundaryDisplay.from_estimator(
+            clf,
+            X,
+            response_method="predict",
+            cmap=plt.cm.coolwarm,
+            alpha=0.8,
+            ax=ax,
+            xlabel=iris.feature_names[0],
+            ylabel=iris.feature_names[1],
+        )
+        ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
+        ax.set_xticks(())
+        ax.set_yticks(())
+        ax.set_title(title)
+    plt.axis('tight')
+    #plt.show()
+    return fig 
+
+heading = '🤗🧡🤍💙 Plot different SVM Classifiers on Iris Dataset'
+
+with gr.Blocks(title = heading, theme= 'snehilsanyal/scikit-learn') as demo:
+    gr.Markdown("# {}".format(heading))
+    gr.Markdown(
+        """
+        ### This demo visualizes different SVM Classifiers on a 2D projection
+        of the Iris dataset. The features to be considered are:
+        <b>1. Sepal length </b>
+        <b>2. Sepal width </b>
+        The SVM Classifiers used for this demo are:
+        <b>1. SVC with linear kernel </b>
+        <b>2. Linear SVC </b>
+        <b>3. SVC with RBF kernel</b>
+        <b>4. SVC with Polynomial (degree 3) kernel</b>
+        """
+    )
+    gr.Markdown('**[Demo is based on this script from scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py)**')
+    button = gr.Button(value = 'Visualize different SVM Classifiers on Iris Dataset')
+    button.click(plot_svm_classifiers, outputs = gr.Plot())
+
+demo.launch()