app.py

import gradio as gr
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. 
 
 <b>The features to be considered are:</b>\
 \
 
 1. Sepal length (cm) \
 
 2. Sepal width (cm) \
 
 <b>The SVM Classifiers used for this demo are:</b>\
 \

 1. SVC with linear kernel \
 
 2. Linear SVC \
 
 3. SVC with RBF kernel\
 
 4. SVC with Polynomial (degree 3) kernel
 """
 )
 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()