Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import svm
+import gradio as gr
+from PIL import Image
+
+def calculate_score(clf):
+    xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
+    X_test = np.c_[xx.ravel(), yy.ravel()]
+    Y_test = np.logical_xor(xx.ravel() > 0, yy.ravel() > 0)
+    return clf.score(X_test, Y_test)
+
+def getColorMap(kernel, gamma):
+    # prepare the training dataset
+    np.random.seed(0)
+    X = np.random.randn(300, 2)
+    Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
+
+    # fit the model
+    clf = svm.NuSVC(kernel=kernel, gamma=gamma)
+    clf.fit(X, Y)
+    
+    #create a grid for the plotting the decision function
+    xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
+    
+    # plot the decision function for each datapoint on the grid
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    plt.imshow(
+        Z,
+        interpolation="nearest",
+        extent=(xx.min(), xx.max(), yy.min(), yy.max()),
+        aspect="auto",
+        origin="lower",
+        cmap=plt.cm.PuOr_r,
+    )
+    contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")
+    plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors='k')
+    plt.title(f"Decision function for Non-Linear SVC with the {kernel} kernel and '{gamma}' gamma ", fontsize='14')	#title
+    plt.xlabel("X",fontsize='13')	#adds a label in the x axis
+    plt.ylabel("Y",fontsize='13')	#adds a label in the y axis
+    return plt, calculate_score(clf)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Learning the XOR function: An application of Binary Classification using Non-linear SVM")
+    gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py).")
+    gr.Markdown("In this demo, we find the XOR of the inputs by learning the XOR function using Non-linear SVM.")
+    
+    xor_image = Image.open("xor.png")
+    gr.Image(xor_image, label="Table explaining the 'XOR' operator")
+                
+    gr.HTML("<hr>")
+    
+    gr.Markdown("Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters the Non-Linear SVC.")
+
+    inp1 = gr.Radio(['poly', 'rbf', 'sigmoid'], label="Kernel", info="Choose a kernel")
+    inp2 = gr.Radio(['scale', 'auto'], label="Gamma", info="Choose a gamma value")
+    btn = gr.Button(value="Submit")
+            
+    with gr.Row():
+        plot = gr.Plot(label=f"Decision function plot for Non-Linear SVC with the '{inp1}' kernel and '{inp2}' gamma ")
+        num = gr.Textbox(label="Test Accuracy")
+    
+    btn.click(getColorMap,  inputs=[inp1, inp2], outputs=[plot, num])
+
+
+if __name__ == "__main__":
+    print("hdh")
+    demo.launch()
+    print("gedhhfhf")