Create app.py

app.py

@@ -0,0 +1,70 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from PIL import Image
+
+from sklearn.gaussian_process import GaussianProcessClassifier
+from sklearn.gaussian_process.kernels import RBF, DotProduct
+
+
+def classify_xor_dataset(kernel_name):
+    xx, yy = np.meshgrid(np.linspace(-3, 3, 50), np.linspace(-3, 3, 50))
+    rng = np.random.RandomState(0)
+    X = rng.randn(200, 2)
+    Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
+
+    # fit the model
+    fig, ax = plt.subplots(figsize=(10, 5))
+    kernels = [1.0 * RBF(length_scale=1.15), 1.0 * DotProduct(sigma_0=1.0) ** 2]
+    kernel_idx = 0 if kernel_name == "RBF" else 1
+    kernel = kernels[kernel_idx]
+    clf = GaussianProcessClassifier(kernel=kernel, warm_start=True).fit(X, Y)
+
+    # plot the decision function for each datapoint on the grid
+    Z = clf.predict_proba(np.vstack((xx.ravel(), yy.ravel())).T)[:, 1]
+    Z = Z.reshape(xx.shape)
+
+    ax.imshow(
+        Z,
+        interpolation="nearest",
+        extent=(xx.min(), xx.max(), yy.min(), yy.max()),
+        aspect="auto",
+        origin="lower",
+        cmap=plt.cm.PuOr_r,
+    )
+    ax.contour(xx, yy, Z, levels=[0.5], linewidths=2, colors=["k"])
+    ax.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors=(0, 0, 0))
+    ax.set_xticks(())
+    ax.set_yticks(())
+    ax.axis([-3, 3, -3, 3])
+    ax.set_title(
+        "%s\n Log-Marginal-Likelihood:%.3f"
+        % (clf.kernel_, clf.log_marginal_likelihood(clf.kernel_.theta)),
+        fontsize=12,
+    )
+
+    fig.canvas.draw()
+    pil_image = Image.frombytes("RGB", fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
+    plt.close(fig)
+    return pil_image
+
+
+title = "Gaussian Process Classification on the XOR Dataset"
+description = "This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In general, stationary kernels often obtain better results. See the original scikit-learn example at https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html"
+kernel_options = ["RBF", "DotProduct"]
+iface = gr.Interface(
+    classify_xor_dataset,
+    gr.inputs.Radio(choices=kernel_options, label="Kernel"),
+    gr.outputs.Image(label="Decision Boundary", type="pil"),
+    title=title,
+    description=description,
+    theme="default",
+    layout="vertical",
+    analytics_enabled=False,
+    examples=[
+        ["RBF"],
+        ["DotProduct"],
+    ],
+)
+
+iface.launch()