app.py

from sklearn.datasets import make_circles
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA, KernelPCA
import gradio as gr

X, y = make_circles(n_samples=1_000, factor=0.3, noise=0.05, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)

def fit_plot(n_comp, gamma, alpha):

    pca = PCA(n_components=n_comp)
    kernel_pca = KernelPCA(
        n_components=None, kernel="rbf", gamma=gamma, fit_inverse_transform=True, alpha=alpha
    )

    X_test_pca = pca.fit(X_train).transform(X_test)
    X_test_kernel_pca = kernel_pca.fit(X_train).transform(X_test)

    fig1, (orig_data_ax, pca_proj_ax, kernel_pca_proj_ax) = plt.subplots(
        ncols=3, figsize=(14, 4)
    )

        orig_data_ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test)
        orig_data_ax.set_ylabel("Feature #1")
        orig_data_ax.set_xlabel("Feature #0")
        orig_data_ax.set_title("Testing data")

        pca_proj_ax.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=y_test)
        pca_proj_ax.set_ylabel("Principal component #1")
        pca_proj_ax.set_xlabel("Principal component #0")
        pca_proj_ax.set_title("Projection of testing data\n using PCA")

        kernel_pca_proj_ax.scatter(X_test_kernel_pca[:, 0], X_test_kernel_pca[:, 1], c=y_test)
        kernel_pca_proj_ax.set_ylabel("Principal component #1")
        kernel_pca_proj_ax.set_xlabel("Principal component #0")
        _ = kernel_pca_proj_ax.set_title("Projection of testing data\n using KernelPCA")
    
    return fig1


with gr.Blocks() as demo:
    gr.Markdown("## PCA vs Kernel PCA")
    gr.Markdown("Demo is based on the [Kernel PCA](https://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py")
    with gr.Row():
        p1 = gr.Slider(0, 10, label="Number of PCs", value=2, step=1)
        p2 = gr.Slider(0, 10, label="Kernel coefficient", value=2, step=1e-3)
        p3 = gr.Slider(0, 1, label="Hyperparameter for ridge regression", value=0.1, step=1e-3)
        btn = gr.Button(value="Submit")
        btn.click(fit_plot, inputs= [p1,p2,p3], outputs= gr.Plot(label='Projecting data with PCA and Kernel PCA') ) 

demo.launch()