Update matplotlib configuration

app.py

@@ -1,52 +1,44 @@
+"""
+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
+"""
+
 from sklearn.datasets import make_circles
 from sklearn.model_selection import train_test_split
 from sklearn.decomposition import PCA, KernelPCA
+import matplotlib
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
 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):
-
+    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)
     pca = PCA(n_components=n_comp)
-    kernel_pca = KernelPCA(
-        n_components=None, kernel="rbf", gamma=gamma, fit_inverse_transform=True, alpha=alpha
-    )
-
+    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)
-    )
-
+    fig, (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
-
+    return fig
 
 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=10, 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') ) 
+    btn.click(fit_plot, inputs=[p1,p2,p3], outputs=gr.Plot(label="Projecting data with PCA and Kernel PCA   ")) 
 
 demo.launch()