Add parameter selection

app.py

@@ -1,42 +1,51 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-
 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)
 
-from sklearn.decomposition import PCA, KernelPCA
-
-pca = PCA(n_components=2)
-kernel_pca = KernelPCA(
-    n_components=None, kernel="rbf", gamma=10, fit_inverse_transform=True, alpha=0.1
-)
-
-X_test_pca = pca.fit(X_train).transform(X_test)
-X_test_kernel_pca = kernel_pca.fit(X_train).transform(X_test)
-
-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")
+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(plot_multi_tree, inputs= [p1,p2,p3], outputs= gr.Plot(label='Projecting data with PCA and Kernel PCA') ) 
+
+demo.launch()