Add gradio app

app.py

@@ -0,0 +1,90 @@
+from sklearn.decomposition import PCA
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import stats
+import gradio as gr
+
+e = np.exp(1)
+np.random.seed(4)
+
+
+def pdf(x):
+    return 0.5 * (stats.norm(scale=0.25 / e).pdf(x) + stats.norm(scale=4 / e).pdf(x))
+
+
+y = np.random.normal(scale=0.5, size=(30000))
+x = np.random.normal(scale=0.5, size=(30000))
+z = np.random.normal(scale=0.1, size=len(x))
+
+density = pdf(x) * pdf(y)
+pdf_z = pdf(5 * z)
+
+density *= pdf_z
+
+a = x + y
+b = 2 * y
+c = a - b + z
+
+norm = np.sqrt(a.var() + b.var())
+a /= norm
+b /= norm
+
+def plot_figs(fig_num, elev, azim):
+    fig = plt.figure()
+    plt.clf()
+    ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim)
+    ax.set_position([0, 0, 0.95, 1])
+
+    ax.scatter(a[::10], b[::10], c[::10], c=density[::10], marker="+", alpha=0.4)
+    Y = np.c_[a, b, c]
+
+    # Using SciPy's SVD, this would be:
+    # _, pca_score, Vt = scipy.linalg.svd(Y, full_matrices=False)
+
+    pca = PCA(n_components=3)
+    pca.fit(Y)
+    V = pca.components_.T
+
+    x_pca_axis, y_pca_axis, z_pca_axis = 3 * V
+    x_pca_plane = np.r_[x_pca_axis[:2], -x_pca_axis[1::-1]]
+    y_pca_plane = np.r_[y_pca_axis[:2], -y_pca_axis[1::-1]]
+    z_pca_plane = np.r_[z_pca_axis[:2], -z_pca_axis[1::-1]]
+    x_pca_plane.shape = (2, 2)
+    y_pca_plane.shape = (2, 2)
+    z_pca_plane.shape = (2, 2)
+    ax.plot_surface(x_pca_plane, y_pca_plane, z_pca_plane)
+    ax.xaxis.set_ticklabels([])
+    ax.yaxis.set_ticklabels([])
+    ax.zaxis.set_ticklabels([])
+
+    plt.savefig(f"{fig_num}.png")
+
+    return fig
+
+def make_plot(plot_type):
+    if plot_type == "Very flat direction":
+        elev = -40
+        azim = -80
+        fig_num = 1
+    else:
+        elev = 30
+        azim = 20
+        fig_num = 2
+    plot_figs(fig_num, elev, azim)
+
+title = "Principal components analysis (PCA)"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown("These figures aid in illustrating how a point cloud can be \
+    very flat in one direction–which is where PCA comes in to choose a direction that is not flat.")
+
+    
+    button = gr.Radio(label="Plot type",
+                      choices=['Very flat direction', 'Not flat direction'], value='Very flat direction')
+    plot = gr.Plot(label="Plot")
+    button.change(make_plot, inputs=button, outputs=[plot])
+    demo.load(make_plot, inputs=[button], outputs=[plot])
+
+demo.launch()