Add Application file

app.py

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+from time import time
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+
+from sklearn import manifold, datasets
+from sklearn.cluster import AgglomerativeClustering
+
+
+SEED = 0
+digits = datasets.load_digits()
+X, y = digits.data, digits.target
+n_samples, n_features = X.shape
+np.random.seed(SEED)
+
+import matplotlib
+matplotlib.use('Agg')
+
+
+
+def plot_clustering(linkage, dim):
+    if dim == '3D':
+        X_red = manifold.SpectralEmbedding(n_components=3).fit_transform(X)
+    else:
+        X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)
+
+    clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
+
+    t0 = time()
+    clustering.fit(X_red)
+    print("%s :\t%.2fs" % (linkage, time() - t0))
+
+    labels = clustering.labels_
+
+    x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
+    X_red = (X_red - x_min) / (x_max - x_min)
+
+    fig = go.Figure()
+   
+    for digit in digits.target_names:
+        subset = X_red[y==digit]
+        rgbas = plt.cm.nipy_spectral(labels[y == digit]/10)
+        color = [f'rgba({rgba[0]}, {rgba[1]}, {rgba[2]}, 0.8)' for rgba in rgbas]
+        if dim == '2D':
+            fig.add_trace(go.Scatter(x=subset[:,0], y=subset[:,1], mode='text', text=str(digit), textfont={'size': 16, 'color': color}))
+        elif dim == '3D':
+            fig.add_trace(go.Scatter3d(x=subset[:,0], y=subset[:,1], z=subset[:,2], mode='text', text=str(digit), textfont={'size': 16, 'color': color}))
+    
+    fig.update_traces(showlegend=False)
+
+    return fig
+
+
+title = '# Agglomerative Clustering on MNIST'
+
+description = """
+An illustration of various linkage option for [agglomerative clustering](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html) on the digits dataset.
+"""
+
+author = '''
+Created by [@Hnabil](https://huggingface.co/Hnabil) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html)
+'''
+
+with gr.Blocks(analytics_enabled=False, title=title) as demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.Markdown(author)
+    
+    with gr.Row():
+        with gr.Column():
+            linkage = gr.Radio(["ward", "average", "complete", "single"], value="average", interactive=True, label="Linkage Method")
+            dim = gr.Radio(['2D', '3D'], label='Embedding Dimensionality', value='2D')
+
+            btn = gr.Button('Submit')
+        
+        with gr.Column():
+            plot = gr.Plot(label='MNIST Embeddings')
+    
+    btn.click(plot_clustering, inputs=[linkage, dim], outputs=[plot])
+    demo.load(plot_clustering, inputs=[linkage,  dim], outputs=[plot])
+
+demo.launch()