Create app.py

app.py

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+# Code source: Gaël Varoquaux
+# Modified for documentation by Jaques Grobler
+# License: BSD 3 clause
+
+import gradio as gr
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn import datasets, cluster
+from sklearn.feature_extraction.image import grid_to_graph
+from datasets import load_dataset
+
+plt.switch_backend("agg")
+
+
+# Theme from - https://huggingface.co/spaces/trl-lib/stack-llama/blob/main/app.py
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+    radius_size=gr.themes.sizes.radius_sm,
+    font=[
+        gr.themes.GoogleFont("Open Sans"),
+        "ui-sans-serif",
+        "system-ui",
+        "sans-serif",
+    ],
+)
+
+
+def do_submit(n_clusters):
+    # Load the dataset
+    dataset = load_dataset("sklearn-docs/digits", header=None)
+    # convert dataset to pandas
+    df = dataset["train"].to_pandas()
+    X = df.iloc[:, :64]
+    labels = df.iloc[:, 64]
+    images = X.values.reshape(-1, 8, 8)
+    connectivity = grid_to_graph(*images[0].shape)
+
+    agglo = cluster.FeatureAgglomeration(
+        connectivity=connectivity, n_clusters=int(n_clusters)
+    )
+    agglo.fit(X)
+    X_reduced = agglo.transform(X)
+
+    X_restored = agglo.inverse_transform(X_reduced)
+    images_restored = np.reshape(X_restored, images.shape)
+    plt.figure(1, figsize=(4, 3.5))
+    plt.clf()
+    plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.91)
+    for i in range(4):
+        plt.subplot(3, 4, i + 1)
+        plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest")
+        plt.xticks(())
+        plt.yticks(())
+        if i == 1:
+            plt.title("Original data")
+        plt.subplot(3, 4, 4 + i + 1)
+        plt.imshow(
+            images_restored[i], cmap=plt.cm.gray, vmax=16, interpolation="nearest"
+        )
+        if i == 1:
+            plt.title("Agglomerated data")
+        plt.xticks(())
+        plt.yticks(())
+
+    plt.subplot(3, 4, 10)
+    plt.imshow(
+        np.reshape(agglo.labels_, images[0].shape),
+        interpolation="nearest",
+        cmap=plt.cm.nipy_spectral,
+    )
+    plt.xticks(())
+    plt.yticks(())
+    plt.title("Labels")
+    return plt
+
+
+title = "Feature Agglomeration"
+with gr.Blocks(title=title, theme=theme) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(
+        "<b>These images show how similar features are merged together using feature agglomeration.</b>"
+    )
+    gr.Markdown(
+        "[Scikit-learn Example](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html)"
+    )
+
+    gr.Markdown(
+        "The FeatureAgglomeration uses [agglomerative clustering](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html#sklearn.cluster.AgglomerativeClustering)\
+ to group together features that look very similar, thus decreasing the number of features. It is a dimensionality reduction \
+    tool, see [Unsupervised dimensionality reduction](https://scikit-learn.org/stable/modules/unsupervised_reduction.html#data-reduction)."
+    )
+    n_clusters = gr.Slider(
+        minimum=10,
+        maximum=50,
+        label="Number of clusters",
+        info="Number of clusters for FeatureAgglomeration",
+        step=1,
+        value=32,
+    )
+
+    sub_btn = gr.Button("Submit")
+    plt_out = gr.Plot()
+
+    sub_btn.click(fn=do_submit, inputs=[n_clusters], outputs=[plt_out])
+
+
+if __name__ == "__main__":
+    demo.launch()