Create app.py

app.py

@@ -0,0 +1,96 @@
+import gradio as gr
+import numpy as np
+from functools import partial
+
+from matplotlib import pyplot as plt
+from scipy.cluster.hierarchy import dendrogram
+from sklearn.datasets import load_iris
+from sklearn.cluster import AgglomerativeClustering
+
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+model_card = f"""
+## Description
+
+This demo shows the plot of the corresponding **Dendrogram of Hierarchical Clustering** using **AgglomerativeClustering** and the dendrogram method on the Iris dataset.
+There are several metrics that use to compute the distance like `euclidean`, `l1`, `l2`, `manhattan`
+You can play around with different ``linkage criterion``. The linkage criterion determines which distance to use between sets of observations.
+Note: If `linkage criterion` is **ward**, only **euclidean** can use
+
+
+## Dataset
+
+Iris dataset
+"""
+iris = load_iris()
+X = iris.data
+
+def iter_grid(n_rows, n_cols):
+    # create a grid using gradio Block
+    for _ in range(n_rows):
+        with gr.Row():
+            for _ in range(n_cols):
+                with gr.Column():
+                    yield
+
+def plot_dendrogram(linkage_name, metric_name):
+    # Create linkage matrix and then plot the dendrogram
+    if linkage_name == "ward" and metric_name != "euclidean":
+        return None
+    # setting distance_threshold=0 ensures we compute the full tree.
+    model = AgglomerativeClustering(distance_threshold=0, n_clusters=None, metric=metric_name, linkage=linkage_name)
+
+    model = model.fit(X)
+
+    # plot the top three levels of the dendrogram
+
+    counts = np.zeros(model.children_.shape[0])
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1  # leaf node
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    linkage_matrix = np.column_stack(
+        [model.children_, model.distances_, counts]
+    ).astype(float)
+    fig, axes = plt.subplots()
+
+    dn1 = dendrogram(linkage_matrix, ax=axes, truncate_mode="level", p=3)
+    # Plot the corresponding dendrogram
+    axes.set_title(f"Hierarchical Clustering Dendrogram. Linkage criterion: {metric_name}")
+    axes.set_xlabel("Number of points in node (or index of point if no parenthesis).")
+    return fig
+
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>Hierarchical Clustering Dendrogram</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py\">scikit-learn</a>")
+    input_linkage = gr.Radio(choices=["ward", "complete", "average", "single"], value="average", label="Linkage criterion to use")
+    metrics = ["euclidean", "l1", "l2", "manhattan"]
+    counter = 0
+    for _ in iter_grid(2, 2):
+        if counter >= len(metrics):
+            break
+
+        input_metric = metrics[counter]
+        plot = gr.Plot(label=input_metric)
+        fn = partial(plot_dendrogram, metric_name=input_metric)
+        input_linkage.change(fn=fn, inputs=[input_linkage], outputs=plot)
+        counter += 1
+
+demo.launch()