should work

app.py

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+import matplotlib
+matplotlib.use('Agg')
+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+import numpy as np
+from collections import defaultdict
+import matplotlib.pyplot as plt
+import plotly.express as px
+from plotly import subplots
+import pandas as pd
+import random
+
+(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+x_data = np.concatenate([x_train, x_test])
+y_data = np.concatenate([y_train, y_test])
+num_classes = 10
+classes = [
+    "airplane",
+    "automobile",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+]
+
+clustering_model = from_pretrained_keras("johko/semantic-image-clustering")
+
+# Get the cluster probability distribution of the input images.
+clustering_probs = clustering_model.predict(x_data, batch_size=500, verbose=1)
+# Get the cluster of the highest probability.
+cluster_assignments = tf.math.argmax(clustering_probs, axis=-1).numpy()
+# Store the clustering confidence.
+# Images with the highest clustering confidence are considered the 'prototypes'
+# of the clusters.
+cluster_confidence = tf.math.reduce_max(clustering_probs, axis=-1).numpy()
+
+clusters = defaultdict(list)
+for idx, c in enumerate(cluster_assignments):
+    clusters[c].append((idx, cluster_confidence[idx]))
+
+def get_cluster_size(cluster_number: int):
+    cluster_size = len(clusters[cluster_number-1])
+    return f"Cluster #{cluster_number} consists of {cluster_size} objects"
+
+def get_images_from_cluster(cluster_number: int, num_images: int, image_mode: str):
+    position = 1
+
+    if image_mode == "Random Images from Cluster":
+        cluster_instances = clusters[cluster_number-1]
+        random.shuffle(cluster_instances)
+    else :
+        cluster_instances = sorted(clusters[cluster_number-1], key=lambda kv: kv[1], reverse=True)
+    fig = plt.figure()
+    for j in range(num_images):
+        image_idx = cluster_instances[j][0]
+        plt.subplot(1, num_images, position)
+        plt.imshow(x_data[image_idx].astype("uint8"))
+        plt.title(classes[y_data[image_idx][0]])
+        plt.axis("off")
+        position += 1
+    fig.tight_layout()
+    return fig
+    
+    # labels = []
+    # images = []
+    # for j in range(num_images):
+    #     image_idx = cluster_instances[j][0]
+    #     images.append(x_data[image_idx].astype("uint8"))
+    #     labels.append(classes[y_data[image_idx][0]])
+
+    # fig = subplots.make_subplots(rows=int(num_images/4)+1, cols=4, subplot_titles=labels)
+    # for j in range(num_images):
+    #     fig.add_trace(px.imshow(images[j]).data[0], row=int(j/4)+1, col=j%4+1)
+
+    # fig.update_xaxes(visible=False)  
+    # fig.update_yaxes(visible=False)
+    
+    # return fig
+
+
+def get_cluster_details(cluster_number: int):
+    cluster_label_counts = list()
+
+    cluster_label_counts = [0] * num_classes
+    instances = clusters[cluster_number-1]
+    for i, _ in instances:
+        cluster_label_counts[y_data[i][0]] += 1
+        
+    class_count = zip(classes, cluster_label_counts)
+    class_count_dict = dict(class_count)
+    
+    count_df = pd.Series(class_count_dict).to_frame()
+    
+    fig_pie = px.pie(count_df, values=0, names=count_df.index, title='Number of class objects in cluster')
+    return fig_pie
+
+
+def get_cluster_info(cluster_number: int, num_images: int, image_mode: str):
+    cluster_size = get_cluster_size(cluster_number)
+    img_fig = get_images_from_cluster(cluster_number, num_images, image_mode)
+    detail_fig = get_cluster_details(cluster_number)
+    
+    return [cluster_size, img_fig, detail_fig]
+
+
+
+article = """<center>
+Authors: <a href='https://twitter.com/johko990' target='_blank'>Johannes Kolbe</a> after an example by [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/) on
+<a href='https://keras.io/examples/vision/semantic_image_clustering/' target='_blank'>**keras.io**</a>"""
+
+
+
+description = """<center> 
+
+# Semantic Image Clustering
+
+This space is intended to give you insights to image clusters, created by a model trained with the [**Semantic Clustering by Adopting Nearest neighbors (SCAN)**](https://arxiv.org/abs/2005.12320)(Van Gansbeke et al., 2020) algorithm.
+
+First choose one of the 20 clusters, and how many images you want to preview from it. There are two options for the images either *Random*, which as you might guess, 
+gives you random images from the cluster or *High Similarity*, which gives you images that are similar according to the learned representations of the cluster.
+"""
+
+    
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown(description)
+    with gr.Row():
+        btn = gr.Button("Get Cluster Info")
+        with gr.Column():
+            inp = [gr.Slider(minimum=1, maximum=20, step=1, label="Select Cluster"),
+                gr.Slider(minimum=6, maximum=15, step=1, label="Number of Images to Show", value=8),
+                gr.Radio(["Random Images from Cluster", "High Similarity Images"], label="Image Choice")]
+    with gr.Row():
+        with gr.Column():
+            out1 = [gr.Text(label="Cluster Size"), gr.Plot(label="Image Examples"), gr.Plot(label="Class details")]
+    gr.Markdown(article)
+    
+    btn.click(fn=get_cluster_info, inputs=inp, outputs=out1)
+
+demo.launch()

requirements.txt

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+tensorflow >=2.6.0
+gradio == 3.0.12
+huggingface_hub
+jinja2
+matplotlib
+plotly
+pandas
+random
+numpy
+matplotlib