Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import gradio as gr
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+from PIL import Image
+from io import BytesIO
+
+# Load your dataset
+dataset = pd.read_csv('Flipcart.com Clusturing Model.csv')
+X = dataset.iloc[:, [2, 4]].values
+
+# Create a K-Means clustering model
+kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42)
+y_means = kmeans.fit_predict(X)
+
+# Function to perform clustering and return cluster labels and the cluster visualization image
+def cluster_data(age, purchase_rating):
+    features = np.array([age, purchase_rating]).reshape(1, -1)
+    cluster = kmeans.predict(features)[0]
+
+    # Scatter plot to visualize clusters
+    plt.figure(figsize=(8, 6))
+    plt.scatter(X[y_means == 0, 0], X[y_means == 0, 1], s=100, c='magenta', label='Cluster 1')
+    plt.scatter(X[y_means == 1, 0], X[y_means == 1, 1], s=100, c='blue', label='Cluster 2')
+    plt.scatter(X[y_means == 2, 0], X[y_means == 2, 1], s=100, c='red', label='Cluster 3')
+    plt.scatter(X[y_means == 3, 0], X[y_means == 3, 1], s=100, c='cyan', label='Cluster 4')
+    plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='black', label='Centroids')
+    plt.title('Cluster of Amazon users')
+    plt.xlabel('Age')
+    plt.ylabel('Purchase Rating')
+    plt.legend()
+    plt.grid(True)
+
+    # Save the plot as an image
+    image_buffer = BytesIO()
+    plt.savefig(image_buffer, format='png')
+    image_buffer.seek(0)
+
+    # Create a PIL image from the buffer
+    pil_image = Image.open(image_buffer)
+    
+    return f'Data point belongs to Cluster {cluster}', pil_image
+
+# Create a Gradio interface for the clustering model
+iface = gr.Interface(
+    fn=cluster_data,
+    inputs=[
+        gr.inputs.Number(label="Age"),
+        gr.inputs.Number(label="Purchase Rating")
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Cluster"),
+        gr.outputs.Image(label="Cluster Visualization", type="pil")
+    ],
+     examples = [[23,44],
+                [26,91],
+                 [72,5]],
+    live = True, 
+    description = " Press flag if any erroneous output comes ",
+    theme=gr.themes.Soft(),
+    title = "Flipcart User Segmentation"
+)
+
+# Launch the Gradio app
+iface.launch(inline=False)