Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN, Birch, MeanShift
+from sklearn_extra.cluster import KMedoids
+from sklearn.mixture import GaussianMixture
+from sklearn.decomposition import PCA
+from scipy.cluster.hierarchy import linkage, dendrogram
+import matplotlib.pyplot as plt
+import skfuzzy as fuzz  # For Fuzzy C-Means
+import hdbscan
+
+def apply_clustering(algorithm, n_clusters, dataset):
+    # Read dataset
+    data = pd.read_csv(dataset.name)
+    data_matrix = data.values
+
+    # Apply dimensionality reduction for visualization
+    pca = PCA(n_components=2)
+    reduced_data = pca.fit_transform(data_matrix)
+
+    # Select clustering algorithm
+    if algorithm == "KMeans":
+        model = KMeans(n_clusters=n_clusters, random_state=42)
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "KMedoid (PAM)":
+        model = KMedoids(n_clusters=n_clusters, method='pam', random_state=42)
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "Fuzzy C-Means (FCM)":
+        # Use skfuzzy for Fuzzy C-Means
+        cntr, u, _, _, _, _, _ = fuzz.cmeans(data_matrix.T, n_clusters, 2, error=0.005, maxiter=1000)
+        labels = np.argmax(u, axis=0)  # Get the fuzzy cluster assignment
+    elif algorithm == "Agglomerative Hierarchical Clustering (AHC)":
+        model = AgglomerativeClustering(n_clusters=n_clusters)
+        labels = model.fit_predict(data_matrix)
+
+        # Dendrogram
+        Z = linkage(data_matrix, 'ward')  # 'ward' minimizes variance of merged clusters
+        plt.figure(figsize=(10, 7))
+        dendrogram(Z)
+        plt.title("Dendrogram for Agglomerative Clustering")
+        plt.xlabel("Sample index")
+        plt.ylabel("Distance")
+        plt.tight_layout()
+        plt.savefig("dendrogram.png")
+        plt.close()
+        return f"Agglomerative clustering applied successfully.", "dendrogram.png"
+    
+    elif algorithm == "BIRCH":
+        model = Birch(n_clusters=n_clusters)
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "DBSCAN":
+        model = DBSCAN(eps=0.5, min_samples=5)
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "HDBSCAN":
+        model = hdbscan.HDBSCAN(min_samples=5)
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "Mean-Shift":
+        model = MeanShift()
+        labels = model.fit_predict(data_matrix)
+    elif algorithm == "Gaussian Mixture Models (GMM)":
+        model = GaussianMixture(n_components=n_clusters)
+        model.fit(data_matrix)
+        labels = model.predict(data_matrix)
+    else:
+        return "Algorithm not supported yet.", None
+
+    # Visualization for Clustering
+    plt.figure(figsize=(8, 6))
+    plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=labels, cmap='viridis', s=50)
+    plt.colorbar(label='Cluster Label')
+    plt.title(f"Clusters Visualization ({algorithm})")
+    plt.xlabel("PCA Component 1")
+    plt.ylabel("PCA Component 2")
+    plt.tight_layout()
+    plt.savefig("clusters_plot.png")
+    plt.close()
+
+    return f"{algorithm} clustering applied successfully.", "clusters_plot.png"
+
+# Gradio Interface
+def main_interface():
+    dataset = gr.File(label="Upload Dataset (CSV format)")
+    algorithm = gr.Dropdown(
+        choices=[
+            "KMeans", 
+            "KMedoid (PAM)", 
+            "Fuzzy C-Means (FCM)", 
+            "Agglomerative Hierarchical Clustering (AHC)", 
+            "BIRCH", 
+            "DBSCAN", 
+            "HDBSCAN", 
+            "Mean-Shift", 
+            "Gaussian Mixture Models (GMM)"
+        ], 
+        label="Select Algorithm"
+    )
+    n_clusters = gr.Slider(minimum=2, maximum=10, value=3, step=1, label="Number of Clusters (for applicable algorithms)")
+
+    output_text = gr.Textbox(label="Result")
+    output_image = gr.Image(label="Cluster Visualization")
+
+    gr.Interface(
+        fn=apply_clustering, 
+        inputs=[algorithm, n_clusters, dataset], 
+        outputs=[output_text, output_image]
+    ).launch(debug=True)
+
+# Run the application
+if __name__ == "__main__":
+    main_interface()