Update app.py

app.py

@@ -4,7 +4,6 @@ from sklearn.datasets import make_classification
 from sklearn.ensemble import IsolationForest
 import shap
 import matplotlib.pyplot as plt
-from itertools import combinations
 import gradio as gr
 
 # Generate synthetic data with 20 features
@@ -86,6 +85,14 @@ def get_scatter_plot(feature1, feature2):
     plt.savefig("scatter_plot.png")
     return "scatter_plot.png"
 
+def get_anomaly_samples():
+    """Returns top, middle, and bottom 10 records based on anomaly score."""
+    sorted_df = df.sort_values("Anomaly_Score", ascending=False)
+    top_10 = sorted_df.head(10).to_dict(orient="records")
+    middle_10 = sorted_df.iloc[len(sorted_df) // 2 - 5: len(sorted_df) // 2 + 5].to_dict(orient="records")
+    bottom_10 = sorted_df.tail(10).to_dict(orient="records")
+    return {"Top 10 Anomalies": top_10, "Middle 10 Records": middle_10, "Bottom 10 Scores": bottom_10}
+
 # Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Isolation Forest Anomaly Detection")
@@ -111,5 +118,11 @@ with gr.Blocks() as demo:
         scatter_image = gr.Image()
         scatter_button.click(get_scatter_plot, inputs=[feature1_dropdown, feature2_dropdown], outputs=scatter_image)
 
+    with gr.Tab("Anomaly Samples"):
+        gr.Markdown("### View Anomaly Scores")
+        anomaly_samples_button = gr.Button("Get Anomaly Samples")
+        anomaly_samples_output = gr.JSON()
+        anomaly_samples_button.click(get_anomaly_samples, outputs=anomaly_samples_output)
+
 # Launch the Gradio app
 demo.launch()