Update app.py

app.py

@@ -113,18 +113,21 @@ def get_anomaly_samples(input_data, n_samples, outliers_fraction):
         "Anomaly_Label": labels,
     })
 
+    # Sort by Anomaly Score in descending order
+    df = df.sort_values("Anomaly_Score", ascending=False)
+
     # Round values to 3 decimal places
     df = df.round({"Feature1": 3, "Feature2": 3, "Anomaly_Score": 3})
 
     # Top 10 anomalies
-    top_10 = df[df["Anomaly_Label"] == "Anomaly"].nlargest(10, "Anomaly_Score")
+    top_10 = df[df["Anomaly_Label"] == "Anomaly"].head(10)
 
     # Middle 10 (mixed)
     mid_start = len(df) // 2 - 5
     middle_10 = df.iloc[mid_start: mid_start + 10]
 
     # Bottom 10 normals
-    bottom_10 = df[df["Anomaly_Label"] == "Normal"].nsmallest(10, "Anomaly_Score")
+    bottom_10 = df[df["Anomaly_Label"] == "Normal"].tail(10)
 
     return top_10, middle_10, bottom_10
 
@@ -134,16 +137,28 @@ with gr.Blocks() as demo:
     gr.Markdown("## 🕵️‍♀️ Anomaly Detection App 🕵️‍♂️")
     gr.Markdown("Explore anomaly detection models, feature interactions, and anomaly examples.")
 
-    # Compare Anomaly Detection Algorithms
-    gr.Markdown("### 1. Compare Anomaly Detection Algorithms")
+    # Interactive Feature Scatter Plot
+    gr.Markdown("### 1. Interactive Feature Scatter Plot")
     input_data = gr.Radio(
         choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
         value="Moons",
         label="Dataset"
     )
+    feature_x = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature1", label="Feature 1")
+    feature_y = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature2", label="Feature 2")
     n_samples = gr.Slider(minimum=10, maximum=10000, step=25, value=500, label="Number of Samples")
-    outliers_fraction = gr.Slider(minimum=0.001, maximum=0.999, step=0.1, value=0.2, label="Fraction of Outliers")
+    scatter_plot_button = gr.Button("Generate Scatter Plot")
+    scatter_plot = gr.Plot(label="Feature Scatter Plot")
+
+    scatter_plot_button.click(
+        fn=plot_interactive_feature_scatter,
+        inputs=[input_data, feature_x, feature_y, n_samples],
+        outputs=scatter_plot,
+    )
 
+    # Compare Anomaly Detection Algorithms
+    gr.Markdown("### 2. Compare Anomaly Detection Algorithms")
+    outliers_fraction = gr.Slider(minimum=0.001, maximum=0.999, step=0.1, value=0.2, label="Fraction of Outliers")
     input_models = ["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"]
     plots = []
     with gr.Row():
@@ -164,19 +179,6 @@ with gr.Blocks() as demo:
     n_samples.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
     outliers_fraction.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
 
-    # Interactive Feature Scatter Plot
-    gr.Markdown("### 2. Interactive Feature Scatter Plot")
-    feature_x = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature1", label="Feature 1")
-    feature_y = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature2", label="Feature 2")
-    scatter_plot_button = gr.Button("Generate Scatter Plot")
-    scatter_plot = gr.Plot(label="Feature Scatter Plot")
-
-    scatter_plot_button.click(
-        fn=plot_interactive_feature_scatter,
-        inputs=[input_data, feature_x, feature_y, n_samples],
-        outputs=scatter_plot,
-    )
-
     # Anomaly Samples Tab
     gr.Markdown("### 3. Example Anomaly Records")
     top_table = gr.Dataframe(label="Top 10 Anomalies")