Update app.py

app.py

@@ -148,25 +148,79 @@ def get_anomaly_samples(input_data, n_samples, outliers_fraction, model_name):
 
     return top_10, middle_10, bottom_10
 
+# Function to generate feature scatter plots
+def plot_interactive_feature_scatter(input_data, feature_x, feature_y, n_samples):
+    data, _ = prepare_data(input_data, n_samples)
+    x_data = data[:, 0] if feature_x == "Feature1" else data[:, 1]
+    y_data = data[:, 1] if feature_y == "Feature2" else data[:, 0]
+
+    # Generate scatter plot
+    plt.figure(figsize=(6, 6))
+    plt.scatter(x_data, y_data, alpha=0.8, c="blue", s=20, label="Features")
+    plt.title(f"Feature Interaction Scatter Plot - {feature_x} vs {feature_y}")
+    plt.xlabel(feature_x)
+    plt.ylabel(feature_y)
+    plt.legend()
+    return plt.gcf()
+
 # Gradio Interface
 with gr.Blocks() as demo:
+    # App Title and Description
     gr.Markdown("## 🕵️‍♀️ Anomaly Detection App 🕵️‍♂️")
+    gr.Markdown("Explore anomaly detection models, feature interactions, and anomaly examples.")
+
+    # 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")
-    model_dropdown = gr.Dropdown(choices=["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"], label="Select Model")
+    scatter_plot_button = gr.Button("Generate Scatter Plot")
+    scatter_plot = gr.Plot(label="Feature Scatter Plot")
 
-    # Anomaly Samples Output
+    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():
+        for model_name in input_models:
+            plot = gr.Plot(label=model_name)
+            plots.append((model_name, plot))
+
+    def update_anomaly_comparison(input_data, outliers_fraction, n_samples):
+        results = []
+        for clf_name, plot in plots:
+            fig = train_models(input_data, outliers_fraction, n_samples, clf_name)
+            results.append(fig)
+        return results
+
+    anomaly_inputs = [input_data, outliers_fraction, n_samples]
+    anomaly_outputs = [plot for _, plot in plots]
+    input_data.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
+    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)
+
+    # Anomaly Samples Tab
+    gr.Markdown("### 3. Example Anomaly Records")
+    model_dropdown = gr.Dropdown(choices=input_models, value="Isolation Forest", label="Select Model")
     top_table = gr.Dataframe(label="Top 10 Anomalies")
     middle_table = gr.Dataframe(label="Middle 10 Records")
     bottom_table = gr.Dataframe(label="Bottom 10 Normals")
     anomaly_samples_button = gr.Button("Show Anomaly Samples")
+
     anomaly_samples_button.click(
-        fn=get_anomaly_samples,
+        fn=get_anomaly_samples, 
         inputs=[input_data, n_samples, outliers_fraction, model_dropdown],
         outputs=[top_table, middle_table, bottom_table],
     )