Update app.py

app.py

@@ -155,42 +155,63 @@ def plot_anomaly_examples(input_data, n_samples, outliers_fraction):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown(f"## 🕵️‍♀️ Interactive Anomaly Detection and Feature Scatter Plot 🕵️‍♂️")
-    
-    # Inputs for dataset selection and sample size
+    # App Title and Description
+    gr.Markdown("## 🕵️‍♀️ Anomaly Detection App 🕵️‍♂️")
+    gr.Markdown("Explore anomaly detection models, feature interactions, and anomaly examples.")
+
+    # Anomaly Detection Comparison
+    gr.Markdown("### 1. Compare Anomaly Detection Algorithms")
     input_data = gr.Radio(
         choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
         value="Moons",
         label="Dataset"
     )
     n_samples = gr.Slider(
-        minimum=100, maximum=500, step=25, value=300, label="Number of Samples"
+        minimum=10, maximum=10000, step=25, value=500, label="Number of Samples"
     )
-    
-    # Row for Interactive Feature Scatter Plot
-    gr.Markdown("### Feature Interaction: Scatter Plot")
+    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():
-        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")
+        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)
+
+    # 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 = gr.Plot(label="Interactive Feature Scatter Plot")
     scatter_plot_button.click(
         fn=plot_interactive_feature_scatter,
         inputs=[input_data, feature_x, feature_y, n_samples],
         outputs=scatter_plot,
     )
-    
-    # Row for Anomaly Examples (Optional)
-    gr.Markdown("### Anomaly Examples")
-    with gr.Row():
-        outliers_fraction = gr.Slider(
-            minimum=0.01, maximum=0.99, step=0.01, value=0.2, label="Fraction of Outliers"
-        )
-        anomaly_plot_button = gr.Button("Generate Anomaly Examples")
-    
+
+    # Anomaly Examples Visualization
+    gr.Markdown("### 3. Anomaly Examples Visualization")
+    anomaly_examples_button = gr.Button("Generate Anomaly Examples")
     anomaly_plot = gr.Plot(label="Anomaly Examples")
-    anomaly_plot_button.click(
+
+    anomaly_examples_button.click(
         fn=plot_anomaly_examples,
         inputs=[input_data, n_samples, outliers_fraction],
         outputs=anomaly_plot,