Update app.py

app.py

@@ -50,6 +50,45 @@ shap_values = explainer(df[columns])
 
 # Define functions for Gradio
 
+def get_shap_summary():
+    """Generates SHAP summary plot."""
+    plt.figure()
+    shap.summary_plot(shap_values, df[columns], feature_names=columns, show=False)
+    plt.savefig("shap_summary.png")
+    return "shap_summary.png"
+
+def get_shap_waterfall(index):
+    """Generates SHAP waterfall plot for a specific data point."""
+    specific_index = int(index)
+    plt.figure()
+    shap.waterfall_plot(
+        shap.Explanation(
+            values=shap_values.values[specific_index],
+            base_values=shap_values.base_values[specific_index],
+            data=df.iloc[specific_index],
+            feature_names=columns
+        )
+    )
+    plt.savefig("shap_waterfall.png")
+    return "shap_waterfall.png"
+
+def get_scatter_plot(feature1, feature2):
+    """Generates scatter plot for two features."""
+    plt.figure(figsize=(8, 6))
+    plt.scatter(
+        df[feature1],
+        df[feature2],
+        c=(df["Anomaly_Label"] == "Anomaly"),
+        cmap="coolwarm",
+        edgecolor="k",
+        alpha=0.7
+    )
+    plt.title(f"Isolation Forest - {feature1} vs {feature2}")
+    plt.xlabel(feature1)
+    plt.ylabel(feature2)
+    plt.savefig("scatter_plot.png")
+    return "scatter_plot.png"
+
 def get_roc_curve():
     """Generates the ROC curve plot."""
     fpr, tpr, _ = roc_curve(true_labels, -df["Anomaly_Score"])  # Use -scores as higher scores mean normal
@@ -81,6 +120,27 @@ def get_anomaly_samples():
 with gr.Blocks() as demo:
     gr.Markdown("# Isolation Forest Anomaly Detection")
     
+    with gr.Tab("SHAP Summary"):
+        gr.Markdown("### Global Explainability: SHAP Summary Plot")
+        shap_button = gr.Button("Generate SHAP Summary Plot")
+        shap_image = gr.Image()
+        shap_button.click(get_shap_summary, outputs=shap_image)
+    
+    with gr.Tab("SHAP Waterfall"):
+        gr.Markdown("### Local Explainability: SHAP Waterfall Plot")
+        index_input = gr.Number(label="Data Point Index", value=0)
+        shap_waterfall_button = gr.Button("Generate SHAP Waterfall Plot")
+        shap_waterfall_image = gr.Image()
+        shap_waterfall_button.click(get_shap_waterfall, inputs=index_input, outputs=shap_waterfall_image)
+    
+    with gr.Tab("Feature Scatter Plot"):
+        gr.Markdown("### Feature Interaction: Scatter Plot")
+        feature1_dropdown = gr.Dropdown(choices=columns, label="Feature 1")
+        feature2_dropdown = gr.Dropdown(choices=columns, label="Feature 2")
+        scatter_button = gr.Button("Generate Scatter Plot")
+        scatter_image = gr.Image()
+        scatter_button.click(get_scatter_plot, inputs=[feature1_dropdown, feature2_dropdown], outputs=scatter_image)
+
     with gr.Tab("Anomaly Samples"):
         gr.HTML("<h3 style='text-align: center; font-size: 18px; font-weight: bold;'>Top 10 Records (Anomalies)</h3>")
         top_table = gr.Dataframe(label="Top 10 Records")