Update app.py

app.py

@@ -5,6 +5,7 @@ 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
 np.random.seed(42)
@@ -44,24 +45,32 @@ df["Anomaly_Label"] = np.where(anomaly_labels == -1, "Anomaly", "Normal")
 explainer = shap.Explainer(iso_forest, df[columns])
 shap_values = explainer(df[columns])
 
-# SHAP Summary Plot (Global Explainability)
-shap.summary_plot(shap_values, df[columns], feature_names=columns)
+# Define functions for Gradio
 
-# SHAP Waterfall Plot for a Specific Data Point (Local Explainability)
-specific_index = df[df["Anomaly_Label"] == "Anomaly"].index[0]  # Select first anomaly
-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
-    )
-)
+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"
 
-# Scatter plots for pairwise combinations of features
-feature_combinations = list(combinations(columns[:5], 2))  # Use first 5 features for simplicity
+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"
 
-for feature1, feature2 in feature_combinations:
+def get_scatter_plot(feature1, feature2):
+    """Generates scatter plot for two features."""
     plt.figure(figsize=(8, 6))
     plt.scatter(
         df[feature1],
@@ -74,4 +83,33 @@ for feature1, feature2 in feature_combinations:
     plt.title(f"Isolation Forest - {feature1} vs {feature2}")
     plt.xlabel(feature1)
     plt.ylabel(feature2)
-    plt.show()
+    plt.savefig("scatter_plot.png")
+    return "scatter_plot.png"
+
+# Create Gradio interface
+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)
+
+# Launch the Gradio app
+demo.launch()