Update app.py

app.py

@@ -1,17 +1,16 @@
 import numpy as np
 import pandas as pd
-from sklearn.datasets import make_classification
+from sklearn.datasets import make_classification, make_blobs, make_moons
 from sklearn.ensemble import IsolationForest
 from sklearn.metrics import roc_curve, auc
-import shap
 import matplotlib.pyplot as plt
 import gradio as gr
-from sklearn import svm
 from sklearn.covariance import EllipticEnvelope
 from sklearn.neighbors import LocalOutlierFactor
 from sklearn.linear_model import SGDOneClassSVM
-from sklearn.kernel_approximation import Nystroem
 from sklearn.pipeline import make_pipeline
+from sklearn.kernel_approximation import Nystroem
+from sklearn import svm
 import time
 from functools import partial
 
@@ -50,14 +49,6 @@ anomaly_labels = iso_forest.predict(df)  # -1 for anomaly, 1 for normal
 df["Anomaly_Score"] = anomaly_scores
 df["Anomaly_Label"] = np.where(anomaly_labels == -1, "Anomaly", "Normal")
 
-# Generate true labels (1 for anomaly, 0 for normal) for ROC curve
-true_labels = np.where(df["Anomaly_Label"] == "Anomaly", 1, 0)
-
-# SHAP Explainability
-explainer = shap.Explainer(iso_forest, df[columns])
-shap_values = explainer(df[columns])
-
-
 # Functions for Anomaly Detection Algorithms tab
 def train_models(input_data, outliers_fraction, n_samples, clf_name):
     """Train anomaly detection models and plot results."""
@@ -121,31 +112,28 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
 
 # Create Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Isolation Forest Anomaly Detection")
-    
-    with gr.Tab("Anomaly Detection Algorithms"):
-        gr.Markdown("## Compare Anomaly Detection Algorithms")
-        input_models = [
-            "Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"
-        ]
-        input_data = gr.Radio(
-            choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
-            value="Moons",
-            label="Dataset Type"
-        )
-        n_samples = gr.Slider(
-            minimum=100, maximum=500, step=25, value=300, label="Number of Samples"
-        )
-        outliers_fraction = gr.Slider(
-            minimum=0.1, maximum=0.9, step=0.1, value=0.2, label="Outlier Fraction"
-        )
-
-        for clf_name in input_models:
-            plot = gr.Plot(label=clf_name)
-            fn = partial(train_models, clf_name=clf_name)
-            input_data.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
-            n_samples.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
-            outliers_fraction.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
-
-# Launch the Gradio app
+    gr.Markdown("# Anomaly Detection Algorithms Comparison")
+
+    input_models = [
+        "Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"
+    ]
+    input_data = gr.Radio(
+        choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
+        value="Moons",
+        label="Dataset Type"
+    )
+    n_samples = gr.Slider(
+        minimum=100, maximum=500, step=25, value=300, label="Number of Samples"
+    )
+    outliers_fraction = gr.Slider(
+        minimum=0.1, maximum=0.9, step=0.1, value=0.2, label="Outlier Fraction"
+    )
+
+    for clf_name in input_models:
+        plot = gr.Plot(label=clf_name)
+        fn = partial(train_models, clf_name=clf_name)
+        input_data.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+        n_samples.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+        outliers_fraction.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+
 demo.launch()