Update app.py

app.py

@@ -1,4 +1,20 @@
-# Updated train_models function with feature name compatibility for IsolationForest
+import numpy as np
+import pandas as pd
+from sklearn.datasets import make_classification, make_blobs, make_moons
+from sklearn.ensemble import IsolationForest
+from sklearn.covariance import EllipticEnvelope
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.linear_model import SGDOneClassSVM
+from sklearn.pipeline import make_pipeline
+from sklearn.kernel_approximation import Nystroem
+from sklearn import svm
+import matplotlib.pyplot as plt
+import gradio as gr
+import time
+from functools import partial
+
+
+# Function to train and visualize anomaly detection models
 def train_models(input_data, outliers_fraction, n_samples, clf_name):
     """Train anomaly detection models and plot results."""
     n_outliers = int(outliers_fraction * n_samples)
@@ -38,7 +54,7 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     # Convert X to DataFrame if using IsolationForest to ensure feature names
     if clf_name == "Isolation Forest":
         X = pd.DataFrame(X, columns=["Feature1", "Feature2"])
-    
+
     t0 = time.time()
     clf.fit(X)
     t1 = time.time()
@@ -62,3 +78,33 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     plt.yticks(())
     plt.title(f"{clf_name} (time: {t1 - t0:.2f}s)")
     return plt
+
+
+# Gradio interface setup
+with gr.Blocks() as demo:
+    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)
+
+# Launch the app
+demo.launch()