Update app.py

app.py

@@ -59,7 +59,7 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     t1 = time.time()
 
     # Plot
-    plt.figure(figsize=(10, 10))
+    plt.figure(figsize=(5, 5))
     if clf_name != "Local Outlier Factor":
         Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
         Z = Z.reshape(xx.shape)
@@ -91,12 +91,12 @@ with gr.Blocks() as demo:
     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="Fraction of Outliers")
     
-    # Models and their plots
+    # Models and their plots in a row
     input_models = ["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"]
     plots = []
 
-    for model_name in input_models:
-        with gr.Row():
+    with gr.Row():
+        for model_name in input_models:
             plot = gr.Plot(label=model_name)
             plots.append((model_name, plot))