Update metadata

app.py

@@ -5,6 +5,11 @@ from collections import OrderedDict
 from sklearn.datasets import make_classification
 from sklearn.ensemble import RandomForestClassifier
 
+def compare(number1, number2):
+    if number1 > number2:
+        number2 = number1
+    return number2
+
 def do_train(random_state, n_samples, min_estimators, max_estimators):
     RANDOM_STATE = random_state
 
@@ -96,19 +101,20 @@ with gr.Blocks() as demo:
             </div>
         ''')
     gr.Markdown(model_card)
-    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py\">scikit-learn</a>")
+    gr.Markdown("Author: <a href=\"https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py\">scikit-learn</a>")
     n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples")
     random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random seed")
-    min_estimators = gr.Slider(minimum=5, maximum=300, step=5, value=15, label="Minimum Number of trees")
-    max_estimators = gr.Slider(minimum=min_estimators, maximum=300, step=5, value=150, label="Maximum Number of trees")
+    min_estimators = gr.Slider(minimum=5, maximum=300, step=5, value=15, label="Minimum number of trees")
+    max_estimators = gr.Slider(minimum=5, maximum=300, step=5, value=150, label="Maximum number of trees")
 
+    min_estimators.change(compare, [min_estimators, max_estimators], max_estimators)
     with gr.Row():
         with gr.Column():
             plot = gr.Plot()
-        
+
     n_samples.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot])
     random_state.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot])
     min_estimators.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot])
     max_estimators.change(fn=do_train, inputs=[n_samples, random_state, min_estimators, max_estimators], outputs=[plot])
 
-demo.launch()
+demo.queue().launch()