Update app.py

app.py

@@ -3,22 +3,21 @@ from sklearn.datasets import load_iris
 from sklearn.tree import DecisionTreeClassifier
 import pandas as pd
 
-# Load iris dataset
+# Load and train model
 iris = load_iris()
 X = pd.DataFrame(iris.data, columns=iris.feature_names)
 y = iris.target
 
-# Train model
 model = DecisionTreeClassifier()
 model.fit(X, y)
 
-# Define prediction function
+# Prediction function
 def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
     input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
     pred = model.predict(input_data)[0]
     return iris.target_names[pred]
 
-# Create Gradio interface
+# Gradio interface
 iface = gr.Interface(
     fn=predict_iris,
     inputs=[
@@ -28,9 +27,10 @@ iface = gr.Interface(
         gr.Number(label="Petal Width (cm)")
     ],
     outputs=gr.Text(label="Predicted Iris Species"),
-    title="Iris Flower Classification",
-    description="Klasifikasi bunga iris menggunakan Decision Tree."
+    title="Iris Flower Classifier",
+    description="A Decision Tree model to classify Iris species based on flower measurements."
 )
 
+# Launch app with explicit host and port for Hugging Face
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch(server_name="0.0.0.0", server_port=7860)