Create app.py

app.py

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+import gradio as gr
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+
+# Load the Iris dataset and train a model
+iris = load_iris()
+X, y = iris.data, iris.target
+clf = RandomForestClassifier()
+clf.fit(X, y)
+
+# Define the prediction function
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    prediction = clf.predict([[sepal_length, sepal_width, petal_length, petal_width]])
+    return iris.target_names[prediction[0]]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_iris,
+    inputs=[
+        gr.components.Number(label="Sepal Length (cm)"),
+        gr.components.Number(label="Sepal Width (cm)"),
+        gr.components.Number(label="Petal Length (cm)"),
+        gr.components.Number(label="Petal Width (cm)")
+    ],
+    outputs="text"
+)
+
+iface.launch()