Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+import sklearn 
+
+# Load the saved model
+loaded_model = joblib.load('iris_model.sav')
+
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = loaded_model.predict(input_data)[0]
+    iris_classes = ['setosa', 'versicolor', 'virginica']
+    return iris_classes[prediction]
+
+iface = gr.Interface(
+    fn=predict_iris,
+    inputs=[
+        gr.Number(label="Sepal Length"),
+        gr.Number(label="Sepal Width"),
+        gr.Number(label="Petal Length"),
+        gr.Number(label="Petal Width")
+    ],
+    outputs=gr.Textbox(label="Predicted Iris Class"),
+    title="Iris Flower Classification",
+    description="Enter sepal and petal measurements to predict the Iris species."
+)
+
+iface.launch()