Upload app (6).py

app (6).py

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+
+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the trained model
+model = joblib.load("iris_decision_tree.pkl")
+
+# Prediction function
+def predict_species(sepal_length, sepal_width, petal_length, petal_width):
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = model.predict(input_data)[0]
+    species = ["setosa", "versicolor", "virginica"]
+    return f"The predicted Iris species is: 🌸 {species[prediction]}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_species,
+    inputs=[
+        gr.Number(label="Sepal Length (cm)"),
+        gr.Number(label="Sepal Width (cm)"),
+        gr.Number(label="Petal Length (cm)"),
+        gr.Number(label="Petal Width (cm)")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Iris Flower Species Predictor",
+    description="Enter flower measurements to predict its species using a Decision Tree model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()