File size: 940 Bytes
6091059 6e4a161 6091059 6e4a161 6091059 6e4a161 6091059 6e4a161 6091059 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | import gradio as gr
import numpy as np
import pickle
# Load the trained model
model = pickle.load(open('model.pkl', 'rb'))
iris_labels = ['Setosa', 'Versicolor', 'Virginica']
# Define the prediction function
def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
prediction = model.predict(data)[0]
return f"Predicted Iris Species: {iris_labels[prediction]}"
# Create the Gradio interface
iface = gr.Interface(
fn=predict_iris,
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="text",
title="Iris Species Predictor",
description="Enter the flower's measurements to predict the Iris species."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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