| import gradio as gr |
| import numpy as np |
| import pickle |
|
|
| |
| model = pickle.load(open('model.pkl', 'rb')) |
| iris_labels = ['Setosa', 'Versicolor', 'Virginica'] |
|
|
| |
| 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]}" |
|
|
| |
| 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." |
| ) |
|
|
| |
| if __name__ == "__main__": |
| iface.launch() |
|
|
