| import gradio as gr |
| import pickle |
| import numpy as np |
| from sklearn.preprocessing import StandardScaler |
|
|
| |
| with open("rf_model.pkl", "rb") as f: |
| MODEL = pickle.load(f) |
|
|
| def predict(f1, f2, f3, f4, f5): |
| data = np.array([[f1, f2, f3, f4, f5]]) |
| scaled = StandardScaler().fit_transform(data) |
| return int(MODEL.predict(scaled)[0]) |
|
|
| if __name__ == "__main__": |
| demo = gr.Interface( |
| fn=predict, |
| inputs=[ |
| gr.Slider(0, 10, value=5, label="Feature 1"), |
| gr.Slider(0, 10, value=3, label="Feature 2"), |
| gr.Slider(0, 10, value=7, label="Feature 3"), |
| gr.Slider(0, 10, value=6, label="Feature 4"), |
| gr.Slider(0, 10, value=4, label="Feature 5"), |
| ], |
| outputs=gr.Textbox(label="Prediction"), |
| title="🐨 TaskMaster Job Scheduler", |
| description="Enter five features and get a RandomForest prediction.", |
| ) |
| demo.launch() |
|
|
