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