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| import joblib | |
| import os | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| file_path = hf_hub_download("osanseviero/wine-quality", "sklearn_model.joblib", | |
| use_auth_token=os.environ['TOKEN']) | |
| model = joblib.load(file_path) | |
| def predict(data): | |
| return model.predict(data.to_numpy()) | |
| headers = [ | |
| "fixed acidity", | |
| "volatile acidity", | |
| "citric acid", | |
| "residual sugar", | |
| "chlorides", | |
| "free sulfur dioxide", | |
| "total sulfur dioxide", | |
| "density", | |
| "pH", | |
| "sulphates", | |
| "alcohol", | |
| ] | |
| default = [ | |
| [7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.9978, 3.51, 0.56, 9.4], | |
| [7.8, 0.88, 0, 2.6, 0.098, 25, 67, 0.9968, 3.2, 0.68, 9.8], | |
| [7.8, 0.76, 0.04, 2.3, 0.092, 15, 54, 0.997, 3.26, 0.65, 9.8], | |
| ] | |
| iface = gr.Interface( | |
| predict, | |
| title="Wine Quality predictor with SKLearn", | |
| inputs=gr.inputs.Dataframe( | |
| headers=headers, | |
| default=default, | |
| ), | |
| outputs="numpy", | |
| description="Learn how to create demos of private models at https://huggingface.co/spaces/osanseviero/tips-and-tricks" | |
| ) | |
| iface.launch() | |