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| import gradio as gr | |
| from sklearn.hub import HubLoader | |
| hub = HubLoader("risingodegua/wine-quality-model", "sklearn_model.joblib") | |
| model = hub.load() | |
| def wine_quality_predictor(X): | |
| '''Predicts the quality of wine | |
| Parameters | |
| ---------- | |
| X : numpy, list | |
| A list containing values used for prediction. | |
| Returns | |
| ------- | |
| List | |
| The list of predicted values. | |
| ''' | |
| return model.predict(X.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( | |
| wine_quality_predictor, | |
| gr.inputs.Dataframe( | |
| headers=headers, | |
| default=default, | |
| ), | |
| ["numpy"], | |
| description="Enter wine properties for prediction" | |
| ) | |
| iface.launch() | |