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| import pandas as pd | |
| from joblib import dump, load | |
| import gradio as gr | |
| rfc = load('wine_pred.joblib') | |
| def predict_quality(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, ph, sulphates, alcohol): | |
| df = pd.DataFrame.from_dict( | |
| { | |
| 'fixed acidity': fixed_acidity, | |
| 'volatile acidity': volatile_acidity, | |
| 'citric acid': citric_acid, | |
| 'residual sugar': residual_sugar, | |
| 'chlorides': chlorides, | |
| 'free sulfur dioxide': free_sulfur_dioxide, | |
| 'total sulfur dioxide': total_sulfur_dioxide, | |
| 'density': density, | |
| 'pH': ph, | |
| 'sulphates': sulphates, | |
| 'alcohol': alcohol | |
| }, orient='index').T | |
| return rfc.predict(df)[0] | |
| iface = gr.Interface( | |
| predict_quality, | |
| [ | |
| gr.inputs.Slider(0, 20), | |
| gr.inputs.Slider(0, 2), | |
| gr.inputs.Slider(0, 1), | |
| gr.inputs.Slider(0, 20), | |
| gr.inputs.Slider(0, 1), | |
| gr.inputs.Slider(0, 100), | |
| gr.inputs.Slider(0, 300), | |
| gr.inputs.Slider(0, 2), | |
| gr.inputs.Slider(0, 5), | |
| gr.inputs.Slider(0, 2), | |
| gr.inputs.Slider(0, 15) | |
| ], | |
| "label", | |
| examples=[ | |
| [7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4], | |
| ], | |
| interpretation="default", | |
| title="Wine quality regressor", | |
| description="Predict wine quality based on properties" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |