| import gradio as gr | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| import requests | |
| from PIL import Image | |
| project = hopsworks.login() | |
| fs = project.get_feature_store() | |
| print("trying to dl model") | |
| mr = project.get_model_registry() | |
| model = mr.get_model("wine_model", version=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/wine_model.pkl") | |
| print("Model downloaded") | |
| def wine(volatile_acidity, chlorides, density, alcohol): | |
| print("Calling wine function") | |
| df = pd.DataFrame( | |
| [[alcohol, chlorides, volatile_acidity, density]], | |
| columns=["alcohol", "chlorides", "volatile_acidity", "density"], | |
| ) | |
| print("Predicting") | |
| print(df) | |
| res = model.predict(df) | |
| print(res) | |
| return res | |
| demo = gr.Interface( | |
| fn=wine, | |
| title="Wine Quality Predictive Analytics", | |
| description="Experiment with different values for these properties", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.Number(label="Alcohol"), | |
| gr.Number(label="Chlorides"), | |
| gr.Number(label="Volatile Acidity"), | |
| gr.Number(label="Density"), | |
| ], | |
| outputs=gr.Number(label="Quality"), | |
| ) | |
| demo.launch(debug=True) | |