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| import gradio as gr | |
| from PIL import Image | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| project = hopsworks.login() | |
| fs = project.get_feature_store() | |
| 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, | |
| residual_sugar, | |
| chlorides, | |
| free_sulfur_dioxide, | |
| alcohol): | |
| print("Calling function") | |
| df = pd.DataFrame([[volatile_acidity, residual_sugar, chlorides, free_sulfur_dioxide, alcohol]], | |
| columns=["volatile_acidity", "residual_sugar", "chlorides", "free_sulfur_dioxide", "alcohol"]) | |
| print("Predicting") | |
| print(df) | |
| # 'res' is a list of predictions returned as the label. | |
| res = model.predict(df) | |
| # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want | |
| # the first element. | |
| print(res[0]) | |
| wine_url = "https://github.com/jordicotxet/id2223/blob/63fe7d525afa1cfb626c9fa7513e2cc886e22d41/Wine/wine_dataset/" + str(int(res[0])) + ".jpg?raw=true" | |
| print(wine_url) | |
| img = Image.open(requests.get(wine_url, stream=True).raw) | |
| return img | |
| demo = gr.Interface( | |
| fn=wine, | |
| title="Wine Quality Predictive Analytics", | |
| description="Experiment with few main wine characteristics to predict which quality it is.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.Slider(minimum=0, maximum=1.5, step=0.01, value=0.2, label="volatile acidity"), | |
| gr.Slider(minimum=0, maximum=100, step=0.1, value=5.9, label="residual sugar"), | |
| gr.Slider(minimum=0, maximum=0.5, step=0.001, value=0.046, label="chlorides"), | |
| gr.Slider(minimum=0, maximum=400, step=1, value=35, label="free_sulfur_dioxide"), | |
| gr.Slider(minimum=2, maximum=15, step=0.1, value=10.6, label="alcohol (in %)"), | |
| ], | |
| examples=[[0.5, 0.8, 0.034, 46, 9.2],[0.54, 14, 0.142, 31, 12], [0.15, 67.1, 0.035, 131, 14.1]], | |
| outputs=gr.Image(type="pil", height=400, width=400)) | |
| demo.launch(share = True) | |