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import gradio as gr |
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from PIL import Image |
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import requests |
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import hopsworks |
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import joblib |
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import pandas as pd |
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project = hopsworks.login() |
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fs = project.get_feature_store() |
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mr = project.get_model_registry() |
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model = mr.get_model("wine_model", version=1) |
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model_dir = model.download() |
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model = joblib.load(model_dir + "/wine_model.pkl") |
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print("Model downloaded") |
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def wine(fixed_acidity, volatile_acidity, citric_acid, residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,ph,sulphates,alchohol): |
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print("Calling function") |
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df = pd.DataFrame([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,ph,sulphates,alchohol]], |
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columns=['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar,chlorides','free_sulfur_dioxide','total_sulfur_dioxide','density,ph,sulphates','alchohol']) |
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print("Predicting") |
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print(df) |
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res = model.predict(df) |
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print(res) |
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image_url = "https://raw.githubusercontent.com/GGmorello/serverless-ml/main/lab1/wine/numbers/" + str(res[0]) + ".png" |
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img = Image.open(requests.get(image_url, stream=True).raw) |
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return img |
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demo = gr.Interface( |
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fn=wine, |
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title="Wine Quality Predictive Analytics", |
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description="Experiment with the wine features to predict the quality of the wine", |
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allow_flagging="never", |
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inputs=[ |
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gr.Number(default=2.0, label="fixed_acidity"), |
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gr.Number(default=1.0, label="volatile_acidity"), |
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gr.Number(default=2.0, label="citric_acid"), |
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gr.Number(default=1.0, label="residual_sugar"), |
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gr.Number(default=1.0, label="chlorides"), |
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gr.Number(default=1.0, label="free_sulfur_dioxide"), |
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gr.Number(default=1.0, label="total_sulfur_dioxide"), |
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gr.Number(default=1.0, label="density"), |
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gr.Number(default=1.0, label="ph"), |
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gr.Number(default=1.0, label="sulphates"), |
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gr.Number(default=1.0, label="alchohol") |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch(debug=True) |