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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(Type, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, |
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total_sulfur_dioxide, density, ph, sulphates, alcohol): |
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if Type=="red": |
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Type=1 |
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else: |
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Type=0 |
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print("Calling function") |
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df = pd.DataFrame([[Type, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, |
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total_sulfur_dioxide, density, ph, sulphates, alcohol]], |
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columns=["type", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", |
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"free_sulfur_dioxide", "total_sulfur_dioxide", "density", "ph", "sulphates", "alcohol"]) |
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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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return "Predicted quality: "+str(res[0]) |
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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 features to predict wine quality.", |
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allow_flagging="never", |
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inputs=[ |
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gr.inputs.Textbox(default="white", label="Type"), |
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gr.inputs.Number(default=0.34, label="Volatile acidity"), |
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gr.inputs.Number(default=0.32, label="Citric acid"), |
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gr.inputs.Number(default=5.4, label="Residual sugar"), |
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gr.inputs.Number(default=0.056, label="Chlorides"), |
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gr.inputs.Number(default=31.0, label="Free sulfur dioxide"), |
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gr.inputs.Number(default=116.0, label="Total sulfur dioxide"), |
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gr.inputs.Number(default=0.995, label="Density"), |
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gr.inputs.Number(default=3.2, label="pH"), |
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gr.inputs.Number(default=0.53, label="Sulphates"), |
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gr.inputs.Number(default=10.5, label="Alcohol precentage"), |
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], |
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outputs=gr.Label()) |
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demo.launch(debug=True) |
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