Create app.py
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app.py
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import gradio as gr
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import hopsworks
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import joblib
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import pandas as pd
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import requests
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from PIL import Image
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project = hopsworks.login()
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fs = project.get_feature_store()
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print("trying to dl model")
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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(volatile_acidity, chlorides, density, alcohol):
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print("Calling wine function")
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df = pd.DataFrame(
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[[alcohol, chlorides, volatile_acidity, density]],
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columns=["alcohol", "chlorides", "volatile_acidity", "density"],
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)
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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 res
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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 different values for these properties",
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allow_flagging="never",
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inputs=[
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gr.Number(label="Alcohol"),
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gr.Number(label="Chlorides"),
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gr.Number(label="Volatile Acidity"),
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gr.Number(label="Density"),
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],
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outputs=gr.Number(label="Quality"),
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)
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demo.launch(debug=True)
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