Wine working

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw
+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(alcohol, density, volatile_acidity, chlorides):
+    print("Calling function")
+    df = pd.DataFrame([[alcohol, density, volatile_acidity, chlorides]], columns=['alcohol', 'density', 'volatile_acidity', 'chlorides'])
+    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}").format(res)
+    print(res)
+    img = Image.new('RGB', (100, 30), color = (73, 109, 137))
+    d = ImageDraw.Draw(img)
+    d.text((10,10), "Quality: " + str(res[0]), fill=(255,255,0))
+    return img
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Predictive Analytics",
+    description="Experiment with different wine configurations.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=10, label="alcohol"),
+        gr.inputs.Number(default=0.99, label="density"),
+        gr.inputs.Number(default=0.33,label="volatile_acidity"),
+        gr.inputs.Number(default=0.056, label="chlorides"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1
+gradio==3.14