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app.py

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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=2)
+model_dir = model.download()
+model = joblib.load(model_dir + "/wine_model.pkl")
+print("Model downloaded")
+
+def wine(alcohol, chlorides, density, type, volatil_acidity):
+    print("Calling function")
+    df = pd.DataFrame([[alcohol, chlorides, density, type, volatil_acidity]], 
+                      columns=['alcohol','chlorides','density','type','volatil_acidity'])
+    print("Predicting")
+    print(df)
+    res = model.predict(df) 
+    print(res)
+    wine_url = "https://raw.githubusercontent.com/Anniyuku/wine_quality/main/" + res[0] + ".png"
+    img = Image.open(requests.get(wine_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Predictive Analytics",
+    description="Experiment with alcohol, chlorides, density, type, volatil_acidity to predict which flower it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=9.00, label="alcohol"),
+        gr.inputs.Number(default=0.60, label="chlorides"),
+        gr.inputs.Number(default=1.00, label="density"),
+        gr.inputs.Number(default=1.00, label="type"),
+        gr.inputs.Number(default=1.00, label="volatil_acidity"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.3
+gradio==3.14