Upload app.py

app.py

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+import gradio as gr
+import hopsworks
+import pandas as pd
+import keras
+
+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 = keras.models.load_model(model_dir + '/wine_model.keras')
+print("Model downloaded")
+
+
+def wine(type, alcohol, density, sugar, vol_acid, chlorides, total_sulfur):
+    print("Calling function")
+    type = 0 if type == 'White' else 1
+
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    df = pd.DataFrame([[type, vol_acid, sugar, chlorides, total_sulfur, density, alcohol]],
+                      columns=['type', 'volatile acidity', 'sugar', 'chlorides', 'total sulfur dioxide', '´density', 'alcohol'])
+    print("Predicting")
+    # 'res' is a list of predictions returned as the label.
+    wine_prediction = model.predict(df)
+    wine_prediction = {'High Quality': float(wine_prediction[0][2]),
+                       'Good Quality': float(wine_prediction[0][1]),
+                       'Low Quality': float(wine_prediction[0][0])}
+    return wine_prediction
+
+
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Quality Analytics",
+    description="Experiment with wine contents to predict what quality of wine it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.Radio(["White", "Red"], value='White', label="What kind of wine is it?"),
+        gr.Number(value=9.6, label="Alcohol content (%), normal range 8-15 %"),
+        gr.Number(value=0.9935, label="Density (kg/dm^3), normal range 0.99-0.101 kg/dm^3"),
+        gr.Number(value=1.1, label="Residual sugar content (g/L), normal range 0.6-66 g/L"),
+        gr.Number(value=0.26, label="Volatile acid content (g/L), normal range 0.1 - 1.6 g/L"),
+        gr.Number(value=0.04, label="Chloride content (g/L), normal range 0.01-0.6 g/L"),
+        gr.Number(value=147, label="Total sulfur dioxide content (ppm), normal range 6-450 ppm"),
+        ],
+    outputs=gr.Label(num_top_classes=3))
+
+demo.launch(debug=True)
+