Update app

app.py

@@ -0,0 +1,57 @@
+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=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/wine_model.pkl")
+print("Model downloaded")
+
+def wine(_type,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,alcohol,quality):
+    print("Calling function")
+    df = pd.DataFrame([[_type,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,alcohol,quality]], 
+                      columns=['_type','fixed_acidity','volatile_acidity','citric_acid','residual_sugar','chlorides','free_sulfur_dioxide','total_sulfur_dioxide','density','pH','sulphates','alcohol','quality'])
+    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)
+    # flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
+    # img = Image.open(requests.get(flower_url, stream=True).raw)            
+    return res
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Predictive Analytics",
+    description="Experiment with wine features to predict which quality of wine it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.Number(value=0, label="_type 0 for white, 1 for red"),
+        gr.Number(value=7.0, label="fixed_acidity"),
+        gr.Number(value=0.0, label="volatile_acidity"),
+        gr.Number(value=0.0, label="citric_acid"),
+        gr.Number(value=5.0, label="residual_sugar"),
+        gr.Number(value=0.0, label="chlorides"),
+        gr.Number(value=30.0, label="free_sulfur_dioxide"),
+        gr.Number(value=115.0, label="total_sulfur_dioxide"),
+        gr.Number(value=1.0, label="density"),
+        gr.Number(value=3.0, label="pH"),
+        gr.Number(value=0.0, label="sulphates"),
+        gr.Number(value=10.0, label="alcohol")
+        ],
+    outputs=gr.outputs.Label(label="Predicted Wine Quality"),
+)
+
+demo.launch(debug=True)
+