Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+
+project = hopsworks.login(project="zeihers_mart")
+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, volatile_acidity, total_sulfur_dioxide, chlorides, density):
+    print("Calling function")
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    df = pd.DataFrame([[alcohol, volatile_acidity, total_sulfur_dioxide, chlorides, density]], 
+                      columns=["alcohol", "volatile acidity", "total sulfur dioxide", "chlorides", "density"])
+    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)
+    return res[0]
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Predictive Analytics",
+    description="Experiment with inputs to predict wine quality.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=0, label="alcohol"),
+        gr.inputs.Number(default=0, label="volatile acidity"),
+        gr.inputs.Number(default=0, label="total sulfur dioxide"),
+        gr.inputs.Number(default=0, label="chlorides"),
+        gr.inputs.Number(defualt=0, label = "density"),
+        ],
+    outputs=gr.Number(label="Prediction"))
+
+demo.launch(debug=True)
+