Initial

README.md

@@ -4,7 +4,7 @@ emoji: 🏢
 colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 4.3.0
+sdk_version: 3.14.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import hopsworks
+import joblib
+import pandas as pd
+import requests
+from PIL import Image
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+print("trying to dl model")
+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(volatile_acidity, chlorides, density, alcohol):
+    print("Calling wine function")
+    df = pd.DataFrame(
+        [[alcohol, chlorides, volatile_acidity, density]],
+        columns=["alcohol", "chlorides", "volatile_acidity", "density"],
+    )
+    print("Predicting")
+    print(df)
+    res = model.predict(df)
+    print(res)
+    return res
+
+
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Quality Predictive Analytics",
+    description="Experiment with different values for these properties",
+    allow_flagging="never",
+    inputs=[
+        gr.Number(label="Alcohol"),
+        gr.Number(label="Chlorides"),
+        gr.Number(label="Volatile Acidity"),
+        gr.Number(label="Density"),
+    ],
+    outputs=gr.Number(label="Quality"),
+)
+
+demo.launch(debug=True)

requirements.txt

@@ -0,0 +1,3 @@
+hopsworks
+joblib
+scikit-learn==1.1.1