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README.md

@@ -4,7 +4,7 @@ emoji: 😻
 colorFrom: blue
 colorTo: green
 sdk: gradio
-sdk_version: 4.4.0
+sdk_version: 3.14.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,59 @@
+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(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide,
+        total_sulfur_dioxide, density, ph, sulphates, alcohol):
+    print("Calling function")
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    df = pd.DataFrame([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide,
+        total_sulfur_dioxide, density, ph, sulphates, alcohol]], 
+                      columns=['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide',
+        'total_sulfur_dioxide', 'density', 'ph', 'sulphates', 'alcohol'])
+    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 img
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine Predictive Analytics",
+    description="Experiment with various wine components to predict its quality.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=0.0, label="fixed acidity"),
+        gr.inputs.Number(default=0.0, label="volatile acidity"),
+        gr.inputs.Number(default=0.0, label="citric acid"),
+        gr.inputs.Number(default=0.0, label="residual sugar"),
+        gr.inputs.Number(default=0.0, label="chlorides"),
+        gr.inputs.Number(default=0.0, label="free sulfur dioxide"),
+        gr.inputs.Number(default=0.0, label="total sulfur dioxide"),
+        gr.inputs.Number(default=0.0, label="density"),
+        gr.inputs.Number(default=0.0, label="pH"),
+        gr.inputs.Number(default=0.0, label="sulphates"),
+        gr.inputs.Number(default=0.0, label="alcohol")
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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