wine app

README.md

@@ -4,7 +4,7 @@ emoji: 👀
 colorFrom: purple
 colorTo: gray
 sdk: gradio
-sdk_version: 4.4.1
+sdk_version: 3.14.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,60 @@
+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=2)
+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):
+    print("Calling function")
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    df = pd.DataFrame([[type,fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,free_sulfur_dioxide,total_sulfur_dioxide,density,ph,sulphates,alcohol]], 
+                      columns=["type","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)
+    if (res==1):
+        wine_url = "https://media.istockphoto.com/id/117068556/sv/foto/bad-wine.jpg?s=2048x2048&w=is&k=20&c=wLOisv5qh9N8bp8AISRo1yP2nOjq_ouvt4sWeZ11yy0="
+    else :
+        wine_url = "https://i.ytimg.com/vi/9wFm7wTJ7JU/maxresdefault.jpg"
+    # wine_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
+    img = Image.open(requests.get(wine_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=wine,
+    title="Wine quality Predictive Analytics",
+    description="Experiment with some factors to predict what quality it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="type"),
+        gr.inputs.Number(default=7.2, label="fixed_acidity"),
+        gr.inputs.Number(default=0.33, label="volatile_acidity"),
+        gr.inputs.Number(default=0.31, label="citric_acid"),
+        gr.inputs.Number(default=5.44, label="residual_sugar"),
+        gr.inputs.Number(default=0.056, label="chlorides"),
+        gr.inputs.Number(default=30.53, label="free_sulfur_dioxide"),
+        gr.inputs.Number(default=115.74, label="total_sulfur_dioxide"),
+        gr.inputs.Number(default=0.995, label="density"),
+        gr.inputs.Number(default=3.21, label="ph"),
+        gr.inputs.Number(default=0.53, label="sulphates"),
+        gr.inputs.Number(default=10.49, label="alcohol"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)

requirements.txt

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