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README.md

@@ -1,10 +1,10 @@
 ---
-title: Wine
-emoji: ⚡
-colorFrom: gray
-colorTo: pink
+title: Iris
+emoji: 🐢
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 3.14.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

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+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+import numpy as np
+
+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):
+    print("Calling function")
+#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
+    if type == "red" or type == "RED" or type == "Red":
+        type = 1
+    elif type == "white" or type=="WHITE" or type == "White":
+        type = 0
+        
+    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 = np.round(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)
+    wine_url = "https://raw.githubusercontent.com/aym1king/serverless-intro/master/wine/wine_imgs/" + str(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 wine features to predict which quality it has.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default="white", label="type (white or red)"),
+        gr.inputs.Number(default=7.3, label="fixed acidity"),
+        gr.inputs.Number(default=0.4, label="volatile acidity"),
+        gr.inputs.Number(default=0.3, label="citric acid"),
+        gr.inputs.Number(default=5.8, label="residual sugar"),
+        gr.inputs.Number(default=0.1, label="chlorides"),
+        gr.inputs.Number(default=30, label="free sulfur dioxide"),
+        gr.inputs.Number(default=120, label="total sulfur dioxide"),
+        gr.inputs.Number(default=1.0, label="density"),
+        gr.inputs.Number(default=3.2, label="pH"),
+        gr.inputs.Number(default=0.5, label="sulphates"),
+        gr.inputs.Number(default=9.8, label="alcohol"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(debug=True)
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.3.0