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app.py

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+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_quality_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/wine_quality_model.pkl")
+print("Model downloaded")
+
+scaler = mr.get_model("wine_quality_scaler", version=1)
+scaler_dir = model.download()
+scaler = joblib.load(scaler_dir + "/wine_quality_scaler.pkl")
+
+print("Scaler downloaded")
+
+def wine_quality(fixed_acidity, volatile_acidity, citric_acid, residual_sugar,
+       chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density,
+       ph, sulphates, alcohol):
+    print("Calling Function...")
+    data = [fixed_acidity, volatile_acidity, citric_acid, residual_sugar,
+       chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density,
+       ph, sulphates, alcohol]
+    feature_cols = ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar',
+       'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density',
+       'ph', 'sulphates', 'alcohol']
+    
+    user_input_df = pd.DataFrame([data], columns=feature_cols)
+    
+    scaled_input = scaler.transform(user_input_df)
+    
+    prediction = model.predict(scaled_input)
+    print(prediction)
+    img = Image.open("/assets/" + str(prediction[0] + ".png"))
+    return img
+
+demo = gr.Interface(
+    fn=wine_quality,
+    title="Wine Quality Predictive Analytics",
+    description="Experiment with different wine characteristics to predict its quality.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=2.0, label="Fixed Acidity"),
+        gr.inputs.Number(default=1.0, label="Volatile Acidity"),
+        gr.inputs.Number(default=2.0, label="Citric Acid"),
+        gr.inputs.Number(default=1.0, label="Residual Sugar"),
+        gr.inputs.Number(default=1.0, label="Chlorides"),
+        gr.inputs.Number(default=1.0, label="Free Sulfur Dioxide"),
+        gr.inputs.Number(default=1.0, label="Total Sulfur Dioxide"),
+        gr.inputs.Number(default=1.0, label="Density"),
+        gr.inputs.Number(default=1.0, label="pH"),
+        gr.inputs.Number(default=1.0, label="Sulphates"),
+        gr.inputs.Number(default=1.0, label="Alcohol"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+
+demo.launch(debug=True)

requirements.txt

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+httpx==0.24.1
+hopsworks
+joblib
+scikit-learn