Updated app at sön 19 nov 2023 21:55:53 CET

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from PIL import Image
+import requests
+import hopsworks
+import joblib
+import pandas as pd
+
+
+# Placeholder for loading your trained models
+# Load your models here, e.g., model_red = joblib.load('path_to_red_wine_model')
+# model_white = joblib.load('path_to_white_wine_model')
+
+def wine_quality(wine_type, fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides,
+                 free_sulfur_dioxide, total_sulfur_dioxide, density, ph, sulphates, alcohol):
+    print("Calling wine_quality() function")
+    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)
+
+    # Use the appropriate model based on the wine type
+    if wine_type == 'Red':
+        # Placeholder for prediction with the red wine model
+        # quality = model_red.predict(df)[0]
+        quality = "Red Wine Quality Prediction Placeholder"
+    else:
+        # Placeholder for prediction with the white wine model
+        # quality = model_white.predict(df)[0]
+        quality = "White Wine Quality Prediction Placeholder"
+
+    return quality
+
+
+# Define the Gradio interface
+iface = gr.Interface(fn=wine_quality,
+                     inputs=["dropdown", "number", "number", "number", "number", "number",
+                             "number", "number", "number", "number", "number", "number"],
+                     outputs="text",
+                     examples=[['Red', 7.4, 0.70, 0.00, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
+                               ['White', 7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.0010, 3.00, 0.45, 8.8]])
+
+iface.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1