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app.py

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+import gradio as gr
+import hopsworks
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+dataset_api = project.get_dataset_api()
+wine_pred_fg = fs.get_feature_group(name="wine_predictions", version=1)
+df = wine_pred_fg.read()
+print(df)
+latest_pred = df['prediction'].iloc[-1]
+latest_label = df['label'].iloc[-1]
+latest_pred = {'High Quality': float(latest_pred[2]),
+                'Good Quality': float(latest_pred[1]),
+                'Low Quality': float(latest_pred[0])}
+latest_label = 'Low Quality' if latest_label == 0 else 'Good Quality' if latest_label == 1 else 'High Quality'
+
+dataset_api.download("Resources/images/df_recent.png", overwrite=True)
+dataset_api.download("Resources/images/confusion_matrix.png", overwrite=True)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Today's Predicted Wine Quality")
+          gr.Label(latest_pred, num_top_classes=3, )
+      with gr.Column():          
+          gr.Label("Today's Actual Wine Quality ")
+          gr.Label(latest_label)
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Recent Prediction History")
+          input_img = gr.Image("df_recent.png", elem_id="recent-predictions")
+      with gr.Column():          
+          gr.Label("Confusion Maxtrix with Historical Prediction Performance")
+          input_img = gr.Image("confusion_matrix.png", elem_id="confusion-matrix")        
+
+demo.launch()

requirements.txt

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+hopsworks