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README.md

@@ -1,10 +1,10 @@
 ---
-title: Iris Monitor
-emoji: 👀
-colorFrom: indigo
+title: Wine Monitoring
+emoji: 💻
+colorFrom: blue
 colorTo: pink
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 4.4.1
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

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+#import os
+
+#os.system("python3 -m pip install --upgrade pip")
+#os.system("pip uninstall -y gradio")
+#os.system("pip install httpx==0.24.1")
+#os.system("pip install gradio==4.4.1")
+
+import gradio as gr
+from PIL import Image
+import hopsworks
+import pandas as pd
+import hopsworks
+import joblib
+import datetime
+from datetime import datetime
+import dataframe_image as dfi
+from sklearn.metrics import confusion_matrix
+from matplotlib import pyplot
+import seaborn as sns
+import requests
+from dotenv import load_dotenv, dotenv_values
+import random
+
+key_value = "KEY_VALUE"
+
+project = hopsworks.login(api_key_value=key_value)
+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")
+    
+feature_view = fs.get_feature_view(name="wine", version=1)
+batch_data = feature_view.get_batch_data()
+                                                                                
+y_pred = model.predict(batch_data)
+print(y_pred)
+print("----------------------------------------------------------------------------------")
+# Setting Offset Value    
+offset = y_pred.shape[0]
+print(f'Offset: {offset}')              # number of rows
+offset = offset -1                      # account for index value
+random_offset = random.randint(0, offset)
+
+pred_quality = y_pred[random_offset]
+
+wine_fg = fs.get_feature_group(name="wine", version=1)
+df = wine_fg.read() 
+print(df)
+true_quality = df.iloc[-random_offset]["quality"]
+
+dataset_api = project.get_dataset_api()
+dataset_api.download("Resources/images/wine_df_recent.png", overwrite=True)
+dataset_api.download("Resources/images/wine_confusion_matrix.png", overwrite=True)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Today's Predicted quality")
+          input_value = gr.Text(pred_quality)
+      with gr.Column():          
+          gr.Label("Today's Actual quality")
+          input_value = gr.Text(true_quality)        
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Recent Prediction History")
+          input_img = gr.Image("wine_df_recent.png", elem_id="recent-predictions")
+      with gr.Column():          
+          gr.Label("Confusion Maxtrix with Historical Prediction Performance")
+          input_img = gr.Image("wine_confusion_matrix.png", elem_id="confusion-matrix")        
+
+demo.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn==1.1.1
+seaborn
+dataframe-image
+gradio==4.4.1
+python-dotenv