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README.md

@@ -1,10 +1,10 @@
 ---
-title: Iris
-emoji: 🐢
-colorFrom: purple
-colorTo: green
+title: Iris Monitoring
+emoji: 💻
+colorFrom: blue
+colorTo: pink
 sdk: gradio
-sdk_version: 3.5
+sdk_version: 3.8.2
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -1,49 +1,31 @@
 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()
 
+dataset_api = project.get_dataset_api()
 
-mr = project.get_model_registry()
-model = mr.get_model("iris_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/iris_model.pkl")
-print("Model downloaded")
+dataset_api.download("Resources/images/latest_iris.png")
+dataset_api.download("Resources/images/actual_iris.png")
+dataset_api.download("Resources/images/df_recent.png")
+dataset_api.download("Resources/images/confusion_matrix.png")
 
-def iris(sepal_length, sepal_width, petal_length, petal_width):
-    print("Calling function")
-#     df = pd.DataFrame([[sepal_length],[sepal_width],[petal_length],[petal_width]], 
-    df = pd.DataFrame([[sepal_length,sepal_width,petal_length,petal_width]], 
-                      columns=['sepal_length','sepal_width','petal_length','petal_width'])
-    print("Predicting")
-    print(df)
-    # 'res' is a list of predictions returned as the label.
-    res = 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)
-    flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
-    img = Image.open(requests.get(flower_url, stream=True).raw)            
-    return img
-        
-demo = gr.Interface(
-    fn=iris,
-    title="Iris Flower Predictive Analytics",
-    description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
-    allow_flagging="never",
-    inputs=[
-        gr.inputs.Number(default=2.0, label="sepal length (cm)"),
-        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
-        gr.inputs.Number(default=2.0, label="petal length (cm)"),
-        gr.inputs.Number(default=1.0, label="petal width (cm)"),
-        ],
-    outputs=gr.Image(type="pil"))
-
-demo.launch(debug=True)
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+          gr.Label("Today's Predicted Image")
+          input_img = gr.Image("latest_iris.png", elem_id="predicted-img")
+      with gr.Column():          
+          gr.Label("Today's Actual Image")
+          input_img = gr.Image("actual_iris.png", elem_id="actual-img")        
+    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

@@ -1,4 +1 @@
-httpx==0.24.1
 hopsworks
-joblib
-scikit-learn==1.1.1