Delete iris-batch-inference-pipeline.py

iris-batch-inference-pipeline.py

@@ -1,108 +0,0 @@
-import os
-import modal
-    
-LOCAL=True
-
-if LOCAL == False:
-   stub = modal.Stub()
-   hopsworks_image = modal.Image.debian_slim().pip_install(["hopsworks","joblib","seaborn","sklearn","dataframe-image"])
-   @stub.function(image=hopsworks_image, schedule=modal.Period(days=1), secret=modal.Secret.from_name("jim-hopsworks-ai"))
-   def f():
-       g()
-
-def g():
-    import pandas as pd
-    import hopsworks
-    import joblib
-    import datetime
-    from PIL import Image
-    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
-
-    project = hopsworks.login()
-    fs = project.get_feature_store()
-    
-    mr = project.get_model_registry()
-    model = mr.get_model("iris_modal", version=1)
-    model_dir = model.download()
-    model = joblib.load(model_dir + "/iris_model.pkl")
-    
-    feature_view = fs.get_feature_view(name="iris_modal", version=1)
-    batch_data = feature_view.get_batch_data()
-    
-    y_pred = model.predict(batch_data)
-    # print(y_pred)
-    flower = y_pred[y_pred.size-1]
-    flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + flower + ".png"
-    print("Flower predicted: " + flower)
-    img = Image.open(requests.get(flower_url, stream=True).raw)            
-    img.save("./latest_iris.png")
-    dataset_api = project.get_dataset_api()    
-    dataset_api.upload("./latest_iris.png", "Resources/images", overwrite=True)
-    
-    iris_fg = fs.get_feature_group(name="iris_modal", version=1)
-    df = iris_fg.read()
-    # print(df["variety"])
-    label = df.iloc[-1]["variety"]
-    label_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + label + ".png"
-    print("Flower actual: " + label)
-    img = Image.open(requests.get(label_url, stream=True).raw)            
-    img.save("./actual_iris.png")
-    dataset_api.upload("./actual_iris.png", "Resources/images", overwrite=True)
-    
-    monitor_fg = fs.get_or_create_feature_group(name="iris_predictions",
-                                                version=1,
-                                                primary_key=["datetime"],
-                                                description="Iris flower Prediction/Outcome Monitoring"
-                                                )
-    
-    now = datetime.now().strftime("%m/%d/%Y, %H:%M:%S")
-    data = {
-        'prediction': [flower],
-        'label': [label],
-        'datetime': [now],
-       }
-    monitor_df = pd.DataFrame(data)
-    monitor_fg.insert(monitor_df, write_options={"wait_for_job" : False})
-    
-    history_df = monitor_fg.read()
-    # Add our prediction to the history, as the history_df won't have it - 
-    # the insertion was done asynchronously, so it will take ~1 min to land on App
-    history_df = pd.concat([history_df, monitor_df])
-
-
-    df_recent = history_df.tail(5)
-    dfi.export(df_recent, './df_recent.png', table_conversion = 'matplotlib')
-    dataset_api.upload("./df_recent.png", "Resources/images", overwrite=True)
-    
-    predictions = history_df[['prediction']]
-    labels = history_df[['label']]
-
-    # Only create the confusion matrix when our iris_predictions feature group has examples of all 3 iris flowers
-    print("Number of different flower predictions to date: " + str(predictions.value_counts().count()))
-    if predictions.value_counts().count() == 3:
-        results = confusion_matrix(labels, predictions)
-    
-        df_cm = pd.DataFrame(results, ['True Setosa', 'True Versicolor', 'True Virginica'],
-                             ['Pred Setosa', 'Pred Versicolor', 'Pred Virginica'])
-    
-        cm = sns.heatmap(df_cm, annot=True)
-        fig = cm.get_figure()
-        fig.savefig("./confusion_matrix.png")
-        dataset_api.upload("./confusion_matrix.png", "Resources/images", overwrite=True)
-    else:
-        print("You need 3 different flower predictions to create the confusion matrix.")
-        print("Run the batch inference pipeline more times until you get 3 different iris flower predictions") 
-
-
-if __name__ == "__main__":
-    if LOCAL == True :
-        g()
-    else:
-        with stub.run():
-            f()
-