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| 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"]) | |
| 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() |