import dask.dataframe as dd

from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateBatch, ImageFileCreateEntry, Region
from msrest.authentication import ApiKeyCredentials
import os, time, uuid

ENDPOINT = "https://trashvision.cognitiveservices.azure.com/"
training_key = "611e786a785648e38f346f18e7f7e7ed"
prediction_key = "611e786a785648e38f346f18e7f7e7ed"
project_id = "a67f7d7b-c980-49bd-b57d-0bd1367b29d0"

credentials = ApiKeyCredentials(in_headers={"Training-key": training_key})
trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
prediction_credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
predictor = CustomVisionPredictionClient(ENDPOINT, prediction_credentials)

print ("Training...")
iteration = trainer.train_project(project_id)
while (iteration.status != "Completed"):
    iteration = trainer.get_iteration(project_id, iteration.id)
    print ("Training status: " + iteration.status)
    time.sleep(1)
    
# The iteration is now trained. Publish it to the project endpoint
trainer.publish_iteration(project_id, iteration.id, "HaxNet1", predictor)
print ("Done!")