diff --git a/trainmodel.py b/trainmodel.py
deleted file mode 100644
index 043ab39..0000000
--- a/trainmodel.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import arcpy
-import time
-import requests
-import json
-from azure.cognitiveservices.vision.customvision.training import training_api
-from azure.cognitiveservices.vision.customvision.training.models import ImageUrlCreateEntry
-
-def imageListChunks(imgList,chunkSize):
-    return [imgList[pos:pos + chunkSize] for pos in range(0, len(imgList), chunkSize)]
-def imageList(tagName):
-    url = "https://api.github.com/repos/esri/photo-survey/contents/Training%20Photos/{}?ref=blight-detection".format(tagName)
-    response = requests.get(url)
-    imgList = json.loads(response.text)
-    if response.status_code == 200:
-        return [img['download_url'] for img in imgList]
-    else:
-        sys.exit(1)
-
-projectNames = ["Boarded", "Overgrowth", "Graffiti"]
-
-training_key = arcpy.GetParameterAsText(0)
-
-trainer = training_api.TrainingApi(training_key)
-
-#Get Existing Project List from Azure:
-existingProjects = [project.name for project in trainer.get_projects()]
-
-# Create a new project
-for name in projectNames:
-    if name not in existingProjects:
-        arcpy.AddMessage("Creating Model {}...".format(name))
-        project = trainer.create_project(name)
-
-        #Negative Tag Name
-        negTagname = "Not_{}".format(name)
-
-        #Make two tags in the new project
-        positive_tag = trainer.create_tag(project.id, name)
-        negative_tag = trainer.create_tag(project.id, negTagname)
-
-        imageEntryList = [ImageUrlCreateEntry(image_url, [positive_tag.id]) for image_url in imageList(name)]
-        negEntryList = [ImageUrlCreateEntry(image_url, [negative_tag.id]) for image_url in imageList(negTagname)]
-
-        arcpy.AddMessage("Loading training photos into model...")
-        for imgChunk in imageListChunks(imageEntryList, 63):
-            trainer.create_images_from_urls(project.id,imgChunk)
-        for imgChunk in imageListChunks(negEntryList, 63):
-            trainer.create_images_from_urls(project.id,imgChunk)
-        arcpy.AddMessage("Training Model...")
-        iteration = trainer.train_project(project.id)
-        while iteration.status == "Training":
-            iteration = trainer.get_iteration(project.id, iteration.id)
-            time.sleep(3)
-
-        # The iteration is now trained. Make it the default project endpoint
-        trainer.update_iteration(project.id, iteration.id, is_default=True)
-        
-        arcpy.AddMessage("Done!")
-    else:
-        arcpy.AddMessage("'{}' model already exists. Skipping...".format(name))