finished app no frills

app.py

@@ -124,6 +124,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
 
 
     BASE_DIR = 'dataset'
+    labeldict = {}
 
     for entry in data_json:
         data_split = entry['Data Split']
@@ -132,6 +133,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
 
         image_url = f"{entry['Labeled Data']}"
         label = entry['Label']['classifications'][0]['answer']['value']
+        labeldict[f'{label.lower()}'] = label
 
         destination_folder = os.path.join(BASE_DIR, data_split, label)
         filename = os.path.basename(image_url)
@@ -255,7 +257,7 @@ def train_and_inference(api_key, ontology_id, model_run_id):
             print(f"InvalidArgumentError: {e}. Skipping this data row.")
             continue  # Skip to the next datarow if an exception occurs
         my_checklist_answer = ClassificationAnswer(
-                    name = label,
+                    name = labeldict[label.lower()],
                     confidence=confidence)
         checklist_prediction = ClassificationAnnotation(
     name=ontology.classifications()[0].instructions,