Update app.py

app.py

@@ -9,7 +9,7 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 st.title('Ethical Judgment Classifier')
 st.write('This app uses a pre-trained Distilbert model fine-tuned on the Commonsense Ethics dataset from the Aligning AI With Shared Human Values project (for more information, see https://github.com/hendrycks/ethics). It judges whether a given action of scenario is wrong or not wrong and uses transformers-interpret (https://pypi.org/project/transformers-interpret/) to show how the words in your scenario affected the model\'s judgment.')
 
-loaded_model = DistilBertForSequenceClassification.from_pretrained('commonsense_ethics')
+loaded_model = DistilBertForSequenceClassification.from_pretrained('distilbert')
 model_name = 'distilbert-base-uncased'
 tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
 cls_explainer = SequenceClassificationExplainer(loaded_model, tokenizer)
@@ -20,7 +20,7 @@ text = st.text_input('Enter a scenario or action.')
 
 if text:
     answer = clf(text)
-    label = 'wrong' if answer[0]['label'] == 'LABEL_1' else 'not wrong'
+    label = 'wrong' if answer[0]['label'] == 'LABEL_0' else 'not wrong'
     st.write(f'This action is {label} (confidence level {answer[0]["score"]*100:.2f}%).')
     attributions = cls_explainer(text)
     df = pd.DataFrame(attributions[1:-1])