Update app.py

app.py

@@ -40,16 +40,38 @@ def predict_aspects(sentence, threshold):
         results[label] = precentage
     return results
 
+
 # Load tokenizer and model
-BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION = 'roberta-large'
+BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION = 'roberta-base'
 tokenizer = RobertaTokenizer.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, do_lower_case=True)
 
-LABEL_COLUMNS_ASPECTS = ['FOOD-CUISINE', 'FOOD-DEALS', 'FOOD-DIET_OPTION', 'FOOD-EXPERIENCE', 'FOOD-FLAVOR', 'FOOD-GENERAL', 'FOOD-INGREDIENT', 'FOOD-KITCHEN', 'FOOD-MEAL', 'FOOD-MENU', 'FOOD-PORTION', 'FOOD-PRESENTATION', 'FOOD-PRICE', 'FOOD-QUALITY', 'FOOD-RECOMMENDATION', 'FOOD-TASTE', 'GENERAL-GENERAL', 'RESTAURANT-ATMOSPHERE', 'RESTAURANT-BUILDING', 'RESTAURANT-DECORATION', 'RESTAURANT-EXPERIENCE', 'RESTAURANT-FEATURES', 'RESTAURANT-GENERAL', 'RESTAURANT-HYGIENE', 'RESTAURANT-KITCHEN', 'RESTAURANT-LOCATION', 'RESTAURANT-OPTIONS', 'RESTAURANT-RECOMMENDATION', 'RESTAURANT-SEATING_PLAN', 'RESTAURANT-VIEW', 'SERVICE-BEHAVIOUR', 'SERVICE-EXPERIENCE', 'SERVICE-GENERAL', 'SERVICE-WAIT_TIME']
+# Define the aspect labels
+LABEL_COLUMNS_ASPECTS = [
+    'FOOD-CUISINE', 'FOOD-DEALS', 'FOOD-DIET_OPTION', 'FOOD-EXPERIENCE', 'FOOD-FLAVOR',
+    'FOOD-GENERAL', 'FOOD-INGREDIENT', 'FOOD-KITCHEN', 'FOOD-MEAL', 'FOOD-MENU',
+    'FOOD-PORTION', 'FOOD-PRESENTATION', 'FOOD-PRICE', 'FOOD-QUALITY', 'FOOD-RECOMMENDATION',
+    'FOOD-TASTE', 'GENERAL-GENERAL', 'RESTAURANT-ATMOSPHERE', 'RESTAURANT-BUILDING', 
+    'RESTAURANT-DECORATION', 'RESTAURANT-EXPERIENCE', 'RESTAURANT-FEATURES', 
+    'RESTAURANT-GENERAL', 'RESTAURANT-HYGIENE', 'RESTAURANT-KITCHEN', 'RESTAURANT-LOCATION', 
+    'RESTAURANT-OPTIONS', 'RESTAURANT-RECOMMENDATION', 'RESTAURANT-SEATING_PLAN', 
+    'RESTAURANT-VIEW', 'SERVICE-BEHAVIOUR', 'SERVICE-EXPERIENCE', 'SERVICE-GENERAL', 
+    'SERVICE-WAIT_TIME'
+]
+
+# Load the model with the specified number of labels
+aspects_model = RobertaForSequenceClassification.from_pretrained(
+    BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, 
+    num_labels=len(LABEL_COLUMNS_ASPECTS)
+)
 
-aspects_model = RobertaForSequenceClassification.from_pretrained(BERT_MODEL_NAME_FOR_ASPECTS_CLASSIFICATION, num_labels=len(LABEL_COLUMNS_ASPECTS))
+# Load the state dictionary
 aspects_model.load_state_dict(torch.load('./Aspects_Extraction_Model_updated.pth', map_location=torch.device('cpu')), strict=False)
+
+# Set the model to evaluation mode
 aspects_model.eval()
 
+
+
 # Streamlit App
 st.title("Implicit and Explicit Aspect Extraction")