fix syntax errors

app.py

@@ -14,44 +14,44 @@ select = st.selectbox('Which model would you like to evaluate?',
 	('Bart', 'mBart'))
 
 def get_datasets():
-	if select == 'Bart'
+	if select == 'Bart':
 		all_datasets = ["Communication Networks: unseen questions", "Communication Networks: unseen answers"]
-	if select == 'mBart'
+	if select == 'mBart':
 		all_datasets = ["Micro Job: unseen questions", "Micro Job: unseen answers", "Legal Domain: unseen questions", "Legal Domain: unseen answers"]
 	return all_datasets
 
 all_datasets = get_datasets()
 
 def get_split(dataset_name):
-	if dataset_name == "Communication Networks: unseen questions"
+	if dataset_name == "Communication Networks: unseen questions":
 		split = load_dataset("Short-Answer-Feedback/saf_communication_networks_english", split="test_unseen_questions")
-	if dataset_name == "Communication Networks: unseen answers"
+	if dataset_name == "Communication Networks: unseen answers":
 		split = load_dataset("Short-Answer-Feedback/saf_communication_networks_english", split="test_unseen_answers")
-	if dataset_name == "Micro Job: unseen questions"
+	if dataset_name == "Micro Job: unseen questions":
 		split = load_dataset("Short-Answer-Feedback/saf_micro_job_german", split="test_unseen_questions")
-	if dataset_name == "Micro Job: unseen answers"
+	if dataset_name == "Micro Job: unseen answers":
 		split = load_dataset("Short-Answer-Feedback/saf_micro_job_german", split="test_unseen_answers")
-	if dataset_name	== "Legal Domain: unseen questions"
+	if dataset_name	== "Legal Domain: unseen questions":
 		split = load_dataset("Short-Answer-Feedback/saf_legal_domain_german", split="test_unseen_questions")
-	if dataset_name	== "Legal Domain: unseen answers"
+	if dataset_name	== "Legal Domain: unseen answers":
 		split = load_dataset("Short-Answer-Feedback/saf_legal_domain_german", split="test_unseen_answers")
 	return split
 
 def get_model(datasetname):
-	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers"
+	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
 		model = "Short-Answer-Feedback/bart-finetuned-saf-communication-networks"
-	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers"
+	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers":
 		model = "Short-Answer-Feedback/mbart-finetuned-saf-micro-job"
-	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers"
+	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers":
 		model = "Short-Answer-Feedback/mbart-finetuned-saf-legal-domain"
 	return model
 
 def get_tokenizer(datasetname):
-	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers"
+	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
 		tokenizer = "Short-Answer-Feedback/bart-finetuned-saf-communication-networks"
-	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers"
+	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers":
 		tokenizer = "Short-Answer-Feedback/mbart-finetuned-saf-micro-job"
-	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers"
+	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers":
 		tokenizer = "Short-Answer-Feedback/mbart-finetuned-saf-legal-domain"
 	return tokenizer 
 
@@ -212,7 +212,7 @@ def load_data():
   		predicted_labels = extract_labels(predictions)
 
    		reference_feedback = [x.split('Feedback:', 1)[1].strip() for x in labels]
-    		reference_labels = [x.split('Feedback:', 1)[0].strip() for x in labels]
+    	reference_labels = [x.split('Feedback:', 1)[0].strip() for x in labels]
 
 		rouge_score = rouge.compute(predictions=predicted_feedback, references=reference_feedback)['rouge2']
 		bleu_score = sacrebleu.compute(predictions=predicted_feedback, references=[[x] for x in reference_feedback])['score']