branching

app.py

@@ -10,19 +10,21 @@ defaultTxt = "I hate you cancerous insects so much"
 txt = st.text_area("Text to analyze", defaultTxt)
 
 # Load tokenizer and model weights, try to default to RoBERTa.
-match option:
-    case RoBERTa:
+# Huggingface does not support Python 3.10 match statements and I'm too lazy to implement an equivalent.
+
+if (option == "RoBERTa"):
         tokenizerPath = "s-nlp/roberta_toxicity_classifier"
         modelPath = "s-nlp/roberta_toxicity_classifier"
-    case DistilBERT:
+elif (option == "DistilBERT"):
         tokenizerPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
         modelPath = "citizenlab/distilbert-base-multilingual-cased-toxicity"
-    case XLM-RoBERTa:
+elif (option == "XLM-RoBERTa"):
         tokenizerPath = "unitary/multilingual-toxic-xlm-roberta"
         modelPath = "unitary/multilingual-toxic-xlm-roberta"
-    case _:
+else:
         tokenizerPath = "s-nlp/roberta_toxicity_classifier"
         modelPath = "s-nlp/roberta_toxicity_classifier"
+
 tokenizer = AutoTokenizer.from_pretrained(tokenizerPath)
 model = AutoModelForSequenceClassification.from_pretrained(modelPath)