cleanup

app.py

@@ -12,7 +12,6 @@ st.button("Submit Text")
 
 # Load tokenizer and model weights, try to default to 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"
@@ -37,16 +36,15 @@ else:
 tokenizer = AutoTokenizer.from_pretrained(tokenizerPath)
 model = AutoModelForSequenceClassification.from_pretrained(modelPath)
 
-# run encoding through model to get classification output
+# Run encoding through model to get classification output.
 # RoBERTA: [0]: neutral, [1]: toxic
 encoding = tokenizer.encode(txt, return_tensors='pt')
 result = model(encoding)
 
-# transform logit to get probabilities
+# Transform logit to get probabilities.
 if (result.logits.size(dim=1) < 2):
     pad = (0, 1)
     result.logits = nn.functional.pad(result.logits, pad, "constant", 0)
-    st.write(result)
 prediction = nn.functional.softmax(result.logits, dim=-1)
 neutralProb = prediction.data[0][neutralIndex]
 toxicProb = prediction.data[0][toxicIndex]