hopefully final commit

app.py

@@ -41,7 +41,6 @@ model = AutoModelForSequenceClassification.from_pretrained(modelPath)
 # RoBERTA: [0]: neutral, [1]: toxic
 encoding = tokenizer.encode(txt, return_tensors='pt')
 result = model(encoding)
-result
 
 # transform logit to get probabilities
 if (result.logits.size(dim=1) < 2):
@@ -49,13 +48,12 @@ if (result.logits.size(dim=1) < 2):
     result.logits = nn.functional.pad(result.logits, pad, "constant", 0)
     st.write(result)
 prediction = nn.functional.softmax(result.logits, dim=-1)
-prediction
-#neutralProb = prediction.data[0][neutralIndex]
-#toxicProb = prediction.data[0][toxicIndex]
+neutralProb = prediction.data[0][neutralIndex]
+toxicProb = prediction.data[0][toxicIndex]
 
 # Expected returns from RoBERTa on default text:
 # Neutral: 0.0052
 # Toxic: 0.9948
 st.write("Classification Probabilities")
-#st.write(f"{neutralProb:.4f} - NEUTRAL")
-#st.write(f"{toxicProb:.4f} - TOXIC")
+st.write(f"{neutralProb:.4f} - NEUTRAL")
+st.write(f"{toxicProb:.4f} - TOXIC")