third attempt

app.py

@@ -96,18 +96,13 @@ def predict_toxicity(text, model, tokenizer, device, model_name):
     
     if model_name == "lmsys/toxicchat-t5-large-v1.0":
         prefix = "ToxicChat: "
-        inputs = tokenizer.encode(prefix + text, return_tensors="pt").to(device)
+        inputs = tokenizer(prefix + text, return_tensors="pt", max_length=512, truncation=True).to(device)
         
         with torch.no_grad():
-            outputs = model.generate(inputs, max_new_tokens=5)
+            outputs = model.generate(**inputs)
         
         prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
-        
-        # Print raw output for debugging
-        print(f"Raw model output: {prediction}")
-        
-        # Adjust the condition to check for both "positive" and potential variations
-        prediction == "Toxic" if prediction == "positive" else "Not Toxic"
+        prediction = "Toxic" if prediction == "positive" else "Not Toxic"
     else:
         inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128, padding="max_length").to(device)