Update app.py

app.py

@@ -269,29 +269,35 @@ def split_text_allow_complete_sentences_nltk(text, max_length=256, tolerance=30,
     return decoded_segments
 
 def predict_quillbot(text):
-    tokenized_text = quillbot_tokenizer(text, padding="max_length", truncation=True, max_length=256, return_tensors="pt").to(device)["input_ids"]
-    output = quillbot_model(tokenized_text)
-    output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
-    q_score = {"QuillBot": output_norm[1].item(), "Original": output_norm[0].item()}
-    return q_score
+    with torch.no_grad():
+        quillbot_model.eval()
+        tokenized_text = quillbot_tokenizer(text, padding="max_length", truncation=True, max_length=256, return_tensors="pt").to(device)
+        output = quillbot_model(**tokenized_text)
+        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
+        q_score = {"QuillBot": output_norm[1].item(), "Original": output_norm[0].item()}
+        return q_score
 
 def predict_bc(model, tokenizer, text):
-    tokens = text_bc_tokenizer(
-        text, padding='max_length', truncation=True, max_length=256, return_tensors="pt"
-    ).to(device)["input_ids"]
-    output = model(tokens)
-    output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
-    print("BC Score: ", output_norm)
-    return output_norm
+    with torch.no_grad():
+        model.eval()
+        tokens = text_bc_tokenizer(
+            text, padding='max_length', truncation=True, max_length=256, return_tensors="pt"
+        ).to(device)
+        output = model(**tokens)
+        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
+        print("BC Score: ", output_norm)
+        return output_norm
 
 def predict_mc(model, tokenizer, text):
-    tokens = text_mc_tokenizer(
-        text, padding='max_length', truncation=True, return_tensors="pt", max_length=256
-    ).to(device)["input_ids"]
-    output = model(tokens)
-    output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
-    print("MC Score: ", output_norm)
-    return output_norm
+    with torch.no_grad():
+        model.eval()
+        tokens = text_mc_tokenizer(
+            text, padding='max_length', truncation=True, return_tensors="pt", max_length=256
+        ).to(device)
+        output = model(**tokens)
+        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
+        print("MC Score: ", output_norm)
+        return output_norm
 
 def ai_generated_test(ai_option, input):