Add torch.nograd

app.py

@@ -150,16 +150,17 @@ def personality_detection(text, threshold=0.05, endpoint= 1.0):
     print(PERSONALITY_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained ("Nasserelsaman/microsoft-finetuned-personality",token=PERSONALITY_TOKEN)
     model = AutoModelForSequenceClassification.from_pretrained ("Nasserelsaman/microsoft-finetuned-personality",token=PERSONALITY_TOKEN)
-    
-    inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
-    outputs = model(**inputs)
-    predictions = outputs.logits.squeeze().detach().numpy()
-    
-    # Get raw logits
-    logits = model(**inputs).logits
-    
-    # Apply sigmoid to squash between 0 and 1
-    probabilities = torch.sigmoid(logits)
+
+    with torch.no_grad():
+        inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+        outputs = model(**inputs)
+        predictions = outputs.logits.squeeze().detach().numpy()
+        
+        # Get raw logits
+        logits = model(**inputs).logits
+        
+        # Apply sigmoid to squash between 0 and 1
+        probabilities = torch.sigmoid(logits)
     
     # # Set values less than the threshold to 0.05
     # predictions[predictions < threshold] = 0.05