Update app.py

app.py

@@ -107,17 +107,23 @@ def generate_ocr(method, image):
     with torch.no_grad():
         outputs = model(**inputs)
         logits = outputs.logits  # Get raw logits
-        probs = F.softmax(logits, dim=1)  # Convert logits to probabilities
 
-    # Print probabilities for debugging
-    print(f"Probabilities: {probs}")
+    # Print raw logits for debugging
+    print(f"Raw logits: {logits}")
 
+    # Convert logits to probabilities
+    probs = F.softmax(logits, dim=1)
+    
     # Extract probability values
     not_spam_prob = probs[0, 0].item()
     spam_prob = probs[0, 1].item()
 
-    # Ensure classification is based on correct threshold
-    if spam_prob > not_spam_prob:
+    # Print probabilities for debugging
+    print(f"Not Spam Probability: {not_spam_prob}, Spam Probability: {spam_prob}")
+
+    # Use a classification threshold to avoid bias
+    threshold = 0.55  # Adjust based on observations
+    if spam_prob >= threshold:
         label = "Spam"
     else:
         label = "Not Spam"