Update app.py

app.py

@@ -10,6 +10,7 @@ def analyze_email(email_body):
     sentiment_model = AutoModelForSequenceClassification.from_pretrained("ISOM5240GP4/email_sentiment", num_labels=2)
     tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
 
+    # this is to handle if user does not input anything
     if not email_body.strip():
         return "error", "Email body is empty. Please provide an email to analyze."
     
@@ -24,11 +25,15 @@ def analyze_email(email_body):
         else:
             # Step 2: Analyze sentiment for non-spam emails
             inputs = tokenizer(email_body, padding=True, truncation=True, return_tensors='pt')
+            # Pass the tokenized inputs to the sentiment model
+            # **inputs unpacks the dictionary of inputs to the model
             outputs = sentiment_model(**inputs)
             predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
             predictions = predictions.cpu().detach().numpy()
             sentiment_index = np.argmax(predictions)
             sentiment_confidence = predictions[0][sentiment_index]
+            # Convert numeric prediction to human-readable sentiment
+            # If index is 1, it's Positive, otherwise Negative
             sentiment = "Positive" if sentiment_index == 1 else "Negative"
             
             if sentiment == "Positive":