Update app.py

app.py

@@ -72,32 +72,20 @@ model = load_model()
 
 # Classification function
 def classify_email(email_text):
-    model.eval()  # Set model to evaluation mode
+    model.eval()
 
     with torch.no_grad():
-        # Tokenize and convert input text to tensor
         inputs = tokenizer(email_text, padding=True, truncation=True, max_length=256, return_tensors="pt")
-
-        # Move inputs to the appropriate device
         inputs = {key: val.to(device) for key, val in inputs.items()}
-
-        # Get model predictions
         outputs = model(**inputs)
         logits = outputs.logits
-
-        # Convert logits to predicted class
         predictions = torch.argmax(logits, dim=1)
-
-        # Convert logits to probabilities using softmax
         probs = F.softmax(logits, dim=1)
-        confidence = torch.max(probs).item() * 100  # Convert to percentage
+        confidence = torch.max(probs).item() * 100
 
-    # Convert numeric prediction to label
     result = "Spam" if predictions.item() == 1 else "Ham"
+    return result, f"{confidence:.2f}%"
 
-    return {
-        "result": result,
-        "confidence": f"{confidence:.2f}%",
     }
 
 # Evaluation function with detailed classification report
@@ -195,6 +183,8 @@ def create_interface():
             fn=classify_email,
             inputs=email_input,
             outputs=[result_output, confidence_output]
+        )
+                
 )
 
         gr.Markdown("## 📊 Model Performance Analytics")