.

app.py

@@ -18,21 +18,26 @@ def postprocess(raw_output):
 
 @spaces.GPU
 def predict_product(num1, num2):
+    # Reverse input digits and add spaces
     input_text = f'{preprocess(num1)} * {preprocess(num2)} ='
+
     inputs = tokenizer(input_text, return_tensors='pt').to('cuda' if torch.cuda.is_available() else 'cpu')
     model.to('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # Generate output
     outputs = model.generate(**inputs, max_new_tokens=40)
+
     output = outputs[0][inputs['input_ids'].shape[-1]:]
     raw_output = tokenizer.decode(output, skip_special_tokens=True)
     prediction = postprocess(raw_output)
 
+    # Evalaute the correctness of the result
     try:
         num1_int = int(num1)
         num2_int = int(num2)
         valid_input = True
     except ValueError:
         valid_input = False
-
     if valid_input:
         correct_product = str(num1_int * num2_int)
         is_correct = (prediction == correct_product)