Update app.py

app.py

@@ -4,15 +4,6 @@ from transformers import T5ForConditionalGeneration, RobertaTokenizer
 # Load the quantized model and tokenizer from Hugging Face Hub
 quantized_model = T5ForConditionalGeneration.from_pretrained("AbdulHadi806/codet5-finetuned-latest-quantized")
 tokenizer = RobertaTokenizer.from_pretrained("AbdulHadi806/codet5-finetuned-latest-quantized")
-
-def generate_code(input_text):
-    print(input_text)
-    input_ids = tokenizer(input_text, return_tensors='pt', padding="max_length", truncation=True, max_length=128).input_ids.to(model.device)
-    outputs = model.generate(input_ids, max_length=128, num_beams=4, early_stopping=True)
-    predicted_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    cleaned_code = clean_generated_code(postprocess_output(predicted_text))
-
-    return cleaned_code
     
 def preprocess_infer_input(text):
     # Assuming the input is already a string, we don't need to access it as a dictionary