from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration

tokenizer_t5 = T5Tokenizer.from_pretrained("panggi/t5-base-indonesian-summarization-cased")
model_t5 = T5ForConditionalGeneration.from_pretrained("panggi/t5-base-indonesian-summarization-cased")

def summ_t5(text):
  input_ids = tokenizer.encode(text, return_tensors='pt')
  summary_ids = model_.generate(input_ids,
              max_length=100, 
              num_beams=2,
              repetition_penalty=2.5, 
              length_penalty=1.0, 
              early_stopping=True,
              no_repeat_ngram_size=2,
              use_cache=True)
  summary_text = tokenizer_t5.decode(summary_ids[0], skip_special_tokens=True)
  return summary_text
  
sentiment_demo = gr.Interface(
    fn=summ_t5,
    inputs="text",
    outputs="text")

if __name__ == "__main__":
    Parallel(summ_demo,
    inputs=gr.Textbox(lines=10, label="Input Text", placeholder="Enter article here..."),
    title="Summary of Summarizer - Indonesia",
    examples=examples).launch()