from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration

import gradio as gr
from gradio.mix import Parallel

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_t5.encode(text, return_tensors='pt')
    summary_ids = model_t5.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

summ_demo = gr.Interface(
    fn=summ_t5,
    inputs="text",
    outputs=gr.Textbox(lines=10, label="T5 Base Output")
    )

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