import gradio as gr
from transformers import MT5Tokenizer, MT5ForConditionalGeneration

def summary(text):

    mt5_model =  MT5ForConditionalGeneration.from_pretrained("google/mt5-large")
    mt5_tokenizer = MT5Tokenizer.from_pretrained("google/mt5-large")
    input_tokenized = mt5_tokenizer.encode(text, return_tensors='pt')
    summary_ids = mt5_model.generate(input_tokenized,
                                       length_penalty = 1,             
                                       min_length = 0,             
                                       max_length = 200,                                       
                                       num_beams = 1,
                                       no_repeat_ngram_size = 2,
                                       early_stopping = True)
    output = mt5_tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) 
    return output[0]
    
iface = gr.Interface(fn=summary, inputs="text", outputs="text")
iface.launch()