from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as grad

mdl = GPT2LMHeadModel.from_pretrained('gpt2')
gpt2_tkn = GPT2Tokenizer.from_pretrained('gpt2')

def generate(starting_text):
    tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
    gpt2_tensors = mdl.generate(tkn_ids, 
                                max_length = 100, 
                                no_repeat_ngram_size = True, 
                                num_beams = 3, 
                                do_sample = True,
                                temperature = 1.5)
    response = ""
    # response = gpt2_tensors
    for i, x in enumerate(gpt2_tensors):
        response += f"{i}: {gpt2_tkn.decode(x, skip_special_tokens = True)}"

    return response

txt = grad.Textbox(lines = 1, label = 'English', placeholder = 'English Text Here')
out = grad.Textbox(lines = 1, label = 'Generated Tensors')
grad.Interface(
    generate,
    inputs = txt,
    outputs = out
).launch()