| 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,temperatue=0.1) |
| response="" |
| |
| for i, x in enumerate(gpt2_tensors): |
| response=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 Text") |
| grad.Interface(generate, inputs=txt, outputs=out).launch() |
|
|
