|
|
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) |
|
|
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 Tensors") |
|
|
grad.Interface(generate, inputs=txt, outputs=out).launch() |
|
|
|
|
|
|
|
|
|
