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from transformers import GPT2LMHeadModel,GPT2Tokenizer |
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import gradio as grad |
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mdl = GPT2LMHeadModel.from_pretrained('gpt2') |
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gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2') |
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def generate(starting_text): |
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tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt') |
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gpt2_tensors = mdl.generate(tkn_ids,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True,temperatue=0.1) |
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response="" |
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for i, x in enumerate(gpt2_tensors): |
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response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}" |
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return response |
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txt=grad.Textbox(lines=1, label="English", placeholder="English Text here") |
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out=grad.Textbox(lines=1, label="Generated Text") |
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grad.Interface(generate, inputs=txt, outputs=out).launch() |
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