| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| trained_tokenizer = GPT2Tokenizer.from_pretrained("Kumarkishalaya/GPT-2-next-word-prediction") | |
| trained_model = GPT2LMHeadModel.from_pretrained("Kumarkishalaya/GPT-2-next-word-prediction") | |
| untrained_model = GPT2Tokenizer.from_pretrained("gpt2") | |
| untrained_tokenizer = ("gpt2") | |
| def generate(commentary_text): | |
| input_ids = trained_tokenizer(commentary_text, return_tensors="pt") | |
| input_ids = input_ids['input_ids'].to(device) | |
| output = trained_model.generate(input_ids, max_length=60, num_beams=5, do_sample=False) | |
| return tokenizer_finetuned.decode(output[0]) | |
| demo = gr.Interface(fn=generate, inputs="text", outputs="text") | |
| demo.launch() |