from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr

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()