app.py

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,temperature=0.1)
    response = ""
    #response = gpt2_tensors
    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()