app.py

import gradio as gr
from transformers import pipeline
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id)


def predict(inputtext):
    # encode context the generation is conditioned on
    input_ids = tokenizer.encode(inputtext, return_tensors='pt')
    
    # generate text until the output length (which includes the context length) reaches 50
    greedy_output = model.generate(input_ids, max_length=50)
    
    print("Output:\n" + 100 * '-')
    print(tokenizer.decode(greedy_output[0], skip_special_tokens=True))


gr.Interface(
    predict,
    inputs=gr.inputs.Textbox(label="Text"),
    outputs=gr.outputs.Label(),
    title="Hot Dog? Or Not?",
).launch()