from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline, set_seed
import re 
import random
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')

def generate(starting_text):
    seed = random.randint(100, 1000000)
    set_seed(seed)

    response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=4)
    response_list = []
    for x in response:
        resp = x['generated_text'].strip()
        if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
            response_list.append(resp+'\n')

    response_end = "\n".join(response_list)
    response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
    response_end = response_end.replace("<", "").replace(">", "")

    if response_end != "":
        return response_end