import gradio as gr, random, re
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, set_seed

tokenizer_en_es = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-es-en")
model_en_es = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-es-en")
en_es_translator = pipeline("translation_es_to_en", model = model_en_es, tokenizer = tokenizer_en_es)

gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')

with open("ideas.txt", "r") as f:
    line = f.readlines()


def generate(inputs):
    resultado = en_es_translator(inputs)
    starting_text = resultado[0]['translation_text']

    for count in range(4):
        seed = random.randint(100, 1000000)
        set_seed(seed)

        if starting_text == "":
            starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
            starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
            print(starting_text)
    
        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
        if count == 4:
            return response_end


txt = gr.Textbox(lines=1, label="Texto inicial", placeholder="Texto en Español")
out = gr.Textbox(lines=4, label="Sugerencia generada")


title = "Generador de sugerencia para Stable Diffusion (SD)"
description = 'Esta es una demostración de la serie de modelos: "MagicPrompt", en este caso, dirigida a: Stable Diffusion. Para utilizarlo, simplemente envíe su texto.'
article = ""

gr.Interface(fn=generate,
               inputs=txt,
               outputs=out,
               title=title,
               description=description,
               article=article,
               allow_flagging='never',
               cache_examples=False,
               theme="default").launch(enable_queue=True, debug=True)