from transformers import pipeline, set_seed
import gradio as grad
import random
import re

gpt2_pipe = pipeline('text-generation', model='succinctly/text2image-prompt-generator')

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


def generate(starting_text):
    for count in range(6):
        seed = random.randint(100, 1000000)
        set_seed(seed)
    
        # If the text field is empty
        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=random.randint(60, 90), num_return_sequences=8)
        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)
    
        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 == 5:
            return response_end


txt = grad.Textbox(lines=1, label="English", placeholder="English Text here")
out = grad.Textbox(lines=6, label="Generated Text")
examples = [["mythology of the Slavs"], ["All-seeing eye monitors these world"], ["astronaut dog"],
            ["A monochrome forest of ebony trees"], ["sad view of worker in office,"],
            ["Headshot photo portrait of John Lennon"], ["wide field with thousands of blue nemophila,"]]
title = "Aiconvert.online"
description = "Ai Image Prompt Generator."
article = "<div><center><img src='' alt='visitor badge'></center></div>"

grad.Interface(fn=generate,
               inputs=txt,
               outputs=out,
               examples=examples,
               title=title,
               description=description,
               article=article,
               allow_flagging='never',
               cache_examples=False).queue(concurrency_count=1, api_open=False).launch(show_api=False, show_error=True)