import gradio as gr
#from tempfile import NamedTemporaryFile
import numpy as np
import random
import string
from diffusers import StableDiffusionPipeline as DiffusionPipeline
import torch
from pathos.multiprocessing import ProcessingPool as ProcessPoolExecutor
import requests
from lxml.html.soupparser import fromstring

pool = ProcessPoolExecutor(4)
pool.__enter__()

model_id = "runwayml/stable-diffusion-v1-5"

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained(model_id, use_safetensors=True)
    pipe = pipe.to(device)

def translate(text,lang):
    html_str = requests.get( url = "https://translate.google.com", params = {"sl": "auto", "tl": lang, "op": "translate", "text": text} ).text()
    tree = fromstring(html_str)
    translated = tree.xpath('span[lang="'+lang+'"]/span/span/text()')[0]
    return translated

def generate_random_string(length):
    characters = string.ascii_letters + string.digits
    return ''.join(random.choice(characters) for _ in range(length))

def infer(prompt):
    name = generate_random_string(12)+".png"
    image = pipe(translate(prompt,"en")).images[0].save(name)
    return name

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
            # Image Generator
            Currently running on {power_device}.
        """)
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False, type='filepath')
    run_button.click(
        fn = infer,
        inputs = [prompt],
        outputs = [result]
    )

demo.queue().launch()