app.py

import os
import re
import spaces
import random
import string
import torch
import requests
import gradio as gr
import numpy as np
from lxml.html import fromstring
from pathos.threading import ThreadPool as Pool
from diffusers import MotionAdapter, EulerDiscreteScheduler
from diffusers.pipelines.flux import FluxPipeline
from diffusers.utils import export_to_gif
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16

step = 8
repo = "ByteDance/AnimateDiff-Lightning"
ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
#base = "emilianJR/epiCRealism"
base = "black-forest-labs/FLUX.1-dev"

adapter = MotionAdapter().to(device, dtype)
adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))
pipe = FluxPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype, token=os.getenv("hf_token")).to(device)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")

def translate(text,lang):
    
    if text == None or lang == None:
        return ""
        
    text = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', text)).lower().strip()
    lang = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', lang)).lower().strip()
    
    if text == "" or lang == "":
        return ""

    if len(text) > 38:
        raise Exception("Translation Error: Too long text!")

    user_agents = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 13_1) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.1 Safari/605.1.15'
    ]
    padded_chars = re.sub("[(^\-)(\-$)]","",text.replace("","-").replace("- -"," ")).strip()
    query_text = f'Please translate {padded_chars}, into {lang}'
    url = f'https://www.google.com/search?q={query_text}'

    print(url)
    
    resp = requests.get(
        url = url,
        headers = {
            'User-Agent': random.choice(user_agents)
        }
    )
    
    content = resp.content
    html = fromstring(content)
    
    translated = text

    try:
        src_lang = html.xpath('//*[@class="source-language"]')[0].text_content().lower().strip()
        trgt_lang = html.xpath('//*[@class="target-language"]')[0].text_content().lower().strip()
        src_text = html.xpath('//*[@id="tw-source-text"]/*')[0].text_content().lower().strip()
        trgt_text = html.xpath('//*[@id="tw-target-text"]/*')[0].text_content().lower().strip()
        
        if trgt_lang == lang:
            translated = trgt_text
    except:
        print(f'Translation Warning: Failed To Translate!')

    ret = re.sub(f'[{string.punctuation}]', '', re.sub('[\s+]', ' ', translated)).lower().strip()
    print(ret)
    return ret

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

@spaces.GPU(duration=120)
def Piper(_do):
    return pipe(
        _do,
        height=256,
        width=768,
        num_inference_steps=step,
        guidance_scale=7
    )

def infer(prompt_en):
    name = generate_random_string(12)+".png"
    if prompt_en == "":
        _do = 'filmed scene'
    else:
        _do = f'filmed { prompt_en } scene'
    export_to_gif(Piper(_do).frames[0],name)
    return name

css="""
input, input::placeholder {
    text-align: center !important;
}
*, *::placeholder {
    direction: ltr !important;
    font-family: Suez One !important;
}
h1,h2,h3,h4,h5,h6,span,p,pre {
    width: 100% !important;
    text-align: center !important;
    display: block !important;
}
footer {
    display: none !important;
}
#col-container {
    margin: 0 auto !important;
    max-width: 15cm !important;
 }
.image-container {
    aspect-ratio: 768 / 256 !important;
}
.dropdown-arrow {
    display: none !important;
}
*:has(.btn), .btn {
    width: 100% !important;
    margin: 0 auto !important;
}
"""

js="""
function custom(){
    document.querySelector("div#prompt input").setAttribute("maxlength","38")
}
"""

with gr.Blocks(theme=gr.themes.Soft(),css=css,js=js) as demo:
    result = []
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
            # MULTI-LANGUAGE GIF GENERATOR AI
        """)
        with gr.Row():
            prompt = gr.Textbox(
                elem_id="prompt",
                placeholder="WHAT TO CREATE",
                container=False,
                max_lines=1
            )
        with gr.Row():
            run_button = gr.Button("START",elem_classes="btn",scale=0)
        with gr.Row():
            result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
            result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
        with gr.Row():
            result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))
            result.append(gr.Image(interactive=False,elem_classes="image-container", label="Result", show_label=False, type='filepath', show_share_button=False))

    def _ret(idx,p1):
        print(f'Starting {idx}: {p1}')
        v = infer(p1)
        print(f'Finished {idx}: {v}')
        return v
    def _rets(p1):
        p1_en = translate(p1,"english")
        ln = len(result)
        idxs = list(range(ln))
        p1s = [p1_en for _ in idxs]
        return list(Pool(ln).imap( _ret, idxs, p1s ))
    run_button.click(fn=_rets,inputs=prompt,outputs=result)

demo.queue().launch()