#Save ZeroGPU limited resources, switch to InferenceAPI
import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
import requests
import re
import asyncio
from PIL import Image

translator = Translator()
HF_TOKEN = os.environ.get("HF_TOKEN", None)
# Constants
basemodel = "black-forest-labs/FLUX.1-dev"
MAX_SEED = np.iinfo(np.int32).max

CSS = """
footer {
    visibility: hidden;
}
"""

JS = """function () {
  gradioURL = window.location.href
  if (!gradioURL.endsWith('?__theme=dark')) {
    window.location.replace(gradioURL + '?__theme=dark');
  }
}"""

def enable_lora(lora_add):
    if not lora_add:
        return basemodel
    else:
        return lora_add

async def generate_image(
    prompt:str,
    negative_prompt:str,
    model:str,
    lora_word:str,
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1):

    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)
    print(f'prompt:{prompt}')
    
    text = str(translator.translate(prompt, 'English')) + "," + lora_word

    client = AsyncInferenceClient()

    image = await client.text_to_image(
        prompt=text,
        negative_prompt=negative_prompt,
        height=height,
        width=width,
        guidance_scale=scales,
        num_inference_steps=steps,
        model=model,
    )
    print(image)
    
    return image, seed

async def gen(
    prompt:str,
    lora_add:str="",
    lora_word:str="",
    negative_prompt:str="",
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1,
    progress=gr.Progress(track_tqdm=True)
):
    model = enable_lora(lora_add)
    print(model)
    image, seed = await generate_image(prompt,negative_prompt,model,lora_word,width,height,scales,steps,seed)
    return image, seed
     
examples = [
    ["A cartoon-style blonde European-American woman wearing sunglasses stood in front of the triumphant door to take a selfie, the upper bodyartistic style","Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration","artistic style blends elements of reality and illustration"],
    ["A cartoon style European woman wearing glasses is eating a table of seafood,including lobster,oysters,and other shellfish,in a well lit modern restaurant. The background of the restaurant is very blurry,and she is holding the utensils ready to eat. There is a glass of red wine and various dishes on the table. The illustrations contrast with the real food and environment,creating a unique mixed media effect and high angle perspective","Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration","artistic style blends elements of reality and illustration"],
    ["photograph, background of Earth from space, red car on the Moon watching Earth","martintomov/retrofuturism-flux","retrofuturism"],
    ["red sports car with `SPACE RIDER` on its frontal hood, frontal view with a person visible inside through the windshield, car is emerging from a circular radiant portal with bright orange and red hues against a dark background, sense of high-speed motion and a tunnel-like effect","martintomov/retrofuturism-flux","retrofuturism"]
]


# Gradio Interface

with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<h1><center>Flux Lab Light</center></h1>")
    gr.HTML("<p><center>Powered By HF Inference API</center></p>")
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Row():
                img = gr.Image(type="filepath", label='flux Generated Image', height=600)
            with gr.Row():
                prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
                sendBtn = gr.Button(scale=1, variant='primary')
        with gr.Accordion("Advanced Options", open=True):
            with gr.Column(scale=1):
                width = gr.Slider(
                    label="Width",
                    minimum=512,
                    maximum=1280,
                    step=8,
                    value=768,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=1280,
                    step=8,
                    value=1024,
                )
                scales = gr.Slider(
                    label="Guidance",
                    minimum=3.5,
                    maximum=7,
                    step=0.1,
                    value=3.5,
                )
                steps = gr.Slider(
                    label="Steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=24,
                )
                seed = gr.Slider(
                    label="Seeds",
                    minimum=-1,
                    maximum=MAX_SEED,
                    step=1,
                    value=-1,
                )
                negative_prompt = gr.Textbox(
                    label="Negative prompt",
                    lines=1,
                    value="low quality"
                )
                lora_add = gr.Textbox(
                    label="Add Flux LoRA",
                    info="Copy the HF LoRA model name here",
                    lines=1,
                    value="Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration",
                )
                lora_word = gr.Textbox(
                    label="Add Flux LoRA Trigger Word",
                    info="Add the Trigger Word",
                    lines=1,
                    value="",
                )

    gr.Examples(
        examples=examples,
        inputs=[prompt,lora_add,lora_word],
        outputs=[img, seed],
        fn=gen,
        cache_examples="lazy",
        examples_per_page=4,
    )

    gr.on(
        triggers=[
            prompt.submit,
            sendBtn.click,
        ],
        fn=gen,
        inputs=[
            prompt,
            lora_add,
            lora_word,
            negative_prompt,
            width, 
            height, 
            scales, 
            steps, 
            seed
        ],
        outputs=[img, seed],
        api_name="run",
    )
    

    
demo.queue().launch()