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)
basemodel = "black-forest-labs/FLUX.1-schnell"
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

def get_upscale_finegrain(prompt, img_path, upscale_factor):
    client = Client("finegrain/finegrain-image-enhancer")
    result = client.predict(
        input_image=handle_file(img_path),
        prompt=prompt,
        negative_prompt="",
        seed=42,
        upscale_factor=upscale_factor,
        controlnet_scale=0.6,
        controlnet_decay=1,
        condition_scale=6,
        tile_width=112,
        tile_height=144,
        denoise_strength=0.35,
        num_inference_steps=18,
        solver="DDIM",
        api_name="/process"
    )
    return result[1]

async def generate_image(
    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()
    try:
        image = await client.text_to_image(
            prompt=text,
            height=height,
            width=width,
            guidance_scale=scales,
            num_inference_steps=steps,
            model=model,
        )
    except Exception as e:
        raise gr.Error(f"Error in {e}")
    
    return image, seed

async def gen(
    prompt:str,
    lora_add:str="",
    lora_word:str="",
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1,
    progress=gr.Progress(track_tqdm=True),
    upscale_factor:int=0
):
    model = enable_lora(lora_add)
    print(model)
    image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed)
    if upscale_factor != 0:
        image = get_upscale_finegrain(prompt, image, upscale_factor)
    return image, seed, image

def upscale_image(img_path, upscale_factor, prompt):
    if upscale_factor == 0:
        return img_path  
    else:
        return get_upscale_finegrain(prompt, img_path, upscale_factor)

with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<h1><center>Flux Lab Light</center></h1>")
    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,
            )
            lora_add = gr.Textbox(
                label="Add Flux LoRA",
                info="Copy the HF LoRA model name here",
                lines=1,
                placeholder="Please use Warm status model",
            )
            lora_word = gr.Textbox(
                label="Add Flux LoRA Trigger Word",
                info="Add the Trigger Word",
                lines=1,
                value="",
            )
            upscale_factor = gr.Radio(
                label="UpScale Factor",
                choices=[
                    0, 
                    2, 
                    3, 
                    4
                ],
                value=0,
                scale=2
            )
            output_res = gr.Image(label="Upscaled Image")

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

if name == "main":
demo.queue(api_open=False).launch(show_api=False, share=False)