import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient, login
from translatepy import Translator
import requests
import re
import asyncio
from PIL import Image
from gradio_client import Client, handle_file


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, model, lora_word, width, height, scales, steps, seed):
    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)
    text = str(translator.translate(prompt, 'English')) + "," + lora_word
    async with AsyncInferenceClient() as client:
        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, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor):
    model = enable_lora(lora_add)
    image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
    if upscale_factor != 0:
        upscaled_image = get_upscale_finegrain(prompt, image, upscale_factor)
        combined_image = Image.new('RGB', (image.width + upscaled_image.width, image.height))
        combined_image.paste(image, (0, 0))
        combined_image.paste(upscaled_image, (image.width, 0))
        return combined_image, seed
    else:
        return image, seed


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='Comparison 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)
    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]
    )