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
from gradio_client import Client, handle_file
from huggingface_hub import login
from gradio_imageslider import ImageSlider

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): return basemodel if not lora_add else lora_add
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
    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, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, progress):
    model = enable_lora(lora_add)
    image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed)
    image_path = "temp_image.png"
    image.save(image_path)
    upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor)
    return upscale_image, seed

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]

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=[2, 3, 4], value=2, scale=2)
    gr.on([prompt.submit, sendBtn.click], gen, [prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor], [img, seed])
demo.queue(api_open=False).launch(show_api=False, share=False)