app.py

import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
from gradio_client import Client, handle_file
from PIL import Image
from lora_saver import main as backup_loras

os.system('quent_models.py')

from loras import loras
from huggingface_hub import login
from themes import IndonesiaTheme  # Import custom IndonesiaTheme
from lorify import Lorify
from css import css2
MAX_SEED = np.iinfo(np.int32).max


HF_TOKEN = os.getenv('HF_TOKEN')
HF_TOKEN_UPSCALER = os.getenv('HF_TOKEN')

qwen_client = Client("K00B404/HugChatWrap",hf_token=HF_TOKEN)
loaded_loras=[]

for lora in loras:
    print(lora.get('repo'))
    loaded_loras.append(lora.get('repo'))
    
# Function to enable LoRA if selected
def enable_lora(lora_add, basemodel):
    print(f"[-] Determining model: LoRA {'enabled' if lora_add else 'disabled'}, base model: {basemodel}")
    return basemodel if not lora_add else lora_add
    
# Function to generate image
async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
    try:
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        seed = int(seed)

        print(f"[-] Menerjemahkan prompt: {prompt}")
        text = str(Translator().translate(prompt, 'English')) + "," + lora_word
        
        print(f"[-] Generating image with prompt: {text}, model: {model}")
        client = AsyncInferenceClient()
        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
        return image, seed
    except Exception as e:
        print(f"[-] Error generating image: {e}")
        return None, None

# Function to upscale image
def get_upscale_finegrain(prompt, img_path, upscale_factor):
    try:
        print(f"[-] Memulai proses upscaling dengan faktor {upscale_factor} untuk gambar {img_path}")
        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN)
        result = client.predict(
            input_image=handle_file(img_path), 
            prompt=prompt, 
            negative_prompt="worst quality, low quality, normal quality",
            upscale_factor=upscale_factor,
            controlnet_scale=0.6,
            controlnet_decay=1,
            condition_scale=6,
            denoise_strength=0.35, 
            num_inference_steps=18,
            solver="DDIM", 
            api_name="/process"
        )
        print(f"[-] Proses upscaling berhasil.")
        return result[1]  # Return upscale image path
    except Exception as e:
        print(f"[-] Error scaling image: {e}")
        return None
        
# Main function to generate images and optionally upscale
async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
    print(f"[-] Memulai generasi gambar dengan prompt: {prompt}")
    
    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
    print(f"[-] Menggunakan model: {model}")

    image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
    
    if image is None:
        print("[-] Image generation failed.")
        return []

    image_path = "temp_image.jpg"
    print(f"[-] Menyimpan gambar sementara di: {image_path}")
    image.save(image_path, format="JPEG")

    upscale_image_path = None
    if process_upscale:
        print(f"[-] Memproses upscaling dengan faktor: {upscale_factor}")
        upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor)
        if upscale_image_path is not None and os.path.exists(upscale_image_path):
            print(f"[-] Proses upscaling selesai. Gambar tersimpan di: {upscale_image_path}")
            return [image_path, upscale_image_path]  # Return both images
        else:
            print("[-] Upscaling gagal, jalur gambar upscale tidak ditemukan.")

    return [image_path]

base_models=[
    "black-forest-labs/FLUX.1-schnell", 
    "black-forest-labs/FLUX.1-DEV", 
    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", 
    #"city96/FLUX.1-dev-gguf"
] 

loras_list_custom=[
    "Keltezaa/anal-riding-missionary",
    "Keltezaa/Fingering",
    "Keltezaa/Spreading",
    "Keltezaa/flux-prone-ass-spread-hd",
    "Keltezaa/Flux_P",
    "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
    "XLabs-AI/flux-RealismLora",       
]# + loaded_loras # add loras loaded from file
 
backup_loras(loras_list_custom)

# Creating Gradio interface
with gr.Blocks(css=css2, theme=IndonesiaTheme()) as WallpaperFluxMaker:
    # Displaying the application title
    gr.HTML('<div id="banner">✨ Flux MultiMode Generator + Upscaler ✨</div>')

    with gr.Column(elem_id="col-container"):
        # Output section (replacing ImageSlider with gr.Gallery)
        with gr.Row():
            output_res = gr.Gallery(label="⚡ Flux / Upscaled Image ⚡", elem_id="output-res", columns=2, height="auto")

        # User input section split into two columns
        with gr.Row():
            # Column 1: Input prompt, LoRA, and base model
            with gr.Column(scale=1, elem_id="col-left"):
                prompt = gr.Textbox(
                    label="📜 Description", 
                    placeholder="Tuliskan prompt Anda dalam bahasa apapun, yang akan langsung diterjemahkan ke bahasa Inggris.",
                    elem_id="textbox-prompt"
                )

                basemodel_choice = gr.Dropdown(
                    label="🖼️ Select Model", 
                    choices=base_models, 
                    value=base_models[0]
                )

                lora_model_choice = gr.Dropdown(
                    label="🎨 Select LoRA", 
                    choices=loras_list_custom, 
                    value=loras_list_custom[0]
                )

                process_lora = gr.Checkbox(label="🎨 Aktifkan LoRA")
                process_upscale = gr.Checkbox(label="🔍 Aktifkan Peningkatan Resolusi")
                upscale_factor = gr.Radio(label="🔍 Faktor Peningkatan Resolusi", choices=[2, 4, 8], value=2)

            # Column 2: Advanced options (always open)
            with gr.Column(scale=1, elem_id="col-right"):
                with gr.Accordion(label="⚙️ Opsi Lanjutan", open=True):
                    width = gr.Slider(label="Lebar", minimum=512, maximum=1280, step=8, value=1280)
                    height = gr.Slider(label="Tinggi", minimum=512, maximum=1280, step=8, value=768)
                    scales = gr.Slider(label="Skala", minimum=1, maximum=20, step=1, value=8)
                    steps = gr.Slider(label="Langkah", minimum=1, maximum=100, step=1, value=8)
                    seed = gr.Number(label="Seed", value=-1)

        # Button to generate image
        btn = gr.Button("🚀 Buat Gambar", elem_id="generate-btn")

        # Running the `gen` function when "Generate" button is pressed
        btn.click(fn=gen, inputs=[
            prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora
        ], outputs=output_res)

# Launching the Gradio app
WallpaperFluxMaker.queue(api_open=True).launch(show_api=True)