app.py

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

MAX_SEED = np.iinfo(np.int32).max

# Initialize the AsyncInferenceClient globally
client = AsyncInferenceClient()

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

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)
        text = str(Translator().translate(prompt, 'English')) + "," + lora_word
        
        # Generate the image
        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

def get_upscale_finegrain(prompt, img_path, upscale_factor):
    try:
        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]
    except Exception as e:
        print(f"Error upscaling image: {e}")
        return None

async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
    model = enable_lora(lora_model, basemodel) if process_lora else basemodel

    # Generate a unique file name for temporary files
    temp_image_path = f"temp_image_{uuid.uuid4().hex}.jpg"
    upscale_image_path = f"upscale_image_{uuid.uuid4().hex}.jpg"

    try:
        # Generate the image
        image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
        if image is None:
            return ["Generation failed", None]

        # Save the image locally
        image.save(temp_image_path, format="JPEG")

        # Process upscale if required
        if process_upscale:
            upscale_result_path = get_upscale_finegrain(prompt, temp_image_path, upscale_factor)
            if upscale_result_path is not None:
                upscale_image = Image.open(upscale_result_path)
                upscale_image.save(upscale_image_path, format="JPEG")
                return [temp_image_path, upscale_image_path]
            else:
                return ["Upscale failed", temp_image_path]
        else:
            return [temp_image_path, temp_image_path]
    except Exception as e:
        print(f"Error in generation pipeline: {e}")
        return ["Error", None]
    finally:
        # Cleanup temporary files
        try:
            if os.path.exists(temp_image_path):
                os.remove(temp_image_path)
            if os.path.exists(upscale_image_path):
                os.remove(upscale_image_path)
        except Exception as cleanup_error:
            print(f"Error during cleanup: {cleanup_error}")

css = """
#col-container{ margin: 0 auto; max-width: 1024px;}
"""

with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column(scale=3):
                output_res = gr.Image(label="Generated Image / Upscaled Image")
            with gr.Column(scale=2):
                prompt = gr.Textbox(label="Image Description")
                basemodel_choice = gr.Dropdown(
                    label="Model", 
                    choices=[
                        "black-forest-labs/FLUX.1-schnell", 
                        "black-forest-labs/FLUX.1-DEV", 
                        "enhanceaiteam/Flux-uncensored", 
                        "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", 
                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
                        "city96/FLUX.1-dev-gguf"
                    ], 
                    value="black-forest-labs/FLUX.1-schnell"
                )
                lora_model_choice = gr.Dropdown(
                    label="LoRA", 
                    choices=[
                        "Shakker-Labs/FLUX.1-dev-LoRA-add-details", 
                        "XLabs-AI/flux-RealismLora", 
                        "enhanceaiteam/Flux-uncensored"
                    ], 
                    value="XLabs-AI/flux-RealismLora"
                )
                process_lora = gr.Checkbox(label="LoRA Process")
                process_upscale = gr.Checkbox(label="Scale Process")
                upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)

                with gr.Accordion(label="Advanced Options", open=False):
                    width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280)
                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768)
                    scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
                    seed = gr.Number(label="Seed", value=-1)

                btn = gr.Button("Generate")
                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,
                )
    demo.launch()