app.py

import gradio as gr
from main import main
from arguments import parse_args
import os

def generate_image(prompt, model, num_iterations, learning_rate, progress = gr.Progress(track_tqdm=True)):
    # Set up arguments
    args = parse_args()
    args.task = "single"
    args.prompt = prompt
    args.model = model
    args.n_iters = num_iterations
    args.lr = learning_rate
    args.cache_dir = "./HF_model_cache"
    args.save_dir = "./outputs"
    args.save_all_images = True
    
    try:
        # Run the main function
        main(args)

        settings = (
            f"{args.model}{'_' + args.prompt if args.task == 't2i-compbench' else ''}"
            f"{'_no-optim' if args.no_optim else ''}_{args.seed if args.task != 'geneval' else ''}"
            f"_lr{args.lr}_gc{args.grad_clip}_iter{args.n_iters}"
            f"_reg{args.reg_weight if args.enable_reg else '0'}"
            f"{'_pickscore' + str(args.pickscore_weighting) if args.enable_pickscore else ''}"
            f"{'_clip' + str(args.clip_weighting) if args.enable_clip else ''}"
            f"{'_hps' + str(args.hps_weighting) if args.enable_hps else ''}"
            f"{'_imagereward' + str(args.imagereward_weighting) if args.enable_imagereward else ''}"
            f"{'_aesthetic' + str(args.aesthetic_weighting) if args.enable_aesthetic else ''}"
        )

        save_dir = f"{args.save_dir}/{args.task}/{settings}/{args.prompt}"
        
        # Return the path to the generated image
        image_path = f"{save_dir}/best_image.png"
        
        if os.path.exists(image_path):
            return image_path, f"Image generated successfully and saved at {image_path}"
        else:
            return None, "Image generation completed, but the file was not found."
    
    except Exception as e:
        return None, f"An error occurred: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Dropdown(["sd-turbo", "sdxl-turbo", "pixart", "hyper-sd"], label="Model"),
        gr.Slider(minimum=10, maximum=100, step=10, value=50, label="Number of Iterations"),
        gr.Slider(minimum=0.1, maximum=10.0, step=0.1, value=5.0, label="Learning Rate"),
    ],
    outputs=[
        gr.Image(type="filepath", label="Generated Image"),
        gr.Textbox(label="Status")
    ],
    title="ReNO Image Generation",
    description="Enter a prompt to generate an image using ReNO. Adjust the model and parameters as needed."
)

# Launch the app
iface.launch()