import gradio as gr
import os
import shutil
from main import fine_tune_model
from diffusers import StableDiffusionPipeline, DDIMScheduler
import torch

MODEL_NAME = "runwayml/stable-diffusion-v1-5"
OUTPUT_DIR = "/home/user/app/stable_diffusion_weights/custom_model"

def fine_tune(instance_prompt, image1, image2=None):
    instance_data_dir = "/home/user/app/instance_images"
    
    try:
        if os.path.exists(instance_data_dir):
            shutil.rmtree(instance_data_dir)
        os.makedirs(instance_data_dir, exist_ok=True)
        
        image1.save(os.path.join(instance_data_dir, "instance_0.png"))
        if image2 is not None:
            image2.save(os.path.join(instance_data_dir, "instance_1.png"))
        
        fine_tune_model(instance_data_dir, instance_prompt, MODEL_NAME, OUTPUT_DIR)
        return "Model fine-tuning complete."
    except Exception as e:
        return str(e)

def generate_images(prompt, num_samples, height, width, num_inference_steps, guidance_scale):
    try:
        if not os.path.exists(OUTPUT_DIR):
            return "The model path does not exist."
        
        pipe = StableDiffusionPipeline.from_pretrained(OUTPUT_DIR, safety_checker=None, torch_dtype=torch.float16).to("cuda")
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        g_cuda = torch.Generator(device='cuda').manual_seed(1337)
        
        with torch.autocast("cuda"), torch.inference_mode():
            images = pipe(
                prompt, height=height, width=width, num_images_per_prompt=num_samples,
                num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=g_cuda
            ).images
        
        return images
    except Exception as e:
        return str(e)

def gradio_app():
    with gr.Blocks() as demo:
        with gr.Tab("Fine-Tune Model"):
            with gr.Row():
                with gr.Column():
                    instance_prompt = gr.Textbox(label="Instance Prompt")
                    image1 = gr.Image(label="Upload Image 1", type="pil")
                    image2 = gr.Image(label="Upload Image 2 (Optional)", type="pil")
                    fine_tune_button = gr.Button("Fine-Tune Model")
                    output_text = gr.Textbox(label="Output")
                fine_tune_button.click(fine_tune, inputs=[instance_prompt, image1, image2], outputs=output_text)
        
        with gr.Tab("Generate Images"):
            with gr.Row():
                with gr.Column():
                    prompt = gr.Textbox(label="Prompt")
                    num_samples = gr.Number(label="Number of Samples", value=1)
                    guidance_scale = gr.Number(label="Guidance Scale", value=7.5)
                    height = gr.Number(label="Height", value=512)
                    width = gr.Number(label="Width", value=512)
                    num_inference_steps = gr.Slider(label="Steps", value=50, minimum=1, maximum=100)
                    generate_button = gr.Button("Generate Images")
                with gr.Column():
                    gallery = gr.Gallery(label="Generated Images")
                generate_button.click(generate_images, inputs=[prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=gallery)
        
    demo.launch()

if __name__ == "__main__":
    gradio_app()