import sys
sys.path.insert(0,'stable_diffusion')
import gradio as gr
from train_esd import train_esd


ckpt_path = "stable-diffusion/models/ldm/sd-v1-4-full-ema.ckpt"
config_path = "stable-diffusion/configs/stable-diffusion/v1-inference.yaml"
diffusers_config_path = "stable-diffusion/config.json"

def train(prompt, train_method, neg_guidance, iterations, lr):

    train_esd(prompt,
              train_method,
              3,
              neg_guidance,
              iterations,
              lr,
              config_path,
              ckpt_path, 
              diffusers_config_path,
              ['cuda']
              )


with gr.Blocks() as demo:

    prompt_input = gr.Text(
        placeholder="Enter prompt...",
        label="Prompt",
        info="Prompt corresponding to concept to erase"
    )
    train_method_input = gr.Dropdown(
        choices=['noxattn', 'selfattn', 'xattn', 'full'],
        value='xattn',
        label='Train Method',
        info='Method of training'
    )

    neg_guidance_input = gr.Number(
        value=1,
        label="Negative Guidance",
        info='Guidance of negative training used to train'
    )

    iterations_input = gr.Number(
        value=1000,
        precision=0,
        label="Iterations",
        info='iterations used to train'
    )

    lr_input = gr.Number(
        value=1e-5,
        label="Iterations",
        info='Learning rate used to train'
    )

    train_button = gr.Button(
        value="Train",
    )
    train_button.click(train, inputs = [
            prompt_input,
            train_method_input, 
            neg_guidance_input,
            iterations_input,
            lr_input
        ]
    )




demo.launch()