import gradio as gr
from convert_url_to_diffusers_sdxl_gr import (
    convert_url_to_diffusers_repo,
    SCHEDULER_CONFIG_MAP,
)

vaes = [""]
loras = [""]
schedulers = list(SCHEDULER_CONFIG_MAP.keys())

css = """"""

with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
    gr.Markdown("# Download and convert any Stable Diffusion XL safetensors to Diffusers and create your repo")
    gr.Markdown(
        f"""
The steps are the following:
- Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens).
- Input a model download url from the Hub or Civitai or other sites.
- If you want to download a model from Civitai, paste a Civitai API Key.
- Input your new repo name. e.g. 'yourid/newrepo'.
- Click "Submit".
- Patiently wait until the output changes.
            """
    )
    with gr.Column():
        dl_url = gr.Textbox(label="URL to download", placeholder="https://...", value="", max_lines=1)
        repo_id = gr.Textbox(label="Your New Repo ID", placeholder="author/model", value="", max_lines=1)
        hf_token = gr.Textbox(label="Your HF write token", placeholder="", value="", max_lines=1)
        civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
        is_half = gr.Checkbox(label="Half precision", value=True)
        vae = gr.Dropdown(label="VAE", choices=vaes, value="", allow_custom_value=True)
        scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
        lora1 = gr.Dropdown(label="LoRA1", choices=loras, value="", allow_custom_value=True)
        lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
        lora2 = gr.Dropdown(label="LoRA2", choices=loras, value="", allow_custom_value=True)
        lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
        lora3 = gr.Dropdown(label="LoRA3", choices=loras, value="", allow_custom_value=True)
        lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
        lora4 = gr.Dropdown(label="LoRA4", choices=loras, value="", allow_custom_value=True)
        lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
        lora5 = gr.Dropdown(label="LoRA5", choices=loras, value="", allow_custom_value=True)
        lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
        run_button = gr.Button(value="Submit")
        repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=None)
        output_md = gr.Markdown(label="Output")

    gr.on(
        triggers=[run_button.click],
        fn=convert_url_to_diffusers_repo,
        inputs=[dl_url, repo_id, hf_token, civitai_key, repo_urls, is_half, vae, scheduler,
                 lora1, lora1s, lora2, lora2s, lora3, lora3s, lora4, lora4s, lora5, lora5s],
        outputs=[repo_urls, output_md],
    )

demo.queue()
demo.launch()