import gradio as gr
import argparse
import os

from musepose_inference import MusePoseInference
from pose_align import PoseAlignmentInference
from downloading_weights import download_models

class App:
    def __init__(self, args):
        self.args = args
        self.pose_alignment_infer = PoseAlignmentInference(
            model_dir=args.model_dir,
            output_dir=args.output_dir
        )
        self.musepose_infer = MusePoseInference(
            model_dir=args.model_dir,
            output_dir=args.output_dir
        )
        if not args.disable_model_download_at_start:
            download_models(model_dir=args.model_dir)

    @staticmethod
    def on_step1_complete(input_img: str, input_pose_vid: str):
        return [
            gr.Image(label="Input Image", value=input_img, type="filepath", scale=5),
            gr.Video(label="Input Aligned Pose Video", value=input_pose_vid, scale=5)
        ]

    def musepose_demo(self):
        with gr.Blocks() as demo:
            self.header()
    
            # 첫 번째 단계: Pose Alignment
            img_pose_input = gr.Image(label="Input Image", type="filepath", scale=5)
            vid_dance_input = gr.Video(label="Input Dance Video", max_length=10, scale=5)
            vid_dance_output = gr.Video(label="Aligned Pose Output", scale=5, interactive=False)
            vid_dance_output_demo = gr.Video(label="Aligned Pose Output Demo", scale=5)
            
            # 두 번째 단계: MusePose Inference
            img_musepose_input = gr.Image(label="Input Image", type="filepath", scale=5)
            vid_pose_input = gr.Video(label="Input Aligned Pose Video", max_length=10, scale=5)
            vid_output = gr.Video(label="MusePose Output", scale=5)
            vid_output_demo = gr.Video(label="MusePose Output Demo", scale=5)
    
            btn_align_pose = gr.Button("ALIGN POSE", variant="primary")
            btn_generate = gr.Button("GENERATE", variant="primary")
            
            btn_align_pose.click(
                fn=self.pose_alignment_infer.align_pose,
                inputs=[vid_dance_input, img_pose_input],
                outputs=[vid_dance_output, vid_dance_output_demo]
            )

            btn_generate.click(
                fn=self.musepose_infer.infer_musepose,
                inputs=[img_musepose_input, vid_pose_input],
                outputs=[vid_output, vid_output_demo]
            )
        
            vid_dance_output.change(
                fn=self.on_step1_complete,
                inputs=[img_pose_input, vid_dance_output],
                outputs=[img_musepose_input, vid_pose_input]
            )

        return demo


    @staticmethod
    def header():
        header = gr.HTML(
            """
            <h1 style="font-size: 23px;">
                <a href="https://github.com/jhj0517/MusePose-WebUI" target="_blank">MusePose WebUI</a>
            </h1>
            <p style="font-size: 18px;">
                <strong>Note</strong>: This space only allows video input up to <strong>10 seconds</strong> because ZeroGPU limits the function runtime to 2 minutes.<br>
                If you want longer video inputs, you have to run it locally. Click the link above and follow the README to try it locally.<br><br>
                When you have completed the <strong>1: Pose Alignment</strong> process, go to <strong>2: MusePose Inference</strong> and click the "GENERATE" button.
            </p>
            """
        )
        return header

    def launch(self):
        demo = self.musepose_demo()
        demo.queue().launch(
            share=self.args.share
        )

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str, default=os.path.join("pretrained_weights"), help='Pretrained models directory for MusePose')
    parser.add_argument('--output_dir', type=str, default=os.path.join("outputs"), help='Output directory for the result')
    parser.add_argument('--disable_model_download_at_start', type=bool, default=False, nargs='?', const=True, help='Disable model download at start or not')
    parser.add_argument('--share', type=bool, default=False, nargs='?', const=True, help='Gradio makes sharable link if it is true')
    args = parser.parse_args()

    app = App(args=args)
    app.launch()