import gradio as gr
from main_func import video_identity

with gr.Blocks() as demo:

    with gr.Row(variant='compact'):

        with gr.Column():
            gr.Markdown("#### Dynamic Time Warping:")

            with gr.Row(variant='compact'):
                dtw_mean = gr.Slider(
                    value=0.5, 
                    minimum=0, 
                    maximum=1.0, 
                    step=0.05, 
                    label="Winsorize Mean"
                )

                dtw_filter = gr.Slider(
                    value=3, 
                    minimum=1, 
                    maximum=20, 
                    step=1, 
                    label="Savitzky-Golay Filter"
                )

            gr.Markdown("#### Thresholds:")

            with gr.Row(variant='compact'):
                angles_sensitive = gr.Number(
                    value=15, 
                    minimum=0, 
                    maximum=75, 
                    step=1, 
                    min_width=100, 
                    label="Sensitive"
                )

                angles_common = gr.Number(
                    value=25, 
                    minimum=0, 
                    maximum=75, 
                    step=1, 
                    min_width=100, 
                    label="Standart"
                )

                angles_insensitive = gr.Number(
                    value=45, 
                    minimum=0, 
                    maximum=75, 
                    step=1, 
                    min_width=100, 
                    label="Insensitive"
                )

            gr.Markdown("#### Patience:")

            trigger_state = gr.Radio(value="three", choices=["three", "two"], label="Trigger Count")

        input_teacher = gr.Video(show_share_button=False, show_download_button=False, sources=["upload"], label="Teacher's Video")
        input_student = gr.Video(show_share_button=False, show_download_button=False, sources=["upload"], label="Student's Video")

  
    with gr.Accordion("Clarifications:", open=True):
        with gr.Accordion("Dynamic Time Warping:", open=False):
            gr.Markdown("""
            Dynamic Time Warping is an algorithm that performs frame-by-frame alignment for videos with different speeds.
            
            - **Winsorized mean**: Determines the portion of DTW paths, sorted from best to worst, to use for generating the mean DTW alignment. Reasonable values range from 0.25 to 0.6.
            - **Savitzky-Golay Filter**: Enhances the capabilities of the Winsorized mean, making DTW alignment more similar to a strict line. Reasonable values range from 2 to 10.
            """)
        
        with gr.Accordion("Thresholds:", open=False):
            gr.Markdown("""
            Thresholds are used to identify student errors in dance. If the difference in angle between the teacher's and student's videos exceeds this threshold, it is counted as an error.
            
            - **Sensitive**: A threshold that is currently not used.
            - **Standard**: A threshold for most angles. Reasonable values range from 20 to 40.
            - **Insensitive**: A threshold for difficult areas, such as hands and toes. Reasonable values range from 35 to 55.
            """)
        
        with gr.Accordion("Patience:", open=False):
            gr.Markdown("""
            Patience helps prevent model errors by highlighting only errors detected in consecutive frames.
            
            - **Three**: Utilizes 3 consecutive frames for error detection.
            - **Two**: Utilizes 2 consecutive frames for error detection.
                        
            Both options can be used interchangeably.
            """)



    with gr.Row():
        gr_button = gr.Button("Run Pose Comparison")

    with gr.Row():
        gr.HTML("<div style='height: 100px;'></div>")

    
    with gr.Row():
        output_merged = gr.Video(show_download_button=True)

    with gr.Row():
        general_log = gr.TextArea(lines=10, max_lines=9999, label="Error log")

    gr_button.click(
        fn=video_identity,
        inputs=[dtw_mean, dtw_filter, angles_sensitive, angles_common, angles_insensitive, trigger_state, input_teacher, input_student],
        outputs=[output_merged, general_log]
    )


if __name__ == "__main__":
    demo.launch()