import os
import gradio as gr
import subprocess
import datetime
import sys

def run_command(command):
    """Run a shell command and print its output."""
    print(f"Running command: {' '.join(command)}")
    try:
        subprocess.check_call(command, shell=True)
    except subprocess.CalledProcessError as e:
        print(f"Error running command {command}: {e}")
        sys.exit(1)

def check_for_mp4_in_outputs(given_folder):
    # Define the path to the outputs folder
    outputs_folder = given_folder
    
    # Check if the outputs folder exists
    if not os.path.exists(outputs_folder):
        return None
    
    # Check if there is a .mp4 file in the outputs folder
    mp4_files = [f for f in os.listdir(outputs_folder) if f.endswith('.mp4')]
    
    # Return the path to the mp4 file if it exists
    if mp4_files:
        return os.path.join(outputs_folder, mp4_files[0])
    else:
        return None


def infer(input_video, cropped_and_aligned):

    filepath = input_video
    # Get the current timestamp
    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")

    output_folder_name = f"results_{timestamp}"

    if cropped_and_aligned is True:
        # Example: Run the inference script (replace with your actual command)
        run_command(f"{sys.executable} inference_keep.py -i={filepath} -o={output_folder_name} --has_aligned --save_video -s=1")
    else:
        run_command(f"{sys.executable} inference_keep.py -i={filepath} -o={output_folder_name} --draw_box --save_video -s=1 --bg_upsampler=realesrgan")

    # Call the function and print the result
    this_infer_folder = os.path.splitext(os.path.basename(filepath))[0]
    joined_path = os.path.join(output_folder_name, this_infer_folder)
    mp4_file_path = check_for_mp4_in_outputs(joined_path)
    print(mp4_file_path)

    print(f"RESULT: {mp4_file_path}")
    
    return mp4_file_path


result_video = gr.Video()
with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# KEEP")
        gr.Markdown("## Kalman-Inspired Feature Propagation for Video Face Super-Resolution")
        gr.HTML("""
        <div style="display:flex;column-gap:4px;">
            <a href='https://jnjaby.github.io/projects/KEEP/'>
                <img src='https://img.shields.io/badge/Project-Page-Green'>
            </a> 
            <a href='https://arxiv.org/abs/2408.05205'>
                <img src='https://img.shields.io/badge/Paper-Arxiv-red'>
            </a>
        </div>
        """)
        with gr.Row():
            with gr.Column():
                input_video = gr.Video(label="Input Video")
                is_cropped_and_aligned = gr.Checkbox(label="Synthetic data", info="Is your input video ready with cropped and aligned faces ?", value=False)
                submit_btn = gr.Button("Submit")
                gr.Examples(
                    examples = [
                        ["./assets/examples/synthetic_1.mp4", True],
                        ["./assets/examples/synthetic_2.mp4", True],
                        ["./assets/examples/synthetic_3.mp4", True],
                        ["./assets/examples/synthetic_4.mp4", True],
                        ["./assets/examples/real_1.mp4", False],
                        ["./assets/examples/real_2.mp4", False],
                        ["./assets/examples/real_3.mp4", False],
                        ["./assets/examples/real_4.mp4", False]
                    ],
                    fn = infer,
                    inputs = [input_video, is_cropped_and_aligned],
                    outputs = [result_video],
                    run_on_click = False,
                    cache_examples = False
                )
            
            with gr.Column():
                result_video.render()
                

    submit_btn.click(
        fn = infer, 
        inputs = [input_video, is_cropped_and_aligned],
        outputs = [result_video],
        show_api=False
    )

demo.queue().launch(show_error=True, show_api=False)