import gradio as gr
import os 
import subprocess

if os.getenv('SYSTEM') == 'spaces':

    subprocess.call('pip install -U openmim'.split())
    subprocess.call('pip install torch==1.11.0 torchvision==0.12.0'.split())
    subprocess.call('mim install mmcv>=2.0.0'.split())
    subprocess.call('mim install mmengine'.split())
    subprocess.call('mim install mmdet'.split())
    subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
    subprocess.call('pip install git+https://github.com/cocodataset/panopticapi.git'.split())
    

import cv2 

import numpy as np
import gradio as gr
 
from inference import inference_frame
import os

def analize_video(x):
    cap = cv2.VideoCapture(x)
    path = '/tmp/test/'
    os.makedirs(path, exist_ok=True)
    videos = len(os.listdir(path))
    path = f'{path}{videos}'
    os.makedirs(path, exist_ok=True)
    outname = f'{path}_processed.mp4'
    #out = cv2.VideoWriter(outname,cv2.VideoWriter_fourcc(*'h264'), 20.0, (640,480))
    counter = 0
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret==True:
            name = os.path.join(path,f'{counter:05d}.png')
            frame = inference_frame(frame)
            # write the flipped frame
            cv2.imwrite(name, frame)
            counter +=1 
        else:
            break
    # Release everything if job is finished
    print(path)
    os.system(f'''ffmpeg -framerate 20 -pattern_type glob -i '{path}/*.png'  -c:v libx264 -pix_fmt yuv420p {outname}''')
    return outname

with gr.Blocks(title='Shark Patrol',theme=gr.themes.Soft(),live=True,) as demo:
    gr.Markdown("Initial DEMO.")
    with gr.Tab("Shark Detector"):
        with gr.Row():
            video_input = gr.Video(source='upload',include_audio=False)
            #video_input.style(witdh='50%',height='50%')
            video_output = gr.Video()
            #video_output.style(witdh='50%',height='50%')
        
        video_button = gr.Button("Analyze")


    with gr.Accordion("Open for More!"):
        gr.Markdown("Place holder for detection")

    video_button.click(analize_video, inputs=video_input, outputs=video_output)
   
demo.queue()
demo.launch(share=True,width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD')))