# import gradio as gr
# import os, subprocess, torchaudio
# import torch
# from PIL import Image

# block = gr.Blocks()

# def pad_image(image):
#     w, h = image.size
#     if w == h:
#         return image
#     elif w > h:
#         new_image = Image.new(image.mode, (w, w), (0, 0, 0))
#         new_image.paste(image, (0, (w - h) // 2))
#         return new_image
#     else:
#         new_image = Image.new(image.mode, (h, h), (0, 0, 0))
#         new_image.paste(image, ((h - w) // 2, 0))
#         return new_image

# def calculate(image_in, audio_in):
#     waveform, sample_rate = torchaudio.load(audio_in)
#     waveform = torch.mean(waveform, dim=0, keepdim=True)
#     torchaudio.save("/content/audio.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
#     image = Image.open(image_in)
#     image = pad_image(image)
#     image.save("image.png")

#     pocketsphinx_run = subprocess.run(['pocketsphinx', '-phone_align', 'yes', 'single', '/content/audio.wav'], check=True, capture_output=True)
#     jq_run = subprocess.run(['jq', '[.w[]|{word: (.t | ascii_upcase | sub("<S>"; "sil") | sub("<SIL>"; "sil") | sub("\\\(2\\\)"; "") | sub("\\\(3\\\)"; "") | sub("\\\(4\\\)"; "") | sub("\\\[SPEECH\\\]"; "SIL") | sub("\\\[NOISE\\\]"; "SIL")), phones: [.w[]|{ph: .t | sub("\\\+SPN\\\+"; "SIL") | sub("\\\+NSN\\\+"; "SIL"), bg: (.b*100)|floor, ed: (.b*100+.d*100)|floor}]}]'], input=pocketsphinx_run.stdout, capture_output=True)
#     with open("test.json", "w") as f:
#         f.write(jq_run.stdout.decode('utf-8').strip())
#     # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#     os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/image.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
#     return "/content/train/image_audio.mp4"
    
# def run():
#   with block:
    
#     with gr.Group():
#       with gr.Box():
#         with gr.Row().style(equal_height=True):
#           image_in = gr.Image(show_label=False, type="filepath")
#           audio_in = gr.Audio(show_label=False, type='filepath')
#           video_out = gr.Video(show_label=False)
#         with gr.Row().style(equal_height=True):
#           btn = gr.Button("Generate")          

    
#     btn.click(calculate, inputs=[image_in, audio_in], outputs=[video_out])
#     block.queue()
#     block.launch(server_name="0.0.0.0", server_port=7860)

# if __name__ == "__main__":
#     run()

import torch
print(torch.cuda.is_available())

print(torch.cuda.device_count())
print(torch.device(cpu))