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# 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))
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