import shlex
import subprocess
import spaces
import torch

# install packages for mamba
def install_mamba():
    #subprocess.run(shlex.split("pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118"))
    #subprocess.run(shlex.split("pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.4.0/causal_conv1d-1.4.0+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
    subprocess.run(shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v2.2.2/mamba_ssm-2.2.2+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
    subprocess.run(shlex.split("pip install numpy==1.26.4"))

    subprocess.run(shlex.split("ls"))


install_mamba()


import gradio as gr
import torch
import yaml
import librosa
import librosa.display
import matplotlib
from huggingface_hub import hf_hub_download
from models.stfts    import mag_phase_stft, mag_phase_istft
from models.generator import SEMamba
from models.pcs400   import cal_pcs

# download model files from your HF repo
#ckpt  = hf_hub_download("rc19477/Speech_Enhancement_Mamba",
#                        "ckpts/SEMamba_advanced.pth")
#cfg_f = hf_hub_download("rc19477/Speech_Enhancement_Mamba",
#                        "recipes/SEMamba_advanced.yaml")

ckpt = "ckpts/SEMamba_advanced.pth"
cfg_f = "recipes/SEMamba_advanced.yaml"


# load config
with open(cfg_f) as f:
    cfg = yaml.safe_load(f)

stft_cfg    = cfg["stft_cfg"]
model_cfg   = cfg["model_cfg"]
sr          = stft_cfg["sampling_rate"]
n_fft       = stft_cfg["n_fft"]
hop_size    = stft_cfg["hop_size"]
win_size    = stft_cfg["win_size"]
compress_ff = model_cfg["compress_factor"]

# init model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model  = SEMamba(cfg).to(device)
sdict  = torch.load(ckpt, map_location=device)
model.load_state_dict(sdict["generator"])
model.eval()

   
@spaces.GPU
def enhance(audio):
    if audio is None: return None, None
    orig_sr, wav_np = audio
    if orig_sr != sr:
        wav_np = librosa.resample(wav_np, orig_sr, sr)
    wav = torch.from_numpy(wav_np).float().to(device)
    norm = torch.sqrt(len(wav) / torch.sum(wav ** 2))
    wav = (wav * norm).unsqueeze(0)

    amp, pha, _ = mag_phase_stft(wav, **stft_cfg, compress_factor=model_cfg["compress_factor"])
    amp_g, pha_g = model(amp, pha)
    out = mag_phase_istft(amp_g, pha_g, **stft_cfg, compress_factor=model_cfg["compress_factor"])
    out = (out / norm).squeeze().cpu().numpy()
    if orig_sr != sr:
        out = librosa.resample(out, sr, orig_sr)

    # spectrogram
    D = librosa.stft(out, n_fft=1024, hop_length=512)
    S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max)
    fig, ax = plt.subplots(figsize=(6, 3))
    librosa.display.specshow(S_db, sr=orig_sr, hop_length=512, x_axis='time', y_axis='hz', ax=ax)
    ax.set_title("Enhanced Spectrogram")
    plt.colorbar(format="%+2.0f dB", ax=ax)
    return (orig_sr, out), fig

# --- Layout with Blocks ---
with gr.Blocks(css=".gr-box {border: none !important}") as demo:
    gr.Markdown("<h1 style='text-align: center;'>🎧 <a href='https://github.com/RoyChao19477/SEMamba' target='_blank'>SEMamba</a>: Speech Enhancement</h1>")
    gr.Markdown("Enhance real-world noisy speech using Mamba. Upload or record an audio clip and view the spectrogram.")

    with gr.Row():
        with gr.Column():
            audio_input = gr.Audio(sources=["upload", "microphone"], type="numpy", label="Upload or Record", elem_id="input-audio")
            run_btn = gr.Button("Enhance Now 🚀", variant="primary")

        with gr.Column():
            enhanced_audio = gr.Audio(label="Enhanced Output", type="numpy")
            spec_plot = gr.Plot(label="Spectrogram")

    run_btn.click(enhance, inputs=audio_input, outputs=[enhanced_audio, spec_plot])

    gr.Examples(
        examples=[
            ["examples/noisy_sample_16k.wav"],
        ],
        inputs=audio_input,
        outputs=[enhanced_audio, spec_plot],
        fn=enhance,
        cache_examples=True,
        label="📂 Try These Examples"
    )

    gr.Markdown("<p style='text-align: center'><a href='https://arxiv.org/abs/2405.15144' target='_blank'>📄 SEMamba: Mamba for Long-Context Speech Enhancement (SLT 2024)</a></p>")

demo.launch()