app.py

import gradio as gr
import os
import subprocess
import spaces
from typing import Tuple, List, Dict
from pydub import AudioSegment

@spaces.GPU
def inference(audio_file: str, model_name: str, vocals: bool, drums: bool, bass: bool, other: bool, mp3: bool, mp3_bitrate: int) -> Tuple[str, gr.HTML]:
    log_messages = []

    def stream_log(message):
        log_messages.append(f"[{model_name}] {message}")
        return gr.HTML("<pre style='margin-bottom: 0;'>" + "<br>".join(log_messages) + "</pre>")

    yield None, stream_log("Starting separation process...")
    yield None, stream_log(f"Loading audio file: {audio_file}")
    
    if audio_file is None:
        yield None, stream_log("Error: No audio file provided")
        raise gr.Error("Please upload an audio file")

    output_dir = os.path.join("separated", model_name, os.path.splitext(os.path.basename(audio_file))[0])
    os.makedirs(output_dir, exist_ok=True)

    # Construct the Demucs command
    cmd = [
        "python", "-m", "demucs", 
        "--out", output_dir,
        "-n", model_name,
        audio_file
    ]

    yield None, stream_log(f"Running Demucs command: {' '.join(cmd)}")

    try:
        # Run the Demucs command
        process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)
        
        # Stream the output
        for line in process.stdout:
            yield None, stream_log(line.strip())
        
        # Wait for the process to complete
        process.wait()

        if process.returncode != 0:
            error_output = process.stderr.read()
            yield None, stream_log(f"Error: Demucs command failed with return code {process.returncode}")
            yield None, stream_log(f"Error output: {error_output}")
            raise gr.Error(f"Demucs separation failed. Check the logs for details.")

    except Exception as e:
        yield None, stream_log(f"Unexpected error: {str(e)}")
        raise gr.Error(f"An unexpected error occurred: {str(e)}")

    yield None, stream_log("Separation completed. Processing stems...")

    stems: Dict[str, str] = {}
    for stem in ["vocals", "drums", "bass", "other"]:
        stem_path = os.path.join(output_dir, model_name, f"{stem}.wav")
        if os.path.exists(stem_path):
            stems[stem] = stem_path
            yield None, stream_log(f"Found {stem} stem")

    selected_stems: List[str] = [stems[stem] for stem in stems if locals()[stem]]
    if not selected_stems:
        raise gr.Error("Please select at least one stem to mix.")

    output_file: str = os.path.join(output_dir, "mixed.wav")
    yield None, stream_log("Mixing selected stems...")
    
    if len(selected_stems) == 1:
        os.rename(selected_stems[0], output_file)
    else:
        mixed_audio: AudioSegment = AudioSegment.empty()
        for stem_path in selected_stems:
            mixed_audio += AudioSegment.from_wav(stem_path)
        mixed_audio.export(output_file, format="wav")

    if mp3:
        yield None, stream_log(f"Converting to MP3 (bitrate: {mp3_bitrate}k)...")
        mp3_output_file: str = os.path.splitext(output_file)[0] + ".mp3"
        mixed_audio.export(mp3_output_file, format="mp3", bitrate=str(mp3_bitrate) + "k")
        output_file = mp3_output_file

    yield None, stream_log("Process completed successfully!")
    yield output_file, gr.HTML("<pre style='color: green;'>Separation and mixing completed successfully!</pre>")

# Define the Gradio interface
with gr.Blocks() as iface:
    gr.Markdown("# Demucs Music Source Separation and Mixing")
    gr.Markdown("Separate vocals, drums, bass, and other instruments from your music using Demucs and mix the selected stems.")
    
    with gr.Row():
        with gr.Column(scale=1):
            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
            model_dropdown = gr.Dropdown(
                ["htdemucs", "htdemucs_ft", "htdemucs_6s", "hdemucs_mmi", "mdx", "mdx_extra", "mdx_q", "mdx_extra_q"],
                label="Model Name",
                value="htdemucs_ft"
            )
            with gr.Row():
                vocals_checkbox = gr.Checkbox(label="Vocals", value=True)
                drums_checkbox = gr.Checkbox(label="Drums", value=True)
            with gr.Row():
                bass_checkbox = gr.Checkbox(label="Bass", value=True)
                other_checkbox = gr.Checkbox(label="Other", value=True)
            mp3_checkbox = gr.Checkbox(label="Save as MP3", value=False)
            mp3_bitrate = gr.Slider(128, 320, step=32, label="MP3 Bitrate", visible=False)
            submit_btn = gr.Button("Process", variant="primary")

        with gr.Column(scale=1):
            output_audio = gr.Audio(type="filepath", label="Processed Audio")
            separation_log = gr.HTML()

    submit_btn.click(
        fn=inference,
        inputs=[audio_input, model_dropdown, vocals_checkbox, drums_checkbox, bass_checkbox, other_checkbox, mp3_checkbox, mp3_bitrate],
        outputs=[output_audio, separation_log]
    )
    
    mp3_checkbox.change(
        fn=lambda mp3: gr.update(visible=mp3),
        inputs=mp3_checkbox,
        outputs=mp3_bitrate
    )

# Launch the Gradio interface
iface.launch()