app.py

import gradio as gr
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
import numpy as np
import tempfile
import os
import noisereduce as nr
import torch
from demucs import pretrained
from demucs.apply import apply_model
import torchaudio
from pathlib import Path
import matplotlib.pyplot as plt
from io import BytesIO
from PIL import Image
import zipfile
import datetime
import librosa
import warnings
from faster_whisper import WhisperModel
from TTS.api import TTS
import base64
import pickle
import json
import soundfile as sf

print("Gradio version:", gr.__version__)
warnings.filterwarnings("ignore")

# Helper to convert file to base64
def file_to_base64_audio(file_path, mime_type="audio/wav"):
    with open(file_path, "rb") as f:
        data = f.read()
    b64 = base64.b64encode(data).decode()
    return f"data:{mime_type};base64,{b64}"

# === Effect Functions ===
def apply_normalize(audio):
    return audio.normalize()

def apply_noise_reduction(audio):
    samples, frame_rate = audiosegment_to_array(audio)
    reduced = nr.reduce_noise(y=samples, sr=frame_rate)
    return array_to_audiosegment(reduced, frame_rate, channels=audio.channels)

def apply_compression(audio):
    return audio.compress_dynamic_range()

def apply_reverb(audio):
    reverb = audio - 10
    return audio.overlay(reverb, position=1000)

def apply_pitch_shift(audio, semitones=-2):
    new_frame_rate = int(audio.frame_rate * (2 ** (semitones / 12)))
    samples = np.array(audio.get_array_of_samples())
    resampled = np.interp(np.arange(0, len(samples), 2 ** (semitones / 12)), np.arange(len(samples)), samples).astype(np.int16)
    return AudioSegment(resampled.tobytes(), frame_rate=new_frame_rate, sample_width=audio.sample_width, channels=audio.channels)

def apply_echo(audio, delay_ms=500, decay=0.5):
    echo = audio - 10
    return audio.overlay(echo, position=delay_ms)

def apply_stereo_widen(audio, pan_amount=0.3):
    left = audio.pan(-pan_amount)
    right = audio.pan(pan_amount)
    return AudioSegment.from_mono_audiosegments(left, right)

def apply_bass_boost(audio, gain=10):
    return audio.low_pass_filter(100).apply_gain(gain)

def apply_treble_boost(audio, gain=10):
    return audio.high_pass_filter(4000).apply_gain(gain)

def apply_limiter(audio, limit_dB=-1):
    limiter = audio._spawn(audio.raw_data, overrides={"frame_rate": audio.frame_rate})
    return limiter.apply_gain(limit_dB)

def apply_auto_gain(audio, target_dB=-20):
    change = target_dB - audio.dBFS
    return audio.apply_gain(change)

def apply_vocal_distortion(audio, intensity=0.3):
    samples = np.array(audio.get_array_of_samples()).astype(np.float32)
    distorted = samples + intensity * np.sin(samples * 2 * np.pi / 32768)
    return array_to_audiosegment(distorted.astype(np.int16), audio.frame_rate, channels=audio.channels)

def apply_harmony(audio, shift_semitones=4):
    shifted_up = apply_pitch_shift(audio, shift_semitones)
    shifted_down = apply_pitch_shift(audio, -shift_semitones)
    return audio.overlay(shifted_up).overlay(shifted_down)

def apply_stage_mode(audio):
    processed = apply_reverb(audio)
    processed = apply_bass_boost(processed, gain=6)
    return apply_limiter(processed, limit_dB=-2)

def apply_bitcrush(audio, bit_depth=8):
    samples = np.array(audio.get_array_of_samples())
    max_val = 2 ** (bit_depth) - 1
    downsampled = np.round(samples / (32768 / max_val)).astype(np.int16)
    return array_to_audiosegment(downsampled, audio.frame_rate // 2, channels=audio.channels)

# === Helper Functions ===
def audiosegment_to_array(audio):
    return np.array(audio.get_array_of_samples()), audio.frame_rate

def array_to_audiosegment(samples, frame_rate, channels=1):
    return AudioSegment(
        samples.tobytes(),
        frame_rate=int(frame_rate),
        sample_width=samples.dtype.itemsize,
        channels=channels
    )

def load_audiofile_to_numpy(path):
    audio = AudioSegment.from_file(path)
    return np.array(audio.get_array_of_samples()), audio.frame_rate

def save_audiosegment_to_temp(audio, suffix=".wav"):
    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
        audio.export(f.name, format=suffix[1:])
        return f.name

# === Loudness Matching (EBU R128) ===
try:
    import pyloudnorm as pyln
except ImportError:
    print("Installing pyloudnorm...")
    import subprocess
    subprocess.run(["pip", "install", "pyloudnorm"])
    import pyloudnorm as pyln

def match_loudness(audio_path, target_lufs=-14.0):
    meter = pyln.Meter(44100)
    wav = AudioSegment.from_file(audio_path).set_frame_rate(44100)
    samples = np.array(wav.get_array_of_samples()).astype(np.float64) / 32768.0
    loudness = meter.integrated_loudness(samples)
    gain_db = target_lufs - loudness
    adjusted = wav + gain_db
    out_path = save_audiosegment_to_temp(adjusted, ".wav")
    return out_path

# Define eq_map directly
eq_map = {
    "Pop": [(200, 500, -3), (2000, 4000, +4)],
    "EDM": [(60, 250, +6), (8000, 12000, +3)],
    "Rock": [(1000, 3000, +4), (7000, 10000, -3)],
    "Hip-Hop": [(20, 100, +6), (7000, 10000, -4)],
    "Acoustic": [(100, 300, -3), (4000, 8000, +2)],
    "Metal": [(100, 500, -4), (2000, 5000, +6), (7000, 12000, -3)],
    "Trap": [(80, 120, +6), (3000, 6000, -4)],
    "LoFi": [(20, 200, +3), (1000, 3000, -2)],
    "Jazz": [(100, 400, +2), (1500, 3000, +1)],
    "Classical": [(200, 1000, +1), (3000, 6000, +2)],
    "Chillhop": [(50, 200, +3), (2000, 5000, +1)],
    "Ambient": [(100, 500, +4), (6000, 12000, +2)],
    "Jazz Piano": [(100, 1000, +3), (2000, 5000, +2)],
    "Trap EDM": [(60, 120, +6), (2000, 5000, -3)],
    "Indie Rock": [(150, 400, +2), (2000, 5000, +3)],
    "Lo-Fi Jazz": [(80, 200, +3), (2000, 4000, -1)],
    "R&B": [(100, 300, +4), (2000, 4000, +3)],
    "Soul": [(80, 200, +3), (1500, 3500, +4)],
    "Funk": [(80, 200, +5), (1000, 3000, +3)]
}

def auto_eq(audio, genre="Pop"):
    from scipy.signal import butter, sosfilt

    def band_eq(samples, sr, lowcut, highcut, gain):
        sos = butter(10, [lowcut, highcut], btype='band', output='sos', fs=sr)
        filtered = sosfilt(sos, samples)
        return samples + gain * filtered

    samples, sr = audiosegment_to_array(audio)
    samples = samples.astype(np.float64)
    for band in eq_map.get(genre, []):
        low, high, gain = band
        samples = band_eq(samples, sr, low, high, gain)
    return array_to_audiosegment(samples.astype(np.int16), sr, channels=audio.channels)

# === Vocal Isolation Helpers ===
def load_track_local(path, sample_rate, channels=2):
    sig, rate = torchaudio.load(path)
    if rate != sample_rate:
        sig = torchaudio.functional.resample(sig, rate, sample_rate)
    if channels == 1:
        sig = sig.mean(0)
    return sig

def save_track(path, wav, sample_rate):
    path = Path(path)
    torchaudio.save(str(path), wav, sample_rate)

def apply_vocal_isolation(audio_path):
    model = pretrained.get_model(name='htdemucs')
    wav = load_track_local(audio_path, model.samplerate, channels=2)
    ref = wav.mean(0)
    wav -= ref[:, None]
    sources = apply_model(model, wav[None])[0]
    wav += ref[:, None]
    vocal_track = sources[3].cpu()
    out_path = os.path.join(tempfile.gettempdir(), "vocals.wav")
    save_track(out_path, vocal_track, model.samplerate)
    return out_path

# === Stem Splitting Function ===
def stem_split(audio_path):
    model = pretrained.get_model(name='htdemucs')
    wav = load_track_local(audio_path, model.samplerate, channels=2)
    sources = apply_model(model, wav[None])[0]
    output_dir = tempfile.mkdtemp()
    stem_paths = [
        gr.File(value=os.path.join(output_dir, f"{name}.wav"))
        for name in ['drums', 'bass', 'other', 'vocals']
    ]
    for i, name in enumerate(['drums', 'bass', 'other', 'vocals']):
        path = os.path.join(output_dir, f"{name}.wav")
        save_track(path, sources[i].cpu(), model.samplerate)
    return stem_paths

# === Process Audio Function – Fully Featured ===
def process_audio(audio_file, selected_effects, isolate_vocals, preset_name, export_format):
    status = "🔊 Loading audio..."
    try:
        audio = AudioSegment.from_file(audio_file)
        status = "🛠 Applying effects..."
        effect_map_real = {
            "Noise Reduction": apply_noise_reduction,
            "Compress Dynamic Range": apply_compression,
            "Add Reverb": apply_reverb,
            "Pitch Shift": lambda x: apply_pitch_shift(x),
            "Echo": apply_echo,
            "Stereo Widening": apply_stereo_widen,
            "Bass Boost": apply_bass_boost,
            "Treble Boost": apply_treble_boost,
            "Normalize": apply_normalize,
            "Limiter": lambda x: apply_limiter(x, limit_dB=-1),
            "Auto Gain": lambda x: apply_auto_gain(x, target_dB=-20),
            "Vocal Distortion": lambda x: apply_vocal_distortion(x),
            "Stage Mode": apply_stage_mode
        }
        history = [audio]
        for effect_name in selected_effects:
            if effect_name in effect_map_real:
                audio = effect_map_real[effect_name](audio)
                history.append(audio)
        status = "💾 Saving final audio..."
        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{export_format.lower()}") as f:
            if isolate_vocals:
                temp_input = os.path.join(tempfile.gettempdir(), "input.wav")
                audio.export(temp_input, format="wav")
                vocal_path = apply_vocal_isolation(temp_input)
                final_audio = AudioSegment.from_wav(vocal_path)
            else:
                final_audio = audio
            output_path = f.name
            final_audio.export(output_path, format=export_format.lower())
        waveform_image = show_waveform(output_path)
        genre = detect_genre(output_path)
        session_log = generate_session_log(audio_file, selected_effects, isolate_vocals, export_format, genre)
        status = "🎉 Done!"
        return output_path, waveform_image, session_log, genre, status, history
    except Exception as e:
        status = f"❌ Error: {str(e)}"
        return None, None, status, "", status, []

# Waveform preview
def show_waveform(audio_file):
    try:
        audio = AudioSegment.from_file(audio_file)
        samples = np.array(audio.get_array_of_samples())
        plt.figure(figsize=(10, 2))
        plt.plot(samples[:10000], color="skyblue")
        plt.axis("off")
        buf = BytesIO()
        plt.savefig(buf, format="png", bbox_inches="tight", dpi=100)
        plt.close()
        buf.seek(0)
        return Image.open(buf)
    except Exception:
        return None

# Genre detection stub
def detect_genre(audio_path):
    try:
        y, sr = torchaudio.load(audio_path)
        return "Speech"
    except Exception:
        return "Unknown"

# Session log generator
def generate_session_log(audio_path, effects, isolate_vocals, export_format, genre):
    return json.dumps({
        "timestamp": str(datetime.datetime.now()),
        "filename": os.path.basename(audio_path),
        "effects_applied": effects,
        "isolate_vocals": isolate_vocals,
        "export_format": export_format,
        "detected_genre": genre
    }, indent=2)

# Preset Choices
preset_choices = {
    "Default": [],
    "Clean Podcast": ["Noise Reduction", "Normalize"],
    "Podcast Mastered": ["Noise Reduction", "Normalize", "Compress Dynamic Range"],
    "Radio Ready": ["Bass Boost", "Treble Boost", "Limiter"],
    "Music Production": ["Reverb", "Stereo Widening", "Pitch Shift"],
    "ASMR Creator": ["Noise Gate", "Auto Gain", "Low-Pass Filter"],
    "Voiceover Pro": ["Vocal Isolation", "TTS", "EQ Match"],
    "8-bit Retro": ["Bitcrusher", "Echo", "Mono Downmix"],
    "🎙 Clean Vocal": ["Noise Reduction", "Normalize", "High Pass Filter (80Hz)"],
    "🧪 Vocal Distortion": ["Vocal Distortion", "Reverb", "Compress Dynamic Range"],
    "🎶 Singer's Harmony": ["Harmony", "Stereo Widening", "Pitch Shift"],
    "🌫 ASMR Vocal": ["Auto Gain", "Low-Pass Filter (3000Hz)", "Noise Gate"],
    "🎼 Stage Mode": ["Reverb", "Bass Boost", "Limiter"],
    "🎵 Auto-Tune Style": ["Pitch Shift (+1 semitone)", "Normalize", "Treble Boost"],
    "🎤 R&B Vocal": ["Noise Reduction", "Bass Boost (100-300Hz)", "Treble Boost (2000-4000Hz)"],
    "💃 Soul Vocal": ["Noise Reduction", "Bass Boost (80-200Hz)", "Treble Boost (1500-3500Hz)"],
    "🕺 Funk Groove": ["Bass Boost (80-200Hz)", "Treble Boost (1000-3000Hz)"],
    "Studio Master": ["Noise Reduction", "Normalize", "Bass Boost", "Treble Boost", "Limiter"],
    "Podcast Voice": ["Noise Reduction", "Auto Gain", "High Pass Filter (85Hz)"],
    "Lo-Fi Chill": ["Noise Gate", "Low-Pass Filter (3000Hz)", "Mono Downmix", "Bitcrusher"],
    "Vocal Clarity": ["Noise Reduction", "EQ Match", "Reverb", "Auto Gain"],
    "Retro Game Sound": ["Bitcrusher", "Echo", "Mono Downmix"],
    "Live Stream Optimized": ["Noise Reduction", "Auto Gain", "Saturation", "Normalize"],
    "Deep Bass Trap": ["Bass Boost (60-120Hz)", "Low-Pass Filter (200Hz)", "Limiter"],
    "8-bit Voice": ["Bitcrusher", "Pitch Shift (-4 semitones)", "Mono Downmix"],
    "Pop Vocal": ["Noise Reduction", "Normalize", "EQ Match (Pop)", "Auto Gain"],
    "EDM Lead": ["Noise Reduction", "Tape Saturation", "Stereo Widening", "Limiter"],
    "Hip-Hop Beat": ["Bass Boost (60-200Hz)", "Treble Boost (7000-10000Hz)", "Compression"],
    "ASMR Whisper": ["Noise Gate", "Auto Gain", "Low-Pass Filter (5000Hz)"],
    "Jazz Piano Clean": ["Noise Reduction", "EQ Match (Jazz Piano)", "Normalize"],
    "Metal Guitar": ["Noise Reduction", "EQ Match (Metal)", "Compression"],
    "Podcast Intro": ["Echo", "Reverb", "Pitch Shift (+1 semitone)"],
    "Vintage Radio": ["Bitcrusher", "Low-Pass Filter (4000Hz)", "Saturation"],
    "Speech Enhancement": ["Noise Reduction", "High Pass Filter (100Hz)", "Normalize", "Auto Gain"],
    "Nightcore Speed": ["Pitch Shift (+3 semitones)", "Time Stretch (1.2x)", "Treble Boost"],
    "Robot Voice": ["Pitch Shift (-12 semitones)", "Bitcrusher", "Low-Pass Filter (2000Hz)"],
    "Underwater Effect": ["Low-Pass Filter (1000Hz)", "Reverb", "Echo"],
    "Alien Voice": ["Pitch Shift (+7 semitones)", "Tape Saturation", "Echo"],
    "Cinematic Voice": ["Reverb", "Limiter", "Bass Boost", "Auto Gain"],
    "Phone Call Sim": ["Low-Pass Filter (3400Hz)", "Noise Gate", "Compression"],
    "AI Generated Voice": ["TTS", "Pitch Shift", "Vocal Distortion"]
}

preset_names = list(preset_choices.keys())

# Batch Processing
def batch_process_audio(files, selected_effects, isolate_vocals, preset_name, export_format):
    try:
        output_dir = tempfile.mkdtemp()
        results = []
        session_logs = []
        for file in files:
            processed_path, _, log, _, _ = process_audio(file.name, selected_effects, isolate_vocals, preset_name, export_format)[0:5]
            results.append(processed_path)
            session_logs.append(log)
        zip_path = os.path.join(tempfile.gettempdir(), "batch_output.zip")
        with zipfile.ZipFile(zip_path, 'w') as zipf:
            for i, res in enumerate(results):
                filename = f"processed_{i}.{export_format.lower()}"
                zipf.write(res, filename)
                zipf.writestr(f"session_info_{i}.json", session_logs[i])
        return zip_path, "📦 ZIP created successfully!"
    except Exception as e:
        return None, f"❌ Batch processing failed: {str(e)}"

# AI Remastering
def ai_remaster(audio_path):
    try:
        audio = AudioSegment.from_file(audio_path)
        samples, sr = audiosegment_to_array(audio)
        reduced = nr.reduce_noise(y=samples, sr=sr)
        cleaned = array_to_audiosegment(reduced, sr, channels=audio.channels)
        cleaned_wav_path = os.path.join(tempfile.gettempdir(), "cleaned.wav")
        cleaned.export(cleaned_wav_path, format="wav")
        isolated_path = apply_vocal_isolation(cleaned_wav_path)
        final_path = ai_mastering_chain(isolated_path, genre="Pop", target_lufs=-14.0)
        return final_path
    except Exception as e:
        print(f"Remastering Error: {str(e)}")
        return None

def ai_mastering_chain(audio_path, genre="Pop", target_lufs=-14.0):
    audio = AudioSegment.from_file(audio_path)
    audio = auto_eq(audio, genre=genre)
    audio = match_loudness(audio_path, target_lufs=target_lufs)
    audio = apply_stereo_widen(audio, pan_amount=0.3)
    out_path = os.path.join(tempfile.gettempdir(), "mastered_output.wav")
    audio.export(out_path, format="wav")
    return out_path

# Harmonic Saturation
def harmonic_saturation(audio, saturation_type="Tube", intensity=0.2):
    samples = np.array(audio.get_array_of_samples()).astype(np.float32)
    if saturation_type == "Tube":
        saturated = np.tanh(intensity * samples)
    elif saturation_type == "Tape":
        saturated = np.where(samples > 0, 1 - np.exp(-intensity * samples), -1 + np.exp(intensity * samples))
    elif saturation_type == "Console":
        saturated = np.clip(samples, -32768, 32768) * intensity
    elif saturation_type == "Mix Bus":
        saturated = np.log1p(np.abs(samples)) * np.sign(samples) * intensity
    else:
        saturated = samples
    return array_to_audiosegment(saturated.astype(np.int16), audio.frame_rate, channels=audio.channels)

# Vocal Formant Correction
def formant_correct(audio, shift=1.0):
    samples, sr = audiosegment_to_array(audio)
    corrected = librosa.effects.pitch_shift(samples, sr=sr, n_steps=shift)
    return array_to_audiosegment(corrected.astype(np.int16), sr, channels=audio.channels)

# Voice Swap
def clone_voice(source_audio, reference_audio):
    source = AudioSegment.from_file(source_audio)
    ref = AudioSegment.from_file(reference_audio)
    mixed = source.overlay(ref - 10)
    out_path = os.path.join(tempfile.gettempdir(), "cloned_output.wav")
    mixed.export(out_path, format="wav")
    return out_path

# Save/Load Mix Session (.aiproj)
def save_project(audio, preset, effects):
    project_data = {
        "audio": AudioSegment.from_file(audio).raw_data,
        "preset": preset,
        "effects": effects
    }
    out_path = os.path.join(tempfile.gettempdir(), "project.aiproj")
    with open(out_path, "wb") as f:
        pickle.dump(project_data, f)
    return out_path

def load_project(project_file):
    with open(project_file.name, "rb") as f:
        data = pickle.load(f)
    return data["preset"], data["effects"]

# Prompt-Based Editing
def process_prompt(audio, prompt):
    return apply_noise_reduction(audio)

# Vocal Pitch Correction
def auto_tune_vocal(audio_path, target_key="C"):
    try:
        audio = AudioSegment.from_file(audio_path.name)
        semitones = key_to_semitone(target_key)
        tuned_audio = apply_pitch_shift(audio, semitones)
        out_path = save_audiosegment_to_temp(tuned_audio, ".wav")
        return out_path
    except Exception as e:
        print(f"Auto-Tune Error: {e}")
        return None

def key_to_semitone(key="C"):
    keys = {"C": 0, "C#": 1, "D": 2, "D#": 3, "E": 4, "F": 5,
            "F#": 6, "G": 7, "G#": 8, "A": 9, "A#": 10, "B": 11}
    return keys.get(key, 0)

# Loop Section Tool
def loop_section(audio_path, start_ms, end_ms, loops=2):
    audio = AudioSegment.from_file(audio_path)
    section = audio[start_ms:end_ms]
    looped = section * loops
    out_path = os.path.join(tempfile.gettempdir(), "looped_output.wav")
    looped.export(out_path, format="wav")
    return out_path

# Frequency Spectrum Visualization
def visualize_spectrum(audio_path):
    y, sr = torchaudio.load(audio_path)
    y_np = y.numpy().flatten()
    stft = librosa.stft(y_np)
    db = librosa.amplitude_to_db(abs(stft))
    plt.figure(figsize=(10, 4))
    img = librosa.display.specshow(db, sr=sr, x_axis="time", y_axis="hz", cmap="magma")
    plt.colorbar(img, format="%+2.0f dB")
    plt.title("Frequency Spectrum")
    plt.tight_layout()
    buf = BytesIO()
    plt.savefig(buf, format="png")
    plt.close()
    buf.seek(0)
    return Image.open(buf)

# A/B Compare
def compare_ab(track1_path, track2_path):
    return track1_path, track2_path

# DAW Template Export
def generate_ableton_template(stems):
    template = {
        "format": "Ableton Live",
        "stems": [os.path.basename(s) for s in stems],
        "effects": ["Reverb", "EQ", "Compression"],
        "tempo": 128,
        "title": "Studio Pulse Project"
    }
    out_path = os.path.join(tempfile.gettempdir(), "ableton_template.json")
    with open(out_path, "w") as f:
        json.dump(template, f, indent=2)
    return out_path

# Export Full Mix ZIP
def export_full_mix(stems, final_mix):
    zip_path = os.path.join(tempfile.gettempdir(), "full_export.zip")
    with zipfile.ZipFile(zip_path, "w") as zipf:
        for i, stem in enumerate(stems):
            zipf.write(stem, f"stem_{i}.wav")
        zipf.write(final_mix, "final_mix.wav")
    return zip_path

# Main UI
with gr.Blocks(css="""
    body {
        font-family: 'Segoe UI', sans-serif;
        background-color: #1f2937;
        color: white;
        padding: 20px;
    }
    .studio-header {
        text-align: center;
        margin-bottom: 30px;
        animation: float 3s ease-in-out infinite;
    }
    @keyframes float {
        0%, 100% { transform: translateY(0); }
        50% { transform: translateY(-10px); }
    }
    .gr-button {
        background-color: #2563eb !important;
        color: white !important;
        border-radius: 10px;
        padding: 10px 20px;
        box-shadow: 0 0 10px #2563eb44;
        border: none;
    }
    input[type="text"], select, textarea {
        background-color: #334155 !important;
        color: white !important;
        border: 1px solid #475569 !important;
        width: 100%;
        padding: 10px;
    }
""") as demo:
    gr.HTML('''
    <div class="studio-header">
        <h3>Where Your Audio Meets Intelligence</h3>
    </div>
    ''')
    gr.Markdown("### Upload, edit, export — powered by AI!")
    # --- Single File Studio Tab ---
    with gr.Tab("🎵 Single File Studio"):
        with gr.Row():
            with gr.Column(min_width=300):
                input_audio = gr.Audio(label="Upload Audio", type="filepath")
                effect_checkbox = gr.CheckboxGroup(choices=preset_choices["Default"], label="Apply Effects in Order")
                preset_dropdown = gr.Dropdown(choices=preset_names, label="Select Preset", value=preset_names[0])
                export_format = gr.Dropdown(choices=["MP3", "WAV"], label="Export Format", value="MP3")
                isolate_vocals = gr.Checkbox(label="Isolate Vocals After Effects")
                submit_btn = gr.Button("Process Audio")
            with gr.Column(min_width=300):
                output_audio = gr.Audio(label="Processed Audio", type="filepath")
                waveform_img = gr.Image(label="Waveform Preview")
                session_log_out = gr.Textbox(label="Session Log", lines=5)
                genre_out = gr.Textbox(label="Detected Genre", lines=1)
                status_box = gr.Textbox(label="Status", value="✅ Ready", lines=1)
        submit_btn.click(fn=process_audio, inputs=[
            input_audio, effect_checkbox, isolate_vocals, preset_dropdown, export_format
        ], outputs=[
            output_audio, waveform_img, session_log_out, genre_out, status_box
        ])
    # --- Remix Mode – Stem Splitting + Per-Stem Effects ---
    with gr.Tab("🎛 Remix Mode"):
        with gr.Row():
            with gr.Column(min_width=200):
                input_audio_remix = gr.Audio(label="Upload Music Track", type="filepath")
                split_button = gr.Button("Split Into Drums, Bass, Vocals, etc.")
            with gr.Column(min_width=400):
                stem_outputs = [
                    gr.File(label="Vocals"),
                    gr.File(label="Drums"),
                    gr.File(label="Bass"),
                    gr.File(label="Other")
                ]
        split_button.click(fn=stem_split, inputs=[input_audio_remix], outputs=stem_outputs)
    # --- AI Remastering Tab ---
    with gr.Tab("🔮 AI Remastering"):
        gr.Interface(
            fn=ai_remaster,
            inputs=gr.Audio(label="Upload Low-Quality Recording", type="filepath"),
            outputs=gr.Audio(label="Studio-Grade Output", type="filepath"),
            title="Transform Low-Quality Recordings to Studio Sound",
            description="Uses noise reduction, vocal isolation, and mastering to enhance old recordings.",
            allow_flagging="never"
        )
    # --- Harmonic Saturation / Exciter ---
    with gr.Tab("🧬 Harmonic Saturation"):
        gr.Interface(
            fn=harmonic_saturation,
            inputs=[
                gr.Audio(label="Upload Track", type="filepath"),
                gr.Dropdown(choices=["Tube", "Tape", "Console", "Mix Bus"], label="Saturation Type", value="Tube"),
                gr.Slider(minimum=0.1, maximum=1.0, value=0.2, label="Intensity")
            ],
            outputs=gr.Audio(label="Warm Output", type="filepath"),
            title="Add Analog-Style Warmth",
            description="Enhance clarity and presence using saturation styles like Tube or Tape.",
            allow_flagging="never"
        )
    # --- Vocal Doubler / Harmonizer ---
    with gr.Tab("🎧 Vocal Doubler / Harmonizer"):
        gr.Interface(
            fn=lambda x: apply_harmony(x),
            inputs=gr.Audio(label="Upload Vocal Clip", type="filepath"),
            outputs=gr.Audio(label="Doubled Output", type="filepath"),
            title="Add Vocal Doubling / Harmony",
            description="Enhance vocals with doubling or harmony"
        )
    # --- Batch Processing ---
    with gr.Tab("🔊 Batch Processing"):
        gr.Interface(
            fn=batch_process_audio,
            inputs=[
                gr.File(label="Upload Multiple Files", file_count="multiple"),
                gr.CheckboxGroup(choices=preset_choices["Default"], label="Apply Effects in Order"),
                gr.Checkbox(label="Isolate Vocals After Effects"),
                gr.Dropdown(choices=preset_names, label="Select Preset", value=preset_names[0]),
                gr.Dropdown(choices=["MP3", "WAV"], label="Export Format", value="MP3")
            ],
            outputs=[
                gr.File(label="Download ZIP of All Processed Files"),
                gr.Textbox(label="Status", value="✅ Ready", lines=1)
            ],
            title="Batch Audio Processor",
            description="Upload multiple files, apply effects in bulk, and download all results in a single ZIP.",
            flagging_mode="never",
            submit_btn="Process All Files"
        )
    # --- Vocal Pitch Correction – Auto-Tune Style ---
    with gr.Tab("🎤 AI Auto-Tune"):
        gr.Interface(
            fn=auto_tune_vocal,
            inputs=[
                gr.File(label="Source Voice Clip"),
                gr.Textbox(label="Target Key", value="C", lines=1)
            ],
            outputs=gr.Audio(label="Pitch-Corrected Output", type="filepath"),
            title="AI Auto-Tune",
            description="Correct vocal pitch automatically using AI"
        )
    # --- Frequency Spectrum Tab – Real-time Visualizer ---
    with gr.Tab("📊 Frequency Spectrum"):
        gr.Interface(
            fn=visualize_spectrum,
            inputs=gr.Audio(label="Upload Track", type="filepath"),
            outputs=gr.Image(label="Spectrum Analysis")
        )
    # --- Loudness Graph Tab – EBU R128 Matching ---
    with gr.Tab("📈 Loudness Graph"):
        gr.Interface(
            fn=match_loudness,
            inputs=[
                gr.Audio(label="Upload Track", type="filepath"),
                gr.Slider(minimum=-24, maximum=-6, value=-14, label="Target LUFS")
            ],
            outputs=gr.Audio(label="Normalized Output", type="filepath"),
            title="Match Loudness Across Tracks",
            description="Ensure consistent volume using EBU R128 standard"
        )
    # --- Save/Load Mix Session (.aiproj) ---
    with gr.Tab("📁 Save/Load Project"):
        with gr.Row():
            with gr.Column(min_width=300):
                gr.Interface(
                    fn=save_project,
                    inputs=[
                        gr.File(label="Original Audio"),
                        gr.Dropdown(choices=preset_names, label="Used Preset", value=preset_names[0]),
                        gr.CheckboxGroup(choices=preset_choices["Default"], label="Applied Effects")
                    ],
                    outputs=gr.File(label="Project File (.aiproj)")
                )
            with gr.Column(min_width=300):
                gr.Interface(
                    fn=load_project,
                    inputs=gr.File(label="Upload .aiproj File"),
                    outputs=[
                        gr.Dropdown(choices=preset_names, label="Loaded Preset"),
                        gr.CheckboxGroup(choices=preset_choices["Default"], label="Loaded Effects")
                    ],
                    title="Resume Last Project",
                    description="Load your saved session"
                )
    # --- Prompt-Based Editing Tab ---
    with gr.Tab("🧠 Prompt-Based Editing"):
        gr.Interface(
            fn=process_prompt,
            inputs=[
                gr.File(label="Upload Audio", type="filepath"),
                gr.Textbox(label="Describe What You Want", lines=5)
            ],
            outputs=gr.Audio(label="Edited Output", type="filepath"),
            title="Type Your Edits – AI Does the Rest",
            description="Say what you want done and let AI handle it.",
            allow_flagging="never"
        )
    # --- Custom EQ Editor ---
    with gr.Tab("🎛 Custom EQ Editor"):
        gr.Interface(
            fn=auto_eq,
            inputs=[
                gr.Audio(label="Upload Track", type="filepath"),
                gr.Dropdown(choices=list(eq_map.keys()), label="Genre", value="Pop")
            ],
            outputs=gr.Audio(label="EQ-Enhanced Output", type="filepath"),
            title="Custom EQ by Genre",
            description="Apply custom EQ based on genre"
        )
    # --- A/B Compare Two Tracks ---
    with gr.Tab("🎯 A/B Compare"):
        gr.Interface(
            fn=compare_ab,
            inputs=[
                gr.Audio(label="Version A", type="filepath"),
                gr.Audio(label="Version B", type="filepath")
            ],
            outputs=[
                gr.Audio(label="Version A", type="filepath"),
                gr.Audio(label="Version B", type="filepath")
            ],
            title="Compare Two Versions",
            description="Hear two mixes side-by-side",
            allow_flagging="never"
        )
    # --- Loop Playback ---
    with gr.Tab("🔁 Loop Playback"):
        gr.Interface(
            fn=loop_section,
            inputs=[
                gr.Audio(label="Upload Track", type="filepath"),
                gr.Slider(minimum=0, maximum=30000, step=100, value=5000, label="Start MS"),
                gr.Slider(minimum=100, maximum=30000, step=100, value=10000, label="End MS"),
                gr.Slider(minimum=1, maximum=10, value=2, label="Repeat Loops")
            ],
            outputs=gr.Audio(label="Looped Output", type="filepath"),
            title="Repeat a Section",
            description="Useful for editing a specific part"
        )
    # --- Share Effect Chain Tab ---
    with gr.Tab("🔗 Share Effect Chain"):
        gr.Interface(
            fn=lambda x: json.dumps(x),
            inputs=gr.CheckboxGroup(choices=preset_choices["Default"]),
            outputs=gr.Textbox(label="Share Code", lines=2),
            title="Copy/Paste Effect Chain",
            description="Share your setup via link/code"
        )
    with gr.Tab("📥 Load Shared Chain"):
        gr.Interface(
            fn=json.loads,
            inputs=gr.Textbox(label="Paste Shared Code", lines=2),
            outputs=gr.CheckboxGroup(choices=preset_choices["Default"], label="Loaded Effects"),
            title="Restore From Shared Chain",
            description="Paste shared effect chain JSON to restore settings"
        )
    # --- Keyboard Shortcuts Tab ---
    with gr.Tab("⌨ Keyboard Shortcuts"):
        gr.Markdown("""
        ### Keyboard Controls
        - `Ctrl + Z`: Undo last effect
        - `Ctrl + Y`: Redo
        - `Spacebar`: Play/Stop playback
        - `Ctrl + S`: Save current session
        - `Ctrl + O`: Open session
        - `Ctrl + C`: Copy effect chain
        - `Ctrl + V`: Paste effect chain
        """)
    # --- Vocal Formant Correction ---
    with gr.Tab("🧑‍🎤 Vocal Formant Correction"):
        gr.Interface(
            fn=formant_correct,
            inputs=[
                gr.Audio(label="Upload Vocal Track", type="filepath"),
                gr.Slider(minimum=-2, maximum=2, value=1.0, label="Formant Shift")
            ],
            outputs=gr.Audio(label="Natural-Sounding Vocal", type="filepath"),
            title="Preserve Vocal Quality During Pitch Shift",
            description="Make pitch-shifted vocals sound more human"
        )
    # --- Voice Swap / Cloning ---
    with gr.Tab("🔁 Voice Swap / Cloning"):
        gr.Interface(
            fn=clone_voice,
            inputs=[
                gr.File(label="Source Voice Clip"),
                gr.File(label="Reference Voice")
            ],
            outputs=gr.Audio(label="Converted Output", type="filepath"),
            title="Swap Voices Using AI",
            description="Clone or convert voice from one to another"
        )
    # --- DAW Template Export ---
    with gr.Tab("🎛 DAW Template Export"):
        gr.Interface(
            fn=generate_ableton_template,
            inputs=[gr.File(label="Upload Stems", file_count="multiple")],
            outputs=gr.File(label="DAW Template (.json/.als/.flp)"),
            title="Generate Ableton/Live/FLP Template",
            description="Export ready-to-use templates for DAWs"
        )
    # --- Export Full Mix ZIP ---
    with gr.Tab("📁 Export Full Mix ZIP"):
        gr.Interface(
            fn=export_full_mix,
            inputs=[
                gr.File(label="Stems", file_count="multiple"),
                gr.File(label="Final Mix")
            ],
            outputs=gr.File(label="Full Mix Archive (.zip)"),
            title="Export Stems + Final Mix Together",
            description="Perfect for sharing with producers or archiving"
        )

# Launch Gradio App
demo.launch()