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 json
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 mutagen.mp3 import MP3
from mutagen.id3 import ID3, TIT2, TPE1, TALB, TYER
from TTS.api import TTS
import pickle

# Suppress warnings
warnings.filterwarnings("ignore")

# === 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=frame_rate,
        sample_width=samples.dtype.itemsize,
        channels=channels
    )

# === 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_noise_gate(audio, threshold=-50.0, attack=50, release=100):
    samples = np.array(audio.get_array_of_samples())
    rms = np.sqrt(np.mean(samples**2))
    if rms < 1:
        return audio
    normalized = samples / np.max(np.abs(samples))
    envelope = np.abs(normalized)
    gated = np.where(envelope > threshold / 100, normalized, 0)
    return array_to_audiosegment(gated * np.iinfo(np.int16).max, audio.frame_rate, channels=audio.channels)

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_phaser(audio, rate=0.5, depth=0.7, feedback=0.2, mix=0.5):
    return audio._spawn(audio.raw_data, overrides={"frame_rate": int(audio.frame_rate * rate)})

def apply_bitcrush(audio, bit_depth=8):
    samples = np.array(audio.get_array_of_samples()).astype(np.float32)
    max_val = np.iinfo(np.int16).max
    crushed = ((samples / max_val) * (2 ** bit_depth)).astype(np.int16)
    return array_to_audiosegment(crushed, audio.frame_rate, channels=audio.channels)

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)

# === 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 (Drums, Bass, Other, Vocals) ===
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 = []

    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)
        stem_paths.append(gr.File(value=path))

    return stem_paths

# === Preset Loader with Fallback ===
def load_presets():
    try:
        preset_files = [f for f in os.listdir("presets") if f.endswith(".json")]
        presets = {}
        for f in preset_files:
            path = os.path.join("presets", f)
            try:
                with open(path, "r") as infile:
                    data = json.load(infile)
                    if "name" in data and "effects" in data:
                        presets[data["name"]] = data["effects"]
            except json.JSONDecodeError:
                print(f"Invalid JSON: {f}")
        return presets
    except FileNotFoundError:
        print("Presets folder not found")
        return {}

preset_choices = load_presets()

if not 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"],

        # 🎤 Vocalist Presets
        "🎙 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"]
    }

preset_names = list(preset_choices.keys())

# === Waveform + Spectrogram Generator ===
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="blue")
        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 as e:
        return None

def detect_genre(audio_path):
    try:
        y, sr = torchaudio.load(audio_path)
        mfccs = librosa.feature.mfcc(y=y.numpy().flatten(), sr=sr, n_mfcc=13).mean(axis=1).reshape(1, -1)
        return "Speech"
    except Exception:
        return "Unknown"

# === Session Info Export ===
def generate_session_log(audio_path, effects, isolate_vocals, export_format, genre):
    log = {
        "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
    }
    return json.dumps(log, indent=2)

# === Main Processing Function with Status Updates ===
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 = {
            "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,
            "Noise Gate": lambda x: apply_noise_gate(x, threshold=-50.0),
            "Limiter": lambda x: apply_limiter(x, limit_dB=-1),
            "Phaser": lambda x: apply_phaser(x),
            "Flanger": lambda x: apply_phaser(x, rate=1.2, depth=0.9, mix=0.7),
            "Bitcrusher": lambda x: apply_bitcrush(x, bit_depth=8),
            "Auto Gain": lambda x: apply_auto_gain(x, target_dB=-20),
            "Vocal Distortion": lambda x: apply_vocal_distortion(x),
            "Harmony": lambda x: apply_harmony(x),
            "Stage Mode": apply_stage_mode
        }

        effects_to_apply = preset_choices.get(preset_name, selected_effects)
        for effect_name in effects_to_apply:
            if effect_name in effect_map:
                audio = effect_map[effect_name](audio)

        status = "💾 Saving final audio..."
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") 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, effects_to_apply, isolate_vocals, export_format, genre)

            status = "🎉 Done!"
            return output_path, waveform_image, session_log, genre, status

    except Exception as e:
        status = f"❌ Error: {str(e)}"
        return None, None, status, "", status

# === Batch Processing Function ===
def batch_process_audio(files, selected_effects, isolate_vocals, preset_name, export_format):
    status = "🔊 Loading files..."
    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)
            results.append(processed_path)
            session_logs.append(log)

        zip_path = os.path.join(output_dir, "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)}"

# === Transcribe & Edit Tab ===
whisper_model = WhisperModel("base")

def transcribe_audio(audio_path):
    segments, info = whisper_model.transcribe(audio_path, beam_size=5)
    text = " ".join([seg.text for seg in segments])
    return text

# === TTS Voice Generator ===
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False)

def generate_tts(text):
    out_path = os.path.join(tempfile.gettempdir(), "tts_output.wav")
    tts.tts_to_file(text=text, file_path=out_path)
    return out_path

# === Save/Load Project File (.aiproj) ===
def save_project(audio_path, preset_name, effects):
    project_data = {
        "audio": AudioSegment.from_file(audio_path).raw_data,
        "preset": preset_name,
        "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"]

# === Trim Silence Automatically (VAD) ===
def detect_silence(audio_file, silence_threshold=-50.0, min_silence_len=1000):
    audio = AudioSegment.from_file(audio_file)
    
    nonsilent_ranges = detect_nonsilent(
        audio,
        min_silence_len=int(min_silence_len),
        silence_thresh=silence_threshold
    )
    
    if not nonsilent_ranges:
        return audio.export(os.path.join(tempfile.gettempdir(), "trimmed.wav"), format="wav")

    trimmed = audio[nonsilent_ranges[0][0]:nonsilent_ranges[-1][1]]
    out_path = os.path.join(tempfile.gettempdir(), "trimmed.wav")
    trimmed.export(out_path, format="wav")
    return out_path

# === Mix Two Tracks ===
def mix_tracks(track1, track2, volume_offset=0):
    a1 = AudioSegment.from_file(track1)
    a2 = AudioSegment.from_file(track2)
    mixed = a1.overlay(a2 - volume_offset)
    out_path = os.path.join(tempfile.gettempdir(), "mixed.wav")
    mixed.export(out_path, format="wav")
    return out_path

# === Dummy Voice Cloning Tab – Works Locally Only ===
def clone_voice(*args):
    return "⚠️ Voice cloning requires local install – use Python 3.9 or below"

# === Speaker Diarization ("Who Spoke When?") ===
try:
    from pyannote.audio import Pipeline as DiarizationPipeline
    from huggingface_hub import login

    hf_token = os.getenv("HF_TOKEN")
    if hf_token:
        login(token=hf_token)
    diarize_pipeline = DiarizationPipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hf_token or True)
except Exception as e:
    diarize_pipeline = None
    print(f"⚠️ Failed to load diarization: {e}")

def diarize_and_transcribe(audio_path):
    if not diarize_pipeline:
        return "⚠️ Diarization pipeline not loaded – check HF token or install pyannote.audio"

    # Run diarization
    audio = AudioSegment.from_file(audio_path)
    temp_wav = os.path.join(tempfile.gettempdir(), "diarize.wav")
    audio.export(temp_wav, format="wav")

    try:
        from pyannote.audio import Pipeline as DiarizationPipeline
        diarization = diarize_pipeline(temp_wav)

        result = whisper.transcribe(temp_wav)

        segments = []
        for turn, _, speaker in diarization.itertracks(yield_label=True):
            text = " ".join([seg["text"] for seg in result["segments"] if seg["start"] >= turn.start and seg["end"] <= turn.end])
            segments.append({
                "speaker": speaker,
                "start": turn.start,
                "end": turn.end,
                "text": text
            })

        return segments
    except Exception as e:
        return f"⚠️ Diarization failed: {str(e)}"

# === UI ===
effect_options = [
    "Noise Reduction",
    "Compress Dynamic Range",
    "Add Reverb",
    "Pitch Shift",
    "Echo",
    "Stereo Widening",
    "Bass Boost",
    "Treble Boost",
    "Normalize",
    "Noise Gate",
    "Limiter",
    "Phaser",
    "Flanger",
    "Bitcrusher",
    "Auto Gain",
    "Vocal Distortion",
    "Harmony",
    "Stage Mode"
]

with gr.Blocks(title="AI Audio Studio", css="style.css") as demo:
    gr.Markdown("## 🎧 Ultimate AI Audio Studio\nUpload, edit, export — powered by AI!")

    # --- Single File Studio ---
    with gr.Tab("🎵 Single File Studio"):
        gr.Interface(
            fn=process_audio,
            inputs=[
                gr.Audio(label="Upload Audio", type="filepath"),
                gr.CheckboxGroup(choices=effect_options, label="Apply Effects in Order"),
                gr.Checkbox(label="Isolate Vocals After Effects"),
                gr.Dropdown(choices=preset_names, label="Select Preset", value=preset_names[0] if preset_names else None),
                gr.Dropdown(choices=["MP3", "WAV"], label="Export Format", value="MP3")
            ],
            outputs=[
                gr.Audio(label="Processed Audio", type="filepath"),
                gr.Image(label="Waveform Preview"),
                gr.Textbox(label="Session Log (JSON)", lines=5),
                gr.Textbox(label="Detected Genre", lines=1),
                gr.Textbox(label="Status", value="✅ Ready", lines=1)
            ],
            title="Edit One File at a Time",
            description="Apply effects, preview waveform, and get full session log.",
            flagging_mode="never",
            submit_btn="Process Audio",
            clear_btn=None
        )

    # --- 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=effect_options, 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",
            clear_btn=None
        )

    # --- Remix Mode ---
    with gr.Tab("🎛 Remix Mode"):
        gr.Interface(
            fn=stem_split,
            inputs=gr.Audio(label="Upload Music Track", type="filepath"),
            outputs=[
                gr.File(label="Vocals"),
                gr.File(label="Drums"),
                gr.File(label="Bass"),
                gr.File(label="Other")
            ],
            title="Split Into Drums, Bass, Vocals, and More",
            description="Use AI to separate musical elements like vocals, drums, and bass.",
            flagging_mode="never",
            clear_btn=None
        )

    # --- Transcribe & Edit Tab ===
    with gr.Tab("📝 Transcribe & Edit"):
        gr.Interface(
            fn=transcribe_audio,
            inputs=gr.Audio(label="Upload Audio", type="filepath"),
            outputs=gr.Textbox(label="Transcribed Text", lines=10),
            title="Transcribe & Edit Spoken Content",
            description="Convert voice to text and edit it before exporting again."
        )

    # --- Vocal Presets for Singers ===
    with gr.Tab("🎤 Vocal Presets for Singers"):
        gr.Interface(
            fn=process_audio,
            inputs=[
                gr.Audio(label="Upload Vocal Track", type="filepath"),
                gr.CheckboxGroup(choices=[
                    "Noise Reduction",
                    "Normalize",
                    "Compress Dynamic Range",
                    "Bass Boost",
                    "Treble Boost",
                    "Reverb",
                    "Auto Gain",
                    "Vocal Distortion",
                    "Harmony",
                    "Stage Mode"
                ]),
                gr.Checkbox(label="Isolate Vocals After Effects"),
                gr.Dropdown(choices=[
                    "🎙 Clean Vocal",
                    "🧪 Vocal Distortion",
                    "🎶 Singer's Harmony",
                    "🌫 ASMR Vocal",
                    "🎼 Stage Mode",
                    "🎵 Auto-Tune Style"
                ], label="Select Vocal Preset", value="Default"),
                gr.Dropdown(choices=["MP3", "WAV"], label="Export Format", value="MP3")
            ],
            outputs=[
                gr.Audio(label="Processed Vocal", type="filepath"),
                gr.Image(label="Waveform Preview"),
                gr.Textbox(label="Session Log (JSON)", lines=5),
                gr.Textbox(label="Detected Genre", lines=1),
                gr.Textbox(label="Status", value="✅ Ready", lines=1)
            ],
            title="Create Studio-Quality Vocal Tracks",
            description="Apply singer-friendly presets and effects to enhance vocals.",
            allow_flagging="never"
        )

    # --- Voice Cloning (Local Only) ===
    with gr.Tab("🎭 Voice Cloning (Local Only)"):
        gr.Interface(
            fn=clone_voice,
            inputs=[
                gr.File(label="Source Voice Clip"),
                gr.File(label="Target Voice Clip"),
                gr.Textbox(label="Text to Clone", lines=5)
            ],
            outputs=gr.Audio(label="Cloned Output", type="filepath"),
            title="Replace One Voice With Another",
            description="Clone voice from source to target speaker using AI"
        )

    # --- Speaker Diarization (Who Spoke When?) ===
    if diarize_pipeline:
        with gr.Tab("🧏‍♂️ Who Spoke When?"):
            gr.Interface(
                fn=diarize_and_transcribe,
                inputs=gr.Audio(label="Upload Interview/Podcast", type="filepath"),
                outputs=gr.JSON(label="Diarized Transcript"),
                title="Split By Speaker + Transcribe",
                description="Detect speakers and transcribe their speech automatically."
            )

    # --- TTS Voice Generator ===
    with gr.Tab("💬 TTS Voice Generator"):
        gr.Interface(
            fn=generate_tts,
            inputs=gr.Textbox(label="Enter Text", lines=5),
            outputs=gr.Audio(label="Generated Speech", type="filepath"),
            title="Text-to-Speech Generator",
            description="Type anything and turn it into natural-sounding speech."
        )

    # --- Auto-Save / Resume Sessions ===
    session_state = gr.State()

    def save_or_resume_session(audio, preset, effects, action="save"):
        if action == "save":
            return {"audio": audio, "preset": preset, "effects": effects}, None, None, None
        elif action == "load" and isinstance(audio, dict):
            return (
                None,
                audio.get("audio"),
                audio.get("preset"),
                audio.get("effects")
            )
        return None, None, None, None

    with gr.Tab("🧾 Auto-Save & Resume"):
        gr.Markdown("Save your current state and resume later.")

        action_radio = gr.Radio(["save", "load"], label="Action", value="save")
        audio_input = gr.Audio(label="Upload or Load Audio", type="filepath")
        preset_dropdown = gr.Dropdown(choices=preset_names, label="Used Preset", value=preset_names[0] if preset_names else None)
        effect_checkbox = gr.CheckboxGroup(choices=effect_options, label="Applied Effects")
        action_btn = gr.Button("Save or Load Session")

        session_data = gr.State()
        loaded_audio = gr.Audio(label="Loaded Audio", type="filepath")
        loaded_preset = gr.Dropdown(choices=preset_names, label="Loaded Preset")
        loaded_effects = gr.CheckboxGroup(choices=effect_options, label="Loaded Effects")

        action_btn.click(
            fn=save_or_resume_session,
            inputs=[audio_input, preset_dropdown, effect_checkbox, action_radio],
            outputs=[session_data, loaded_audio, loaded_preset, loaded_effects]
        )

    # --- VAD – Detect & Remove Silence ===
    with gr.Tab("✂️ Trim Silence Automatically"):
        gr.Interface(
            fn=detect_silence,
            inputs=[
                gr.File(label="Upload Track"),
                gr.Slider(minimum=-100, maximum=-10, value=-50, label="Silence Threshold (dB)"),
                gr.Number(label="Min Silence Length (ms)", value=1000)
            ],
            outputs=gr.File(label="Trimmed Output"),
            title="Auto-Detect & Remove Silence",
            description="Detect and trim silence at start/end or between words"
        )

    # --- Load/Save Project File (.aiproj) ===
    with gr.Tab("📁 Save/Load Project"):
        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=effect_options, label="Applied Effects")
            ],
            outputs=gr.File(label="Project File (.aiproj)"),
            title="Save Everything Together",
            description="Save your session, effects, and settings in one file to reuse later."
        )

        gr.Interface(
            fn=load_project,
            inputs=gr.File(label="Upload .aiproj File"),
            outputs=[
                gr.Dropdown(choices=preset_names, label="Loaded Preset"),
                gr.CheckboxGroup(choices=effect_options, label="Loaded Effects")
            ],
            title="Resume Last Project",
            description="Load your saved session"
        )

    # --- Mix Two Tracks ===
    with gr.Tab(" remix mode"),
        gr.Interface(
            fn=mix_tracks,
            inputs=[
                gr.File(label="Main Track"),
                gr.File(label="Background Track"),
                gr.Slider(minimum=-10, maximum=10, value=0, label="Volume Offset (dB)")
            ],
            outputs=gr.File(label="Mixed Output"),
            title="Overlay Two Tracks",
            description="Mix, blend, or subtract two audio files."
        )

demo.launch()