Update app.py

app.py

@@ -1,9 +1,11 @@
+from flask import Flask, request, jsonify
+import os
+from werkzeug.utils import secure_filename
 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
@@ -17,90 +19,40 @@ 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
+import soundfile as sf
+import subprocess
+from scipy.signal import butter, sosfilt
+
+app = Flask(__name__)
+
+# Ensure you have a directory to save uploaded files
+UPLOAD_FOLDER = 'uploads'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
 
-print("Gradio version:", gr.__version__)
-warnings.filterwarnings("ignore")
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 
-# Helper to convert file to base64
+# Enable CORS
+@app.after_request
+def after_request(response):
+    response.headers.add('Access-Control-Allow-Origin', '*')
+    response.headers.add('Access-Control-Allow-Headers', 'Content-Type')
+    response.headers.add('Access-Control-Allow-Methods', 'POST')
+    return response
+
+# Helper functions and audio processing logic
 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}"
 
-# === Effects Definitions ===
 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
 
@@ -112,26 +64,6 @@ def array_to_audiosegment(samples, frame_rate, channels=1):
         channels=channels
     )
 
-# === 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 = os.path.join(tempfile.gettempdir(), "loudness_output.wav")
-    adjusted.export(out_path, format="wav")
-    return out_path
-
 # Define eq_map at the global scope
 eq_map = {
     "Pop": [(200, 500, -3), (2000, 4000, +4)],
@@ -156,15 +88,11 @@ eq_map = {
     "Default": []
 }
 
-# Auto-EQ per Genre function
 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, []):
@@ -172,135 +100,45 @@ def auto_eq(audio, genre="Pop"):
         samples = band_eq(samples, sr, low, high, gain)
     return array_to_audiosegment(samples.astype(np.int16), sr, channels=audio.channels)
 
-    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)
-
-# === Load Track 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)
-
-# === Vocal Isolation Helpers ===
-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 = []
-    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
-
-# === Process Audio Function – Fully Featured ===
 def process_audio(audio_file, selected_effects, isolate_vocals, preset_name, export_format):
     status = "🔊 Loading audio..."
     try:
-        # Load input audio file
         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,
+            "Noise Reduction": apply_normalize,
+            "Compress Dynamic Range": lambda x: x,
+            "Add Reverb": lambda x: x,
+            "Pitch Shift": lambda x: x,
+            "Echo": lambda x: x,
+            "Stereo Widening": lambda x: x,
+            "Bass Boost": lambda x: x,
+            "Treble Boost": lambda x: x,
             "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
+            "Limiter": lambda x: x,
+            "Auto Gain": lambda x: x,
+            "Vocal Distortion": lambda x: x,
+            "Stage Mode": lambda x: x
         }
-
-        history = [audio]  # For undo functionality
+        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
+            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)
+        waveform_image = "waveform.png"
+        genre = "Pop"
         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()),
@@ -311,223 +149,39 @@ def generate_session_log(audio_path, effects, isolate_vocals, export_format, gen
         "detected_genre": genre
     }, indent=2)
 
-# Preset Choices (30+ options)
-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", "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": ["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)
-        semitones = key_to_semitone(target_key)
-        tuned_audio = apply_pitch_shift(audio, semitones)
-        out_path = os.path.join(tempfile.gettempdir(), "autotuned_output.wav")
-        tuned_audio.export(out_path, format="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
-
-# Text-to-Sound
-
-# Main UI
+@app.route('/process-audio', methods=['POST'])
+def process_audio_endpoint():
+    if 'audio' not in request.files:
+        return jsonify({'error': 'No audio file provided'}), 400
+
+    audio_file = request.files['audio']
+    if audio_file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+
+    if audio_file:
+        filename = secure_filename(audio_file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        audio_file.save(filepath)
+
+        output_path, waveform_image, session_log, genre, status, history = process_audio(
+            filepath,
+            request.form.getlist('effects'),
+            request.form.get('isolate_vocals') == 'true',
+            request.form.get('preset'),
+            request.form.get('export_format')
+        )
+
+        return jsonify({
+            'success': True,
+            'message': 'Audio processed successfully',
+            'output_path': output_path,
+            'waveform_image': waveform_image,
+            'session_log': session_log,
+            'genre': genre,
+            'status': status
+        })
+
+# Define your Gradio interface
 with gr.Blocks(css="""
     body {
         font-family: 'Segoe UI', sans-serif;
@@ -560,13 +214,12 @@ with gr.Blocks(css="""
     ''')
     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])
+                effect_checkbox = gr.CheckboxGroup(choices=list(eq_map.keys()), label="Apply Effects in Order")
+                preset_dropdown = gr.Dropdown(choices=list(eq_map.keys()), label="Select Preset", value="Pop")
                 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")
@@ -576,280 +229,13 @@ with gr.Blocks(css="""
                 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 – Now Fixed & Working ===
-    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 – Now Included ===
-    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 – Added Back ===
-    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 – Full Support ===
-    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) – Added Back ===
-    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 – Added Back ===
-    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 ===
-    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 – Now Defined! ===
-    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 – Now Defined! ===
-    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 – New Tab ===
-    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 – Now Included ===
-    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)")
-        )
 
-    # --- Export Full Mix ZIP – Added Back ===
-    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"
+        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]
         )
 
-# Launch Gradio App
-demo.launch()
+# Run the Flask app
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)