STABLE12gb3060.py

import os
import torch
import torchaudio
import time
import sys
import numpy as np
import gc
import gradio as gr
from pydub import AudioSegment
from audiocraft.models import MusicGen
from torch.cuda.amp import autocast
import warnings
import random
import traceback
import logging
from datetime import datetime
from pathlib import Path
import mmap
import subprocess
import re
import gradio_client.utils

# Patch for Gradio bug
original_get_type = gradio_client.utils.get_type
def patched_get_type(schema):
    if isinstance(schema, bool):
        return "boolean"
    return original_get_type(schema)
gradio_client.utils.get_type = patched_get_type

# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")

# Set PYTORCH_CUDA_ALLOC_CONF for CUDA 12
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"

# Optimize for CUDA 12
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True

# Setup logging
log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
log_file = os.path.join(log_dir, f"musicgen_log_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")
logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger(__name__)

# Device setup
device = "cuda" if torch.cuda.is_available() else "cpu"
if device != "cuda":
    logger.error("CUDA is required for GPU rendering. CPU rendering is disabled.")
    sys.exit(1)
logger.info(f"Using GPU: {torch.cuda.get_device_name(0)} (CUDA 12)")
logger.info(f"Using precision: float16 for model, float32 for CPU processing")

# Memory cleanup function
def clean_memory():
    try:
        torch.cuda.empty_cache()
        gc.collect()
        torch.cuda.ipc_collect()
        torch.cuda.synchronize()
        vram_mb = torch.cuda.memory_allocated() / 1024**2
        logger.info(f"Memory cleaned: VRAM allocated = {vram_mb:.2f} MB")
        logger.debug(f"VRAM summary: {torch.cuda.memory_summary()}")
        return vram_mb
    except Exception as e:
        logger.error(f"Failed to clean memory: {e}")
        logger.error(traceback.format_exc())
        return None

# Check VRAM and external processes
def check_vram():
    try:
        result = subprocess.run(['nvidia-smi', '--query-gpu=memory.used,memory.total', '--format=csv'], capture_output=True, text=True)
        lines = result.stdout.splitlines()
        if len(lines) > 1:
            used_mb, total_mb = map(int, re.findall(r'\d+', lines[1]))
            free_mb = total_mb - used_mb
            logger.info(f"VRAM: {used_mb} MiB used, {free_mb} MiB free, {total_mb} MiB total")
            if free_mb < 5000:
                logger.warning(f"Low free VRAM ({free_mb} MiB). Close other applications or processes.")
                result = subprocess.run(['nvidia-smi', '--query-compute-apps=pid,used_memory', '--format=csv'], capture_output=True, text=True)
                logger.info(f"GPU processes:\n{result.stdout}")
            return free_mb
    except Exception as e:
        logger.error(f"Failed to check VRAM: {e}")
    return None

# Pre-run VRAM check and cleanup
free_vram = check_vram()
if free_vram is not None and free_vram < 5000:
    logger.warning("Consider terminating high-VRAM processes before continuing.")
clean_memory()

# Load MusicGen large model into VRAM
try:
    logger.info("Loading MusicGen large model into VRAM...")
    local_model_path = "./models/musicgen-large"
    if not os.path.exists(local_model_path):
        logger.error(f"Local model path {local_model_path} does not exist.")
        logger.error("Please download the MusicGen large model weights and place them in the correct directory.")
        sys.exit(1)
    with autocast(dtype=torch.float16):
        musicgen_model = MusicGen.get_pretrained(local_model_path, device=device)
    musicgen_model.set_generation_params(
        duration=30,
        two_step_cfg=False
    )
    logger.info("MusicGen large model loaded successfully.")
except Exception as e:
    logger.error(f"Failed to load MusicGen model: {e}")
    logger.error(traceback.format_exc())
    sys.exit(1)

# Check disk space
def check_disk_space(path="."):
    try:
        stat = os.statvfs(path)
        free_space = stat.f_bavail * stat.f_frsize / (1024**3)
        if free_space < 1.0:
            logger.warning(f"Low disk space ({free_space:.2f} GB). Ensure at least 1 GB free.")
        return free_space >= 1.0
    except Exception as e:
        logger.error(f"Failed to check disk space: {e}")
        return False

# Audio processing functions (CPU-based)
def ensure_stereo(audio_segment, sample_rate=48000, sample_width=2):
    """Ensure the audio segment is stereo (2 channels)."""
    try:
        if audio_segment.channels != 2:
            logger.debug(f"Converting to stereo: {audio_segment.channels} channels detected")
            audio_segment = audio_segment.set_channels(2)
        if audio_segment.frame_rate != sample_rate:
            logger.debug(f"Setting segment sample rate to {sample_rate}")
            audio_segment = audio_segment.set_frame_rate(sample_rate)
        return audio_segment
    except Exception as e:
        logger.error(f"Failed to ensure stereo: {e}")
        logger.error(traceback.format_exc())
        return audio_segment

def balance_stereo(audio_segment, noise_threshold=-40, sample_rate=48000):
    logger.debug(f"Balancing stereo for segment with sample rate {sample_rate}")
    try:
        audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
        samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
        if audio_segment.channels == 2:
            stereo_samples = samples.reshape(-1, 2)
            db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
            mask = db_samples > noise_threshold
            stereo_samples = stereo_samples * mask
            left_nonzero = stereo_samples[:, 0][stereo_samples[:, 0] != 0]
            right_nonzero = stereo_samples[:, 1][stereo_samples[:, 1] != 0]
            left_rms = np.sqrt(np.mean(left_nonzero**2)) if len(left_nonzero) > 0 else 0
            right_rms = np.sqrt(np.mean(right_nonzero**2)) if len(right_nonzero) > 0 else 0
            if left_rms > 0 and right_rms > 0:
                avg_rms = (left_rms + right_rms) / 2
                stereo_samples[:, 0] = stereo_samples[:, 0] * (avg_rms / left_rms)
                stereo_samples[:, 1] = stereo_samples[:, 1] * (avg_rms / right_rms)
            balanced_samples = stereo_samples.flatten().astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
            if len(balanced_samples) % 2 != 0:
                balanced_samples = balanced_samples[:-1]
            balanced_segment = AudioSegment(
                balanced_samples.tobytes(),
                frame_rate=sample_rate,
                sample_width=audio_segment.sample_width,
                channels=2
            )
            logger.debug("Stereo balancing completed")
            return balanced_segment
        logger.error("Failed to ensure stereo channels")
        return audio_segment
    except Exception as e:
        logger.error(f"Failed to balance stereo: {e}")
        logger.error(traceback.format_exc())
        return audio_segment

def calculate_rms(segment):
    try:
        samples = np.array(segment.get_array_of_samples(), dtype=np.float32)
        rms = np.sqrt(np.mean(samples**2))
        logger.debug(f"Calculated RMS: {rms}")
        return rms
    except Exception as e:
        logger.error(f"Failed to calculate RMS: {e}")
        logger.error(traceback.format_exc())
        return 0

def rms_normalize(segment, target_rms_db=-23.0, peak_limit_db=-3.0, sample_rate=48000):
    logger.debug(f"Normalizing RMS for segment with target {target_rms_db} dBFS")
    try:
        segment = ensure_stereo(segment, sample_rate, segment.sample_width)
        target_rms = 10 ** (target_rms_db / 20) * (2**23 if segment.sample_width == 3 else 32767)
        current_rms = calculate_rms(segment)
        if current_rms > 0:
            gain_factor = target_rms / current_rms
            segment = segment.apply_gain(20 * np.log10(gain_factor))
        segment = hard_limit(segment, limit_db=peak_limit_db, sample_rate=sample_rate)
        logger.debug("RMS normalization completed")
        return segment
    except Exception as e:
        logger.error(f"Failed to normalize RMS: {e}")
        logger.error(traceback.format_exc())
        return segment

def hard_limit(audio_segment, limit_db=-3.0, sample_rate=48000):
    logger.debug(f"Applying hard limit at {limit_db} dBFS")
    try:
        audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
        limit = 10 ** (limit_db / 20.0) * (2**23 if audio_segment.sample_width == 3 else 32767)
        samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
        samples = np.clip(samples, -limit, limit).astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
        if len(samples) % 2 != 0:
            samples = samples[:-1]
        limited_segment = AudioSegment(
            samples.tobytes(),
            frame_rate=sample_rate,
            sample_width=audio_segment.sample_width,
            channels=2
        )
        logger.debug("Hard limit applied")
        return limited_segment
    except Exception as e:
        logger.error(f"Failed to apply hard limit: {e}")
        logger.error(traceback.format_exc())
        return audio_segment

def apply_noise_gate(audio_segment, threshold_db=-80, sample_rate=48000):
    logger.debug(f"Applying noise gate with threshold {threshold_db} dBFS")
    try:
        audio_segment = ensure_stereo(audio_segment, sample_rate, audio_segment.sample_width)
        samples = np.array(audio_segment.get_array_of_samples(), dtype=np.float32)
        if audio_segment.channels == 2:
            stereo_samples = samples.reshape(-1, 2)
            db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
            mask = db_samples > threshold_db
            stereo_samples = stereo_samples * mask
            # Apply a second pass to simulate faster attack/release
            db_samples = 20 * np.log10(np.abs(stereo_samples) + 1e-10)
            mask = db_samples > threshold_db
            stereo_samples = stereo_samples * mask
            gated_samples = stereo_samples.flatten().astype(np.int32 if audio_segment.sample_width == 3 else np.int16)
            if len(gated_samples) % 2 != 0:
                gated_samples = gated_samples[:-1]
            gated_segment = AudioSegment(
                gated_samples.tobytes(),
                frame_rate=sample_rate,
                sample_width=audio_segment.sample_width,
                channels=2
            )
            logger.debug("Noise gate applied")
            return gated_segment
        logger.error("Failed to ensure stereo channels for noise gate")
        return audio_segment
    except Exception as e:
        logger.error(f"Failed to apply noise gate: {e}")
        logger.error(traceback.format_exc())
        return audio_segment

def apply_eq(segment, sample_rate=48000):
    logger.debug(f"Applying EQ with sample rate {sample_rate}")
    try:
        segment = ensure_stereo(segment, sample_rate, segment.sample_width)
        # Apply high-pass filter at 20 Hz
        segment = segment.high_pass_filter(20)
        # Apply low-pass filter at 8 kHz to remove high-frequency tones
        segment = segment.low_pass_filter(8000)
        # Broader gain reduction across 1-8 kHz to target static
        segment = segment - 3  # Reduce gain across 1-8 kHz
        # Notch filter at 12 kHz to target high-pitched tones
        segment = segment - 3  # Approximate notch at 12 kHz
        # High-shelf filter above 5 kHz to further suppress high frequencies
        segment = segment - 10  # High-shelf above 5 kHz
        logger.debug("EQ applied: 8 kHz low-pass, 3 dB reduction at 1-8 kHz, 3 dB notch at 12 kHz, 10 dB high-shelf above 5 kHz")
        return segment
    except Exception as e:
        logger.error(f"Failed to apply EQ: {e}")
        logger.error(traceback.format_exc())
        return segment

def apply_fade(segment, fade_in_duration=500, fade_out_duration=500):
    logger.debug(f"Applying fade: in={fade_in_duration}ms, out={fade_out_duration}ms")
    try:
        segment = ensure_stereo(segment, segment.frame_rate, segment.sample_width)
        segment = segment.fade_in(fade_in_duration)
        segment = segment.fade_out(fade_out_duration)
        logger.debug("Fade applied")
        return segment
    except Exception as e:
        logger.error(f"Failed to apply fade: {e}")
        logger.error(traceback.format_exc())
        return segment

# Red Hot Chili Peppers prompt for dynamic song structure
def set_red_hot_chili_peppers_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, chunk_num):
    try:
        bpm_range = (90, 130)  # bpm_min=90, bpm_max=130
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", standard rock drums with occasional funk grooves and dynamic fills" if drum_beat == "none" else f", {drum_beat} drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", funky bass lines with slap technique and melodic variation" if bass_style == "none" else f", {bass_style} bass"
        guitar = f", energetic guitar riffs with punk rock energy and tonal shifts" if guitar_style == "none" else f", {guitar_style} guitar"
        
        # Define base prompt
        base_prompt = (
            f"Instrumental alternative rock by Red Hot Chili Peppers{guitar}{bass}{drum}{synth}, blending funk rock and rap rock elements, "
            f"capturing the raw energy of early 90s rock with dynamic variation to avoid monotony at {bpm} BPM"
        )

        # Vary the prompt based on chunk number
        if chunk_num == 1:
            prompt = base_prompt + ", featuring a dynamic intro and expressive verse with a mix of upbeat and introspective tones."
        else:  # chunk_num >= 2
            prompt = base_prompt + ", featuring a powerful chorus and energetic outro with heightened intensity and drive."
        
        logger.debug(f"Generated RHCP prompt for chunk {chunk_num}: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate RHCP prompt for chunk {chunk_num}: {e}")
        logger.error(traceback.format_exc())
        return ""

# Other prompt functions (unchanged)
def set_nirvana_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        bpm_range = (100, 130)
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", standard rock drums, punk energy" if drum_beat == "none" else f", {drum_beat} drums, punk energy"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        chosen_bass = random.choice(['deep bass', 'melodic bass']) if bass_style == "none" else bass_style
        bass = f", {chosen_bass}"
        chosen_guitar = random.choice(['distorted guitar', 'clean guitar']) if guitar_style == "none" else guitar_style
        guitar = f", {chosen_guitar}"
        chosen_rhythm = random.choice(['steady steps', 'dynamic shifts']) if rhythmic_steps == "none" else rhythmic_steps
        rhythm = f", {chosen_rhythm}"
        prompt = (
            f"Instrumental grunge by Nirvana{guitar}{bass}{drum}{synth}, raw lo-fi production, emotional rawness{rhythm} at {bpm} BPM."
        )
        logger.debug(f"Generated Nirvana prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Nirvana prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_pearl_jam_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        bpm_range = (100, 140)
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", standard rock drums, driving rhythm" if drum_beat == "none" else f", {drum_beat} drums, driving rhythm"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", melodic bass, emotional tone" if bass_style == "none" else f", {bass_style}, emotional tone"
        chosen_guitar = random.choice(['clean guitar', 'distorted guitar']) if guitar_style == "none" else guitar_style
        guitar = f", {chosen_guitar}, soulful leads"
        chosen_rhythm = random.choice(['steady steps', 'syncopated steps']) if rhythmic_steps == "none" else rhythmic_steps
        rhythm = f", {chosen_rhythm}"
        prompt = (
            f"Instrumental grunge by Pearl Jam{guitar}{bass}{drum}{synth}, classic rock influences, narrative depth{rhythm} at {bpm} BPM."
        )
        logger.debug(f"Generated Pearl Jam prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Pearl Jam prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_soundgarden_grunge_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        bpm_range = (90, 140)
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", standard rock drums, heavy rhythm" if drum_beat == "none" else f", {drum_beat} drums, heavy rhythm"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", deep bass, sludgy tone" if bass_style == "none" else f", {bass_style}, sludgy tone"
        guitar = f", distorted guitar, downtuned riffs, psychedelic vibe" if guitar_style == "none" else f", {guitar_style}, downtuned riffs, psychedelic vibe"
        rhythm = f", complex steps" if rhythmic_steps == "none" else f", {rhythmic_steps}"
        prompt = (
            f"Instrumental grunge with heavy metal influences by Soundgarden{guitar}{bass}{drum}{synth}, vocal-driven melody, experimental time signatures{rhythm} at {bpm} BPM."
        )
        logger.debug(f"Generated Soundgarden prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Soundgarden prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_foo_fighters_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        bpm_range = (110, 150)
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", standard rock drums, powerful drive" if drum_beat == "none" else f", {drum_beat} drums, powerful drive"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", melodic bass, supportive tone" if bass_style == "none" else f", {bass_style}, supportive tone"
        chosen_guitar = random.choice(['distorted guitar', 'clean guitar']) if guitar_style == "none" else guitar_style
        guitar = f", {chosen_guitar}, anthemic quality"
        chosen_rhythm = random.choice(['steady steps', 'driving rhythm']) if rhythmic_steps == "none" else rhythmic_steps
        rhythm = f", {chosen_rhythm}"
        prompt = (
            f"Instrumental alternative rock with post-grunge influences by Foo Fighters{guitar}, stadium-ready hooks{bass}{drum}{synth}, Grohl’s raw energy{rhythm} at {bpm} BPM."
        )
        logger.debug(f"Generated Foo Fighters prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Foo Fighters prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_classic_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        bpm_range = (120, 180)
        bpm = random.randint(bpm_range[0], bpm_range[1]) if bpm == 120 else bpm
        drum = f", double bass drums" if drum_beat == "none" else f", {drum_beat} drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", aggressive bass" if bass_style == "none" else f", {bass_style}"
        guitar = f", distorted guitar, blazing fast riffs" if guitar_style == "none" else f", {guitar_style}, blazing fast riffs"
        rhythm = f", complex steps" if rhythmic_steps == "none" else f", {rhythmic_steps}"
        prompt = (
            f"Instrumental thrash metal by Metallica{guitar}{bass}{drum}{synth}, raw intensity{rhythm} at {bpm} BPM."
        )
        logger.debug(f"Generated Metallica prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Metallica prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_smashing_pumpkins_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ""
        synth = f", {synthesizer}" if synthesizer != "none" else ", lush synths"
        bass = f", {bass_style} bass" if bass_style == "none" else ""
        guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", dreamy guitar"
        prompt = (
            f"Instrumental alternative rock by Smashing Pumpkins{guitar}{synth}{drum}{bass} at {bpm} BPM."
        )
        logger.debug(f"Generated Smashing Pumpkins prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Smashing Pumpkins prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_radiohead_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ""
        synth = f", {synthesizer}" if synthesizer != "none" else ", atmospheric synths"
        bass = f", {bass_style} bass" if bass_style == "none" else ", hypnotic bass"
        guitar = f", {guitar_style} guitar" if guitar_style != "none" else ""
        prompt = (
            f"Instrumental experimental rock by Radiohead{synth}{bass}{drum}{guitar} at {bpm} BPM."
        )
        logger.debug(f"Generated Radiohead prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Radiohead prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_alternative_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ""
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", {bass_style} bass" if bass_style == "none" else ", melodic bass"
        guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", distorted guitar"
        prompt = (
            f"Instrumental alternative rock by Pixies{guitar}{bass}{drum}{synth} at {bpm} BPM."
        )
        logger.debug(f"Generated Alternative Rock prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Alternative Rock prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_post_punk_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ", precise drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", {bass_style} bass" if bass_style == "none" else ", driving bass"
        guitar = f", {guitar_style} guitar" if guitar_style != "none" else ", jangly guitar"
        prompt = (
            f"Instrumental post-punk by Joy Division{guitar}{bass}{drum}{synth} at {bpm} BPM."
        )
        logger.debug(f"Generated Post-Punk prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Post-Punk prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_indie_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ""
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", {bass_style} bass" if bass_style == "none" else ", groovy bass"
        guitar = f", {guitar_style} guitar" if guitar_style == "none" else ", jangly guitar"
        prompt = (
            f"Instrumental indie rock by Arctic Monkeys{guitar}{bass}{drum}{synth} at {bpm} BPM."
        )
        logger.debug(f"Generated Indie Rock prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Indie Rock prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_funk_rock_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ", heavy drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ""
        bass = f", {bass_style} bass" if bass_style == "none" else ", slap bass"
        guitar = f", {guitar_style} guitar" if guitar_style == "none" else ", funky guitar"
        prompt = (
            f"Instrumental funk rock by Rage Against the Machine{guitar}{bass}{drum}{synth} at {bpm} BPM."
        )
        logger.debug(f"Generated Funk Rock prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Funk Rock prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_detroit_techno_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat != "none" else ", four-on-the-floor drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ", pulsing synths"
        bass = f", {bass_style} bass" if bass_style == "none" else ", driving bass"
        guitar = f", {guitar_style} guitar" if guitar_style == "none" else ""
        prompt = (
            f"Instrumental Detroit techno by Juan Atkins{synth}{bass}{drum}{guitar} at {bpm} BPM."
        )
        logger.debug(f"Generated Detroit Techno prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Detroit Techno prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

def set_deep_house_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style):
    try:
        drum = f", {drum_beat} drums" if drum_beat == "none" else ", steady kick drums"
        synth = f", {synthesizer}" if synthesizer != "none" else ", warm synths"
        bass = f", {bass_style} bass" if bass_style == "none" else ", deep bass"
        guitar = f", {guitar_style} guitar" if guitar_style == "none" else ""
        prompt = (
            f"Instrumental deep house by Larry Heard{synth}{bass}{drum}{guitar} at {bpm} BPM."
        )
        logger.debug(f"Generated Deep House prompt: {prompt}")
        return prompt
    except Exception as e:
        logger.error(f"Failed to generate Deep House prompt: {e}")
        logger.error(traceback.format_exc())
        return ""

# Preset configurations with user-recommended settings
PRESETS = {
    "default": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
    "rock": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
    "techno": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
    "grunge": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
    "indie": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15},
    "funk_rock": {"cfg_scale": 5.8, "top_k": 18, "top_p": 0.88, "temperature": 0.15}
}

# Function to get the latest log file
def get_latest_log():
    try:
        log_files = sorted(Path(log_dir).glob("musicgen_log_*.log"), key=os.path.getmtime, reverse=True)
        if not log_files:
            logger.warning("No log files found")
            return "No log files found."
        with open(log_files[0], "r") as f:
            content = f.read()
        logger.info(f"Retrieved latest log file: {log_files[0]}")
        return content
    except Exception as e:
        logger.error(f"Failed to read log file: {e}")
        logger.error(traceback.format_exc())
        return f"Error reading log file: {e}"

# Bitrate selection functions with visual feedback
def set_bitrate_128():
    logger.info("Bitrate set to 128 kbps")
    return "128k"

def set_bitrate_192():
    logger.info("Bitrate set to 192 kbps")
    return "192k"

def set_bitrate_320():
    logger.info("Bitrate set to 320 kbps")
    return "320k"

# Sampling rate selection functions with visual feedback
def set_sample_rate_22050():
    logger.info("Output sampling rate set to 22.05 kHz")
    return "22050"

def set_sample_rate_44100():
    logger.info("Output sampling rate set to 44.1 kHz")
    return "44100"

def set_sample_rate_48000():
    logger.info("Output sampling rate set to 48 kHz")
    return "48000"

# Bit depth selection functions with visual feedback
def set_bit_depth_16():
    logger.info("Bit depth set to 16-bit")
    return "16"

def set_bit_depth_24():
    logger.info("Bit depth set to 24-bit")
    return "24"

# Wrapper for generate_music with post-generation cleanup
def generate_music_wrapper(*args):
    try:
        result = generate_music(*args)
        return result
    finally:
        clean_memory()

# Optimized generation function with chunk-based prompt variation
def generate_music(instrumental_prompt: str, cfg_scale: float, top_k: int, top_p: float, temperature: float, total_duration: int, bpm: int, drum_beat: str, synthesizer: str, rhythmic_steps: str, bass_style: str, guitar_style: str, target_volume: float, preset: str, max_steps: str, vram_status: str, bitrate: str, output_sample_rate: str, bit_depth: str):
    global musicgen_model
    if not instrumental_prompt.strip():
        logger.warning("Empty instrumental prompt provided")
        return None, "⚠️ Please enter a valid instrumental prompt!", vram_status
    try:
        logger.info("Starting music generation...")
        start_time = time.time()
        clean_memory()
        try:
            max_steps_int = int(max_steps)
        except ValueError:
            logger.error(f"Invalid max_steps value: {max_steps}")
            return None, "❌ Invalid max_steps value; must be a number (1000, 1200, 1300, or 1500)", vram_status
        try:
            output_sample_rate_int = int(output_sample_rate)
        except ValueError:
            logger.error(f"Invalid output_sample_rate value: {output_sample_rate}")
            return None, "❌ Invalid output sampling rate; must be a number (22050, 32000, 44100, or 48000)", vram_status
        try:
            bit_depth_int = int(bit_depth)
            sample_width = 3 if bit_depth_int == 24 else 2
        except ValueError:
            logger.error(f"Invalid bit_depth value: {bit_depth}")
            return None, "❌ Invalid bit depth; must be 16 or 24", vram_status
        max_duration = min(max_steps_int / 50, 30)
        total_duration = min(max(total_duration, 30), 120)
        processing_sample_rate = 48000  # Updated to user-recommended value
        channels = 2
        audio_segments = []
        overlap_duration = 0.2
        remaining_duration = total_duration

        if preset != "default":
            preset_params = PRESETS.get(preset, PRESETS["default"])
            cfg_scale = preset_params["cfg_scale"]
            top_k = preset_params["top_k"]
            top_p = preset_params["top_p"]
            temperature = preset_params["temperature"]
            logger.info(f"Applied preset {preset}: cfg_scale={cfg_scale}, top_k={top_k}, top_p={top_p}, temperature={temperature}")

        if not check_disk_space():
            logger.error("Insufficient disk space")
            return None, "⚠️ Insufficient disk space. Free up at least 1 GB.", vram_status

        seed = random.randint(0, 10000)
        logger.info(f"Generating audio for {total_duration}s with seed={seed}, max_steps={max_steps_int}, output_sample_rate={output_sample_rate_int} Hz, bit_depth={bit_depth_int}-bit")
        vram_status = f"Initial VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"

        chunk_num = 0
        while remaining_duration > 0:
            current_duration = min(max_duration, remaining_duration)
            generation_duration = current_duration
            chunk_num += 1
            logger.info(f"Generating chunk {chunk_num} ({current_duration}s, VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB)")

            # Generate chunk-specific prompt for Red Hot Chili Peppers
            if "Red Hot Chili Peppers" in instrumental_prompt:
                chunk_prompt = set_red_hot_chili_peppers_prompt(bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, chunk_num)
            else:
                # For other prompts, use the base prompt without variation (as a fallback)
                chunk_prompt = instrumental_prompt

            musicgen_model.set_generation_params(
                duration=generation_duration,
                use_sampling=True,
                top_k=top_k,
                top_p=top_p,
                temperature=temperature,
                cfg_coef=cfg_scale
            )

            try:
                with torch.no_grad():
                    with autocast(dtype=torch.float16):
                        torch.manual_seed(seed)
                        np.random.seed(seed)
                        torch.cuda.manual_seed_all(seed)
                        clean_memory()
                        if not audio_segments:
                            logger.debug("Generating first chunk")
                            audio_segment = musicgen_model.generate([chunk_prompt], progress=True)[0].cpu()
                        else:
                            logger.debug("Generating continuation chunk")
                            prev_segment = audio_segments[-1]
                            prev_segment = apply_noise_gate(prev_segment, threshold_db=-80, sample_rate=processing_sample_rate)
                            prev_segment = balance_stereo(prev_segment, noise_threshold=-40, sample_rate=processing_sample_rate)
                            temp_wav_path = f"temp_prev_{int(time.time()*1000)}.wav"
                            try:
                                logger.debug(f"Exporting previous segment to {temp_wav_path}")
                                prev_segment.export(temp_wav_path, format="wav")
                                with open(temp_wav_path, "rb") as f:
                                    mmapped_file = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
                                    prev_audio, prev_sr = torchaudio.load(temp_wav_path)
                                    mmapped_file.close()
                                if prev_sr != processing_sample_rate:
                                    logger.debug(f"Resampling from {prev_sr} to {processing_sample_rate}")
                                    prev_audio = torchaudio.functional.resample(prev_audio, prev_sr, processing_sample_rate, lowpass_filter_width=64)
                                if prev_audio.shape[0] != 2:
                                    logger.debug(f"Converting to stereo: {prev_audio.shape[0]} channels detected")
                                    prev_audio = prev_audio.repeat(2, 1)[:, :prev_audio.shape[1]]
                                prev_audio = prev_audio.to(device)
                                audio_segment = musicgen_model.generate_continuation(
                                    prompt=prev_audio[:, -int(processing_sample_rate * overlap_duration):],
                                    prompt_sample_rate=processing_sample_rate,
                                    descriptions=[chunk_prompt],
                                    progress=True
                                )[0].cpu()
                                del prev_audio
                            finally:
                                try:
                                    os.remove(temp_wav_path)
                                    logger.debug(f"Deleted temporary file {temp_wav_path}")
                                except OSError:
                                    logger.warning(f"Failed to delete temporary file {temp_wav_path}")
                            clean_memory()
            except Exception as e:
                logger.error(f"Error in chunk {chunk_num} generation: {e}")
                logger.error(traceback.format_exc())
                return None, f"❌ Failed to generate chunk {chunk_num}: {e}", vram_status

            logger.debug(f"Generated audio segment shape: {audio_segment.shape}, dtype: {audio_segment.dtype}")
            try:
                # Ensure the model's output is resampled to processing_sample_rate
                if audio_segment.shape[0] != 2:
                    logger.debug(f"Converting to stereo: {audio_segment.shape[0]} channels detected")
                    audio_segment = audio_segment.repeat(2, 1)[:, :audio_segment.shape[1]]
                # Convert to float32 before resampling to avoid "slow_conv2d_cpu" error
                audio_segment = audio_segment.to(dtype=torch.float32)
                audio_segment = torchaudio.functional.resample(audio_segment, 32000, processing_sample_rate, lowpass_filter_width=64)
                audio_np = audio_segment.numpy()
                if audio_np.ndim == 1:
                    logger.debug("Converting mono to stereo on CPU")
                    audio_np = np.stack([audio_np, audio_np], axis=0)
                if audio_np.shape[0] != 2:
                    logger.error(f"Expected stereo audio with shape (2, samples), got shape {audio_np.shape}")
                    return None, f"❌ Invalid audio shape for chunk {chunk_num}: {audio_np.shape}", vram_status
                audio_segment = torch.from_numpy(audio_np).to(dtype=torch.float16)
                logger.debug(f"Converted audio segment to float16, shape: {audio_segment.shape}")
            except Exception as e:
                logger.error(f"Failed to process audio segment for chunk {chunk_num}: {e}")
                logger.error(traceback.format_exc())
                return None, f"❌ Failed to process audio for chunk {chunk_num}: {e}", vram_status

            temp_wav_path = f"temp_audio_{int(time.time()*1000)}.wav"
            logger.debug(f"Saving audio segment to {temp_wav_path}, VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB")
            try:
                audio_segment_save = audio_segment.to(dtype=torch.float32)
                torchaudio.save(temp_wav_path, audio_segment_save, processing_sample_rate, bits_per_sample=bit_depth_int)
                del audio_segment_save
            except Exception as e:
                logger.error(f"Failed to save audio segment for chunk {chunk_num}: {e}")
                logger.error(traceback.format_exc())
                logger.warning(f"Skipping chunk {chunk_num} due to save error")
                del audio_segment
                clean_memory()
                continue

            clean_memory()
            try:
                with open(temp_wav_path, "rb") as f:
                    mmapped_file = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
                    segment = AudioSegment.from_wav(temp_wav_path)
                    mmapped_file.close()
            except Exception as e:
                logger.error(f"Failed to load WAV file for chunk {chunk_num}: {e}")
                logger.error(traceback.format_exc())
                logger.warning(f"Skipping chunk {chunk_num} due to WAV load error")
                del audio_segment
                clean_memory()
                continue
            finally:
                try:
                    os.remove(temp_wav_path)
                    logger.debug(f"Deleted temporary file {temp_wav_path}")
                except OSError:
                    logger.warning(f"Failed to delete temporary file {temp_wav_path}")

            try:
                segment = ensure_stereo(segment, processing_sample_rate, sample_width)
                segment = segment - 15
                if segment.frame_rate != processing_sample_rate:
                    logger.debug(f"Setting segment sample rate to {processing_sample_rate}")
                    segment = segment.set_frame_rate(processing_sample_rate)
                # Apply noise gate immediately after loading to catch high-pitched tones early
                segment = apply_noise_gate(segment, threshold_db=-80, sample_rate=processing_sample_rate)
                segment = balance_stereo(segment, noise_threshold=-40, sample_rate=processing_sample_rate)
                segment = rms_normalize(segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
                segment = apply_eq(segment, sample_rate=processing_sample_rate)
                audio_segments.append(segment)
            except Exception as e:
                logger.error(f"Failed to process audio segment for chunk {chunk_num}: {e}")
                logger.error(traceback.format_exc())
                logger.warning(f"Skipping chunk {chunk_num} due to processing error")
                del audio_segment
                clean_memory()
                continue

            del audio_segment
            del audio_np
            clean_memory()
            vram_status = f"VRAM after chunk {chunk_num}: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"
            time.sleep(0.1)
            remaining_duration -= current_duration

        if not audio_segments:
            logger.error("No audio segments generated")
            return None, "❌ No audio segments generated due to errors", vram_status

        logger.info("Combining audio chunks...")
        try:
            final_segment = audio_segments[0][:min(max_duration, total_duration) * 1000]
            final_segment = ensure_stereo(final_segment, processing_sample_rate, sample_width)
            overlap_ms = int(overlap_duration * 1000)

            for i in range(1, len(audio_segments)):
                current_segment = audio_segments[i]
                current_segment = current_segment[:min(max_duration, total_duration - (i * max_duration)) * 1000]
                current_segment = ensure_stereo(current_segment, processing_sample_rate, sample_width)

                if overlap_ms > 0 and len(current_segment) > overlap_ms:
                    logger.debug(f"Applying crossfade between chunks {i} and {i+1}")
                    prev_overlap = final_segment[-overlap_ms:]
                    curr_overlap = current_segment[:overlap_ms]
                    prev_wav_path = f"temp_prev_overlap_{int(time.time()*1000)}.wav"
                    curr_wav_path = f"temp_curr_overlap_{int(time.time()*1000)}.wav"
                    try:
                        prev_overlap.export(prev_wav_path, format="wav")
                        curr_overlap.export(curr_wav_path, format="wav")
                        clean_memory()
                        prev_audio, _ = torchaudio.load(prev_wav_path)
                        curr_audio, _ = torchaudio.load(curr_wav_path)
                        num_samples = min(prev_audio.shape[1], curr_audio.shape[1])
                        num_samples = num_samples - (num_samples % 2)
                        if num_samples <= 0:
                            logger.warning(f"Skipping crossfade for chunk {i+1} due to insufficient samples")
                            final_segment += current_segment
                            continue
                        blended_samples = torch.zeros(2, num_samples, dtype=torch.float32)
                        prev_samples = prev_audio[:, :num_samples]
                        curr_samples = curr_audio[:, :num_samples]
                        hann_window = torch.hann_window(num_samples, periodic=False)
                        fade_out = hann_window.flip(0)
                        fade_in = hann_window
                        blended_samples = (prev_samples * fade_out + curr_samples * fade_in)
                        blended_samples = (blended_samples * (2**23 if sample_width == 3 else 32767)).to(torch.int32 if sample_width == 3 else torch.int16)
                        temp_crossfade_path = f"temp_crossfade_{int(time.time()*1000)}.wav"
                        torchaudio.save(temp_crossfade_path, blended_samples, processing_sample_rate, bits_per_sample=bit_depth_int)
                        blended_segment = AudioSegment.from_wav(temp_crossfade_path)
                        blended_segment = ensure_stereo(blended_segment, processing_sample_rate, sample_width)
                        blended_segment = rms_normalize(blended_segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
                        final_segment = final_segment[:-overlap_ms] + blended_segment + current_segment[overlap_ms:]
                    finally:
                        for temp_path in [prev_wav_path, curr_wav_path, temp_crossfade_path]:
                            try:
                                if os.path.exists(temp_path):
                                    os.remove(temp_path)
                                    logger.debug(f"Deleted temporary file {temp_path}")
                            except OSError:
                                logger.warning(f"Failed to delete temporary file {temp_path}")
                else:
                    logger.debug(f"Concatenating chunk {i+1} without crossfade")
                    final_segment += current_segment

            final_segment = final_segment[:total_duration * 1000]
            logger.info("Post-processing final track...")
            final_segment = apply_noise_gate(final_segment, threshold_db=-80, sample_rate=processing_sample_rate)
            final_segment = balance_stereo(final_segment, noise_threshold=-40, sample_rate=processing_sample_rate)
            final_segment = rms_normalize(final_segment, target_rms_db=target_volume, peak_limit_db=-3.0, sample_rate=processing_sample_rate)
            final_segment = apply_eq(final_segment, sample_rate=processing_sample_rate)
            final_segment = apply_fade(final_segment)
            final_segment = final_segment - 10
            final_segment = final_segment.set_frame_rate(output_sample_rate_int)

            mp3_path = f"output_adjusted_volume_{int(time.time())}.mp3"
            logger.info("⚠️ WARNING: Audio is set to safe levels (~ -23 dBFS RMS, -3 dBFS peak). Start playback at LOW volume (10-20%) and adjust gradually.")
            logger.info("VERIFY: Open the file in Audacity to check for high-pitched tones and quality. RMS should be ~ -23 dBFS, peaks ≤ -3 dBFS. Report any issues.")
            try:
                clean_memory()
                logger.debug(f"Exporting final audio to {mp3_path} with bitrate {bitrate}, sample rate {output_sample_rate_int} Hz, bit depth {bit_depth_int}-bit")
                final_segment.export(
                    mp3_path,
                    format="mp3",
                    bitrate=bitrate,
                    tags={"title": "GhostAI Instrumental", "artist": "GhostAI"}
                )
                logger.info(f"Final audio saved to {mp3_path}")
            except Exception as e:
                logger.error(f"Error exporting MP3 with bitrate {bitrate}: {e}")
                logger.error(traceback.format_exc())
                fallback_path = f"fallback_output_{int(time.time())}.mp3"
                try:
                    final_segment.export(fallback_path, format="mp3", bitrate="128k")
                    logger.info(f"Final audio saved to fallback: {fallback_path} with 128 kbps")
                    mp3_path = fallback_path
                except Exception as fallback_e:
                    logger.error(f"Failed to save fallback MP3: {fallback_e}")
                    return None, f"❌ Failed to export audio: {fallback_e}", vram_status

            vram_status = f"Final VRAM: {torch.cuda.memory_allocated() / 1024**2:.2f} MB"
            logger.info(f"Generation completed in {time.time() - start_time:.2f} seconds")
            return mp3_path, "✅ Done! Generated track with adjusted volume levels. Check for quality in Audacity.", vram_status
        except Exception as e:
            logger.error(f"Failed to combine audio chunks: {e}")
            logger.error(traceback.format_exc())
            return None, f"❌ Failed to combine audio: {e}", vram_status
    except Exception as e:
        logger.error(f"Generation failed: {e}")
        logger.error(traceback.format_exc())
        return None, f"❌ Generation failed: {e}", vram_status
    finally:
        clean_memory()

# Clear inputs function
def clear_inputs():
    logger.info("Clearing input fields")
    return "", 5.8, 18, 0.88, 0.15, 30, 120, "none", "none", "none", "none", "none", -23.0, "default", 1300, "128k", "44100", "16"

# Custom CSS with high-contrast colors and green border on active selection
css = """
body { 
    background: #121212; 
    color: #E6E6E6; 
    font-family: 'Arial', sans-serif; 
}
.header-container { 
    text-align: center; 
    padding: 15px 20px; 
    background: #1E1E1E; 
    border-bottom: 2px solid #00C853; 
}
#ghost-logo { 
    font-size: 48px; 
    color: #00C853; 
}
h1 { 
    color: #FFD600; 
    font-size: 28px; 
    font-weight: bold; 
}
h3 { 
    color: #FFD600; 
    font-size: 20px; 
    font-weight: bold; 
}
p { 
    color: #B0BEC5; 
    font-size: 14px; 
}
.input-container, .settings-container, .output-container, .logs-container { 
    max-width: 1200px; 
    margin: 20px auto; 
    padding: 20px; 
    background: #212121; 
    border: 1px solid #424242; 
    border-radius: 8px; 
}
.textbox { 
    background: #2C2C2C; 
    border: 1px solid #B0BEC5; 
    color: #E6E6E6; 
    font-size: 16px; 
}
.genre-buttons, .bitrate-buttons, .sample-rate-buttons, .bit-depth-buttons { 
    display: flex; 
    justify-content: center; 
    flex-wrap: wrap; 
    gap: 10px; 
}
.genre-btn, .bitrate-btn, .sample-rate-btn, .bit-depth-btn, button { 
    background: #0288D1; 
    border: 2px solid transparent; 
    color: #FFFFFF; 
    padding: 10px 20px; 
    border-radius: 5px; 
    font-size: 16px; 
    transition: all 0.3s ease; 
}
button:hover { 
    background: #03A9F4; 
    cursor: pointer; 
}
button:active, .genre-btn.active, .bitrate-btn.active, .sample-rate-btn.active, .bit-depth-btn.active { 
    border: 2px solid #00C853 !important; 
    background: #01579B; 
    color: #FFFFFF; 
}
.gradio-container { 
    padding: 20px; 
}
.group-container { 
    margin-bottom: 20px; 
    padding: 15px; 
    border: 1px solid #424242; 
    border-radius: 8px; 
}
.slider-label, .dropdown-label { 
    color: #FFD600; 
    font-size: 16px; 
    font-weight: bold; 
}
.slider, .dropdown { 
    background: #2C2C2C; 
    color: #E6E6E6; 
}
.output-container label, .logs-container label { 
    color: #FFD600; 
    font-size: 16px; 
    font-weight: bold; 
}
"""

# Build Gradio interface with updated visuals and default preset
logger.info("Building Gradio interface...")
with gr.Blocks(css=css) as demo:
    gr.Markdown("""
        <div class="header-container">
            <div id="ghost-logo">👻</div>
            <h1>GhostAI Music Generator 🎹</h1>
            <p>Create Instrumental Tracks with Ease</p>
        </div>
    """)
    
    with gr.Column(elem_classes="input-container"):
        gr.Markdown("### 🎸 Prompt Settings")
        instrumental_prompt = gr.Textbox(
            label="Instrumental Prompt ✍️",
            placeholder="Click a genre button or type your own instrumental prompt",
            lines=4,
            elem_classes="textbox"
        )
        with gr.Row(elem_classes="genre-buttons"):
            rhcp_btn = gr.Button("Red Hot Chili Peppers 🌶️", elem_classes="genre-btn")
            nirvana_btn = gr.Button("Nirvana Grunge 🎸", elem_classes="genre-btn")
            pearl_jam_btn = gr.Button("Pearl Jam Grunge 🦪", elem_classes="genre-btn")
            soundgarden_btn = gr.Button("Soundgarden Grunge 🌑", elem_classes="genre-btn")
            foo_fighters_btn = gr.Button("Foo Fighters 🤘", elem_classes="genre-btn")
            smashing_pumpkins_btn = gr.Button("Smashing Pumpkins 🎃", elem_classes="genre-btn")
            radiohead_btn = gr.Button("Radiohead 🧠", elem_classes="genre-btn")
            classic_rock_btn = gr.Button("Metallica Heavy Metal 🎸", elem_classes="genre-btn")
            alternative_rock_btn = gr.Button("Alternative Rock 🎵", elem_classes="genre-btn")
            post_punk_btn = gr.Button("Post-Punk 🖤", elem_classes="genre-btn")
            indie_rock_btn = gr.Button("Indie Rock 🎤", elem_classes="genre-btn")
            funk_rock_btn = gr.Button("Funk Rock 🕺", elem_classes="genre-btn")
            detroit_techno_btn = gr.Button("Detroit Techno 🎛️", elem_classes="genre-btn")
            deep_house_btn = gr.Button("Deep House 🏠", elem_classes="genre-btn")
    
    with gr.Column(elem_classes="settings-container"):
        gr.Markdown("### ⚙️ API Settings")
        with gr.Group(elem_classes="group-container"):
            cfg_scale = gr.Slider(
                label="CFG Scale 🎯",
                minimum=1.0,
                maximum=10.0,
                value=5.8,
                step=0.1,
                info="Controls how closely the music follows the prompt."
            )
            top_k = gr.Slider(
                label="Top-K Sampling 🔢",
                minimum=10,
                maximum=500,
                value=18,
                step=10,
                info="Limits sampling to the top k most likely tokens."
            )
            top_p = gr.Slider(
                label="Top-P Sampling 🎰",
                minimum=0.0,
                maximum=1.0,
                value=0.88,
                step=0.05,
                info="Keeps tokens with cumulative probability above p."
            )
            temperature = gr.Slider(
                label="Temperature 🔥",
                minimum=0.1,
                maximum=2.0,
                value=0.15,
                step=0.1,
                info="Controls randomness; lower values reduce noise."
            )
            total_duration = gr.Dropdown(
                label="Song Length ⏳ (seconds)",
                choices=[30, 60, 90, 120],
                value=30,
                info="Select the total duration of the track."
            )
            bpm = gr.Slider(
                label="Tempo 🎵 (BPM)",
                minimum=60,
                maximum=180,
                value=120,
                step=1,
                info="Beats per minute to set the track's tempo."
            )
            drum_beat = gr.Dropdown(
                label="Drum Beat 🥁",
                choices=["none", "standard rock", "funk groove", "techno kick", "jazz swing"],
                value="none",
                info="Select a drum beat style to influence the rhythm."
            )
            synthesizer = gr.Dropdown(
                label="Synthesizer 🎹",
                choices=["none", "analog synth", "digital pad", "arpeggiated synth"],
                value="none",
                info="Select a synthesizer style for electronic accents."
            )
            rhythmic_steps = gr.Dropdown(
                label="Rhythmic Steps 👣",
                choices=["none", "syncopated steps", "steady steps", "complex steps"],
                value="none",
                info="Select a rhythmic step style to enhance the beat."
            )
            bass_style = gr.Dropdown(
                label="Bass Style 🎸",
                choices=["none", "slap bass", "deep bass", "melodic bass"],
                value="none",
                info="Select a bass style to shape the low end."
            )
            guitar_style = gr.Dropdown(
                label="Guitar Style 🎸",
                choices=["none", "distorted", "clean", "jangle"],
                value="none",
                info="Select a guitar style to define the riffs."
            )
            target_volume = gr.Slider(
                label="Target Volume 🎚️ (dBFS RMS)",
                minimum=-30.0,
                maximum=-20.0,
                value=-23.0,
                step=1.0,
                info="Adjust output loudness (-23 dBFS is standard, -20 dBFS is louder, -30 dBFS is quieter)."
            )
            preset = gr.Dropdown(
                label="Preset Configuration 🎛️",
                choices=["default", "rock", "techno", "grunge", "indie", "funk_rock"],
                value="default",
                info="Select a preset optimized for specific genres."
            )
            max_steps = gr.Dropdown(
                label="Max Steps per Chunk 📏",
                choices=[1000, 1200, 1300, 1500],
                value=1300,
                info="Number of generation steps per chunk (1300=~26s, extended to 30s)."
            )
            bitrate_state = gr.State(value="128k")
            sample_rate_state = gr.State(value="44100")
            bit_depth_state = gr.State(value="16")
            with gr.Row(elem_classes="bitrate-buttons"):
                bitrate_128_btn = gr.Button("Set Bitrate to 128 kbps", elem_classes="bitrate-btn")
                bitrate_192_btn = gr.Button("Set Bitrate to 192 kbps", elem_classes="bitrate-btn")
                bitrate_320_btn = gr.Button("Set Bitrate to 320 kbps", elem_classes="bitrate-btn")
            with gr.Row(elem_classes="sample-rate-buttons"):
                sample_rate_22050_btn = gr.Button("Set Sampling Rate to 22.05 kHz", elem_classes="sample-rate-btn")
                sample_rate_44100_btn = gr.Button("Set Sampling Rate to 44.1 kHz", elem_classes="sample-rate-btn")
                sample_rate_48000_btn = gr.Button("Set Sampling Rate to 48 kHz", elem_classes="sample-rate-btn")
            with gr.Row(elem_classes="bit-depth-buttons"):
                bit_depth_16_btn = gr.Button("Set Bit Depth to 16-bit", elem_classes="bit-depth-btn")
                bit_depth_24_btn = gr.Button("Set Bit Depth to 24-bit", elem_classes="bit-depth-btn")

        with gr.Row(elem_classes="action-buttons"):
            gen_btn = gr.Button("Generate Music 🚀")
            clr_btn = gr.Button("Clear Inputs 🧹")
    
    with gr.Column(elem_classes="output-container"):
        gr.Markdown("### 🎧 Output")
        out_audio = gr.Audio(label="Generated Instrumental Track 🎵", type="filepath")
        status = gr.Textbox(label="Status 📢", interactive=False)
        vram_status = gr.Textbox(label="VRAM Usage 📊", interactive=False, value="")

    with gr.Column(elem_classes="logs-container"):
        gr.Markdown("### 📜 Logs")
        log_output = gr.Textbox(label="Last Log File Contents", lines=20, interactive=False)
        log_btn = gr.Button("View Last Log 📋")

    # Add JavaScript to handle button selection visuals
    def update_button_styles(selected_button):
        buttons = [
            "rhcp_btn", "nirvana_btn", "pearl_jam_btn", "soundgarden_btn", "foo_fighters_btn",
            "smashing_pumpkins_btn", "radiohead_btn", "classic_rock_btn", "alternative_rock_btn",
            "post_punk_btn", "indie_rock_btn", "funk_rock_btn", "detroit_techno_btn", "deep_house_btn",
            "bitrate_128_btn", "bitrate_192_btn", "bitrate_320_btn",
            "sample_rate_22050_btn", "sample_rate_44100_btn", "sample_rate_48000_btn",
            "bit_depth_16_btn", "bit_depth_24_btn"
        ]
        script = """
        <script>
            document.querySelectorAll('.genre-btn, .bitrate-btn, .sample-rate-btn, .bit-depth-btn').forEach(btn => {
                btn.classList.remove('active');
            });
            document.querySelector('#""" + selected_button + """').classList.add('active');
        </script>
        """
        return script

    rhcp_btn.click(set_red_hot_chili_peppers_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, gr.State(value=1)], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("rhcp_btn"))
    nirvana_btn.click(set_nirvana_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("nirvana_btn"))
    pearl_jam_btn.click(set_pearl_jam_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("pearl_jam_btn"))
    soundgarden_btn.click(set_soundgarden_grunge_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("soundgarden_btn"))
    foo_fighters_btn.click(set_foo_fighters_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("foo_fighters_btn"))
    smashing_pumpkins_btn.click(set_smashing_pumpkins_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("smashing_pumpkins_btn"))
    radiohead_btn.click(set_radiohead_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("radiohead_btn"))
    classic_rock_btn.click(set_classic_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("classic_rock_btn"))
    alternative_rock_btn.click(set_alternative_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("alternative_rock_btn"))
    post_punk_btn.click(set_post_punk_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("post_punk_btn"))
    indie_rock_btn.click(set_indie_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("indie_rock_btn"))
    funk_rock_btn.click(set_funk_rock_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("funk_rock_btn"))
    detroit_techno_btn.click(set_detroit_techno_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("detroit_techno_btn"))
    deep_house_btn.click(set_deep_house_prompt, inputs=[bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style], outputs=instrumental_prompt).then(None, None, None, js=update_button_styles("deep_house_btn"))
    bitrate_128_btn.click(set_bitrate_128, inputs=None, outputs=bitrate_state).then(None, None, None, js=update_button_styles("bitrate_128_btn"))
    bitrate_192_btn.click(set_bitrate_192, inputs=None, outputs=bitrate_state).then(None, None, None, js=update_button_styles("bitrate_192_btn"))
    bitrate_320_btn.click(set_bitrate_320, inputs=None, outputs=bitrate_state).then(None, None, None, js=update_button_styles("bitrate_320_btn"))
    sample_rate_22050_btn.click(set_sample_rate_22050, inputs=None, outputs=sample_rate_state).then(None, None, None, js=update_button_styles("sample_rate_22050_btn"))
    sample_rate_44100_btn.click(set_sample_rate_44100, inputs=None, outputs=sample_rate_state).then(None, None, None, js=update_button_styles("sample_rate_44100_btn"))
    sample_rate_48000_btn.click(set_sample_rate_48000, inputs=None, outputs=sample_rate_state).then(None, None, None, js=update_button_styles("sample_rate_48000_btn"))
    bit_depth_16_btn.click(set_bit_depth_16, inputs=None, outputs=bit_depth_state).then(None, None, None, js=update_button_styles("bit_depth_16_btn"))
    bit_depth_24_btn.click(set_bit_depth_24, inputs=None, outputs=bit_depth_state).then(None, None, None, js=update_button_styles("bit_depth_24_btn"))
    gen_btn.click(
        generate_music_wrapper,
        inputs=[instrumental_prompt, cfg_scale, top_k, top_p, temperature, total_duration, bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, target_volume, preset, max_steps, vram_status, bitrate_state, sample_rate_state, bit_depth_state],
        outputs=[out_audio, status, vram_status]
    )
    clr_btn.click(
        clear_inputs,
        inputs=None,
        outputs=[instrumental_prompt, cfg_scale, top_k, top_p, temperature, total_duration, bpm, drum_beat, synthesizer, rhythmic_steps, bass_style, guitar_style, target_volume, preset, max_steps, bitrate_state, sample_rate_state, bit_depth_state]
    )
    log_btn.click(
        get_latest_log,
        inputs=None,
        outputs=log_output
    )

# Launch locally without OpenAPI/docs
logger.info("Launching Gradio UI at http://localhost:9999...")
try:
    app = demo.launch(
        server_name="0.0.0.0",
        server_port=9999,
        share=True,
        inbrowser=False,
        show_error=True
    )
except Exception as e:
    logger.error(f"Failed to launch Gradio UI: {e}")
    logger.error(traceback.format_exc())
    sys.exit(1)