# Handle spaces dependency for localhost
try:
    import spaces
    SPACES_AVAILABLE = True
except ImportError:
    # Mock spaces for localhost
    class spaces:
        class GPU:
            def __init__(self, duration=None):
                pass
            def __call__(self, func):
                return func
    SPACES_AVAILABLE = False

from kokoro import KModel, KPipeline
import gradio as gr
import os
import random
import torch

IS_DUPLICATE = not os.getenv('SPACE_ID', '').startswith('hexgrad/')
CUDA_AVAILABLE = torch.cuda.is_available()
if not IS_DUPLICATE:
    try:
        import kokoro
        import misaki
        print('DEBUG', kokoro.__version__, CUDA_AVAILABLE, misaki.__version__)
    except ImportError:
        print('DEBUG: kokoro/misaki version info not available')

CHAR_LIMIT = None if IS_DUPLICATE else 5000
# Try to use your own model repository, fallback to original if not accessible
MODEL_REPO_ID = os.getenv('MODEL_REPO_ID', None)
# Check if model files exist locally first (in Space)
if os.path.exists('kokoro-v1_0.pth') and os.path.exists('config.json'):
    # Use local files - don't specify repo_id to use local config.json
    try:
        models = {gpu: KModel().to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
        pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False) for lang_code in 'ab'}
        print("✓ Using local model files from Space")
    except Exception as e:
        print(f"Error loading local model, trying repository: {e}")
        # Fallback to repository
        MODEL_REPO_ID = MODEL_REPO_ID or 'ashishkblink/NeuralVoiceM2TTS'
        try:
            models = {gpu: KModel(repo_id=MODEL_REPO_ID).to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
            pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False, repo_id=MODEL_REPO_ID) for lang_code in 'ab'}
            print(f"✓ Using model from repository: {MODEL_REPO_ID}")
        except Exception as e2:
            print(f"Error loading from custom repository, falling back to original: {e2}")
            # Final fallback to original
            models = {gpu: KModel(repo_id='hexgrad/Kokoro-82M').to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
            pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False, repo_id='hexgrad/Kokoro-82M') for lang_code in 'ab'}
            print("✓ Using original model repository as fallback")
else:
    # No local files, try custom repository first
    MODEL_REPO_ID = MODEL_REPO_ID or 'ashishkblink/NeuralVoiceM2TTS'
    try:
        models = {gpu: KModel(repo_id=MODEL_REPO_ID).to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
        pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False, repo_id=MODEL_REPO_ID) for lang_code in 'ab'}
        print(f"✓ Using model from repository: {MODEL_REPO_ID}")
    except Exception as e:
        print(f"Error loading from custom repository, falling back to original: {e}")
        # Fallback to original
        models = {gpu: KModel(repo_id='hexgrad/Kokoro-82M').to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
        pipelines = {lang_code: KPipeline(lang_code=lang_code, model=False, repo_id='hexgrad/Kokoro-82M') for lang_code in 'ab'}
        print("✓ Using original model repository as fallback")
pipelines['a'].g2p.lexicon.golds['kokoro'] = 'kˈOkəɹO'
pipelines['b'].g2p.lexicon.golds['kokoro'] = 'kˈQkəɹQ'

@spaces.GPU(duration=30)
def forward_gpu(ps, ref_s, speed):
    return models[True](ps, ref_s, speed)

def generate_first(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
    pipeline = pipelines[voice[0]]
    pack = pipeline.load_voice(voice)
    use_gpu = use_gpu and CUDA_AVAILABLE
    for _, ps, _ in pipeline(text, voice, speed):
        ref_s = pack[len(ps)-1]
        try:
            if use_gpu:
                audio = forward_gpu(ps, ref_s, speed)
            else:
                audio = models[False](ps, ref_s, speed)
        except gr.exceptions.Error as e:
            if use_gpu:
                gr.Warning(str(e))
                gr.Info('Retrying with CPU. To avoid this error, change Hardware to CPU.')
                audio = models[False](ps, ref_s, speed)
            else:
                raise gr.Error(e)
        return (24000, audio.numpy()), ps
    return None, ''

# Arena API
def predict(text, voice='af_heart', speed=1):
    return generate_first(text, voice, speed, use_gpu=False)[0]

def tokenize_first(text, voice='af_heart'):
    pipeline = pipelines[voice[0]]
    for _, ps, _ in pipeline(text, voice):
        return ps
    return ''

def generate_all(text, voice='af_heart', speed=1, use_gpu=CUDA_AVAILABLE):
    text = text if CHAR_LIMIT is None else text.strip()[:CHAR_LIMIT]
    pipeline = pipelines[voice[0]]
    pack = pipeline.load_voice(voice)
    use_gpu = use_gpu and CUDA_AVAILABLE
    first = True
    for _, ps, _ in pipeline(text, voice, speed):
        ref_s = pack[len(ps)-1]
        try:
            if use_gpu:
                audio = forward_gpu(ps, ref_s, speed)
            else:
                audio = models[False](ps, ref_s, speed)
        except gr.exceptions.Error as e:
            if use_gpu:
                gr.Warning(str(e))
                gr.Info('Switching to CPU')
                audio = models[False](ps, ref_s, speed)
            else:
                raise gr.Error(e)
        yield 24000, audio.numpy()
        if first:
            first = False
            yield 24000, torch.zeros(1).numpy()

with open('en.txt', 'r') as r:
    random_quotes = [line.strip() for line in r]

def get_random_quote():
    return random.choice(random_quotes)

def get_gatsby():
    with open('gatsby5k.md', 'r') as r:
        return r.read().strip()

def get_frankenstein():
    with open('frankenstein5k.md', 'r') as r:
        return r.read().strip()

CHOICES = {
'🇺🇸 🚺 Heart ❤️': 'af_heart',
'🇺🇸 🚺 Bella 🔥': 'af_bella',
'🇺🇸 🚺 Nicole 🎧': 'af_nicole',
'🇺🇸 🚺 Aoede': 'af_aoede',
'🇺🇸 🚺 Kore': 'af_kore',
'🇺🇸 🚺 Sarah': 'af_sarah',
'🇺🇸 🚺 Nova': 'af_nova',
'🇺🇸 🚺 Sky': 'af_sky',
'🇺🇸 🚺 Alloy': 'af_alloy',
'🇺🇸 🚺 Jessica': 'af_jessica',
'🇺🇸 🚺 River': 'af_river',
'🇺🇸 🚹 Michael': 'am_michael',
'🇺🇸 🚹 Fenrir': 'am_fenrir',
'🇺🇸 🚹 Puck': 'am_puck',
'🇺🇸 🚹 Echo': 'am_echo',
'🇺🇸 🚹 Eric': 'am_eric',
'🇺🇸 🚹 Liam': 'am_liam',
'🇺🇸 🚹 Onyx': 'am_onyx',
'🇺🇸 🚹 Santa': 'am_santa',
'🇺🇸 🚹 Adam': 'am_adam',
'🇬🇧 🚺 Emma': 'bf_emma',
'🇬🇧 🚺 Isabella': 'bf_isabella',
'🇬🇧 🚺 Alice': 'bf_alice',
'🇬🇧 🚺 Lily': 'bf_lily',
'🇬🇧 🚹 George': 'bm_george',
'🇬🇧 🚹 Fable': 'bm_fable',
'🇬🇧 🚹 Lewis': 'bm_lewis',
'🇬🇧 🚹 Daniel': 'bm_daniel',
}
for v in CHOICES.values():
    pipelines[v[0]].load_voice(v)

TOKEN_NOTE = '''
💡 Customize pronunciation with Markdown link syntax and /slashes/ like `[example](/ɪɡˈzæmpəl/)`

💬 To adjust intonation, try punctuation `;:,.!?—…"()“”` or stress `ˈ` and `ˌ`

⬇️ Lower stress `[1 level](-1)` or `[2 levels](-2)`

⬆️ Raise stress 1 level `[or](+2)` 2 levels (only works on less stressed, usually short words)
'''

with gr.Blocks() as generate_tab:
    out_audio = gr.Audio(label='Output Audio', interactive=False, streaming=False, autoplay=True)
    generate_btn = gr.Button('Generate', variant='primary')
    with gr.Accordion('Output Tokens', open=True):
        out_ps = gr.Textbox(interactive=False, show_label=False, info='Tokens used to generate the audio, up to 510 context length.')
        tokenize_btn = gr.Button('Tokenize', variant='secondary')
        gr.Markdown(TOKEN_NOTE)
        predict_btn = gr.Button('Predict', variant='secondary', visible=False)

STREAM_NOTE = ['⚠️ There is an unknown Gradio bug that might yield no audio the first time you click `Stream`.']
if CHAR_LIMIT is not None:
    STREAM_NOTE.append(f'✂️ Each stream is capped at {CHAR_LIMIT} characters.')
    STREAM_NOTE.append('🚀 For longer text generation, you can duplicate this space or use the API.')
STREAM_NOTE = '\n\n'.join(STREAM_NOTE)

with gr.Blocks() as stream_tab:
    out_stream = gr.Audio(label='Output Audio Stream', interactive=False, streaming=True, autoplay=True)
    with gr.Row():
        stream_btn = gr.Button('Stream', variant='primary')
        stop_btn = gr.Button('Stop', variant='stop')
    with gr.Accordion('Note', open=True):
        gr.Markdown(STREAM_NOTE)
        gr.DuplicateButton()

BANNER_TEXT = '''
**TTS model with 82 million parameters.**

This demo showcases high-quality text-to-speech synthesis in English and other languages.
'''
API_OPEN = True  # Enable API for all Spaces
API_NAME = None  # Use default API endpoints
with gr.Blocks() as app:
    with gr.Row():
        gr.Markdown(BANNER_TEXT, container=True)
    with gr.Row():
        with gr.Column():
            text = gr.Textbox(label='Input Text', info=f"Up to ~500 characters per Generate, or {'∞' if CHAR_LIMIT is None else CHAR_LIMIT} characters per Stream")
            with gr.Row():
                voice = gr.Dropdown(list(CHOICES.items()), value='af_heart', label='Voice', info='Quality and availability vary by language')
                use_gpu = gr.Dropdown(
                    [('ZeroGPU 🚀', True), ('CPU 🐌', False)],
                    value=CUDA_AVAILABLE,
                    label='Hardware',
                    info='GPU is usually faster, but has a usage quota',
                    interactive=CUDA_AVAILABLE
                )
            speed = gr.Slider(minimum=0.5, maximum=2, value=1, step=0.1, label='Speed')
            random_btn = gr.Button('🎲 Random Quote 💬', variant='secondary')
            with gr.Row():
                gatsby_btn = gr.Button('🥂 Gatsby 📕', variant='secondary')
                frankenstein_btn = gr.Button('💀 Frankenstein 📗', variant='secondary')
        with gr.Column():
            gr.TabbedInterface([generate_tab, stream_tab], ['Generate', 'Stream'])
    random_btn.click(fn=get_random_quote, inputs=[], outputs=[text], api_name=API_NAME)
    gatsby_btn.click(fn=get_gatsby, inputs=[], outputs=[text], api_name=API_NAME)
    frankenstein_btn.click(fn=get_frankenstein, inputs=[], outputs=[text], api_name=API_NAME)
    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
    tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps], api_name=API_NAME)
    stream_event = stream_btn.click(fn=generate_all, inputs=[text, voice, speed, use_gpu], outputs=[out_stream], api_name=API_NAME)
    stop_btn.click(fn=None, cancels=stream_event)
    predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio], api_name=API_NAME)

if __name__ == '__main__':
    # Get port from environment variable (Railway/cloud platforms) or default to 7860
    port = int(os.getenv('PORT', 7860))
    # For localhost and cloud, launch with server_name 0.0.0.0 to allow access from network
    # Note: show_api parameter removed as it's not supported in all Gradio versions
    # Enable API for all environments - required for API access
    app.queue(api_open=True).launch(ssr_mode=True, server_name='0.0.0.0', server_port=port)