import gradio as gr
import edge_tts
import asyncio
import tempfile
import os

async def get_voices():
    # Obtiene la lista de voces disponibles
    voices = await edge_tts.list_voices()
    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}

async def text_to_speech(text, voice, rate, pitch):
    if not text.strip():
        return None, "Please enter text to convert."
    if not voice:
        return None, "Please select a voice."
    
    # Corregida la identación y extracción del nombre
    voice_short_name = voice.split(" - ")[0]
    rate_str = f"{rate:+d}%"
    pitch_str = f"{pitch:+d}Hz"
    
    communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    
    return tmp_path, None

async def tts_interface(text, voice, rate, pitch):
    audio, warning = await text_to_speech(text, voice, rate, pitch)
    if warning:
        # Lanzar la advertencia y devolver None para el audio
        gr.Warning(warning)
        return None, warning
    # Devolver el audio y limpiar el campo de Markdown
    return audio, ""

async def create_demo():
    voices = await get_voices()
    description = "Convert text to speech using Edge TTS. Adjust speech rate and pitch."
    
    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Input Text", lines=5),
            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Voice", value=""),
            gr.Slider(minimum=-50, maximum=50, value=0, label="Speech Rate Adjustment (%)", step=1),
            gr.Slider(minimum=-20, maximum=20, value=0, label="Pitch Adjustment (Hz)", step=1)
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.Markdown(label="Status") # Cambiado para recibir el mensaje de texto/error
        ],
        title="Free-TTS Unlimited",
        description=description,
        analytics_enabled=False,
        api_name=None
    )
    return demo

async def main():
    demo = await create_demo()
    # Gradio 5: queue ya no requiere obligatoriamente default_concurrency_limit
    demo.queue()
    demo.launch(show_api=False)

if __name__ == "__main__":
    asyncio.run(main())