import gradio as gr
import torch
import numpy as np
from soprano import SopranoTTS
from scipy.io.wavfile import write as wav_write
import tempfile
import os
import spaces

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(DEVICE)

# Load model once
model = SopranoTTS(
    backend="auto",
    device=DEVICE,
    cache_size_mb=100,
    decoder_batch_size=1,
)

SAMPLE_RATE = 32000


@spaces.GPU
def tts_stream(text, temperature, top_p, repetition_penalty, state):
    if not text.strip():
        yield None, state
        return

    chunks = []
    stream = model.infer_stream(
        text,
        chunk_size=20,
        temperature=temperature,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
    )

    for chunk in stream:
        if isinstance(chunk, torch.Tensor):
            audio_np = chunk.detach().cpu().numpy().astype(np.float32)
            chunks.append(audio_np)
            # stream partial audio
            yield (SAMPLE_RATE, audio_np), np.concatenate(chunks)

    #if chunks:
    #    final_audio = np.concatenate(chunks)
    #    yield (SAMPLE_RATE, final_audio), final_audio


def save_audio(state):
    if state is None or len(state) == 0:
        return None
    fd, path = tempfile.mkstemp(suffix=".wav")
    os.close(fd)
    wav_write(path, SAMPLE_RATE, state)
    return path


with gr.Blocks() as demo:
    state_audio = gr.State(None)

    with gr.Row():
        with gr.Column():
            gr.Markdown("# Soprano Demo")

            text_in = gr.Textbox(
                label="Input Text",
                placeholder="Enter text to synthesize...",
                lines=4,
            )

            with gr.Accordion("Advanced options", open=False):
                temperature = gr.Slider(
                    0.0, 1.0, value=0.3, step=0.05, label="Temperature"
                )
                top_p = gr.Slider(
                    0.0, 1.0, value=0.95, step=0.01, label="Top-p"
                )
                repetition_penalty = gr.Slider(
                    0.5, 2.0, value=1.2, step=0.05, label="Repetition penalty"
                )

            gen_btn = gr.Button("Generate")

        with gr.Column():
            audio_out = gr.Audio(
                label="Output Audio",
                autoplay=True,
                streaming=True,
            )
            download_btn = gr.Button("Download")
            file_out = gr.File(label="Download file")
            gr.Markdown(
                "Usage tips:\n\n"
                "- Soprano works best when each sentence is between 2 and 15 seconds long.\n"
                "- Although Soprano recognizes numbers and some special characters, it occasionally mispronounces them. Best results can be achieved by converting these into their phonetic form. (1+1 -> one plus one, etc)\n"
                "- If Soprano produces unsatisfactory results, you can easily regenerate it for a new, potentially better generation. You may also change the sampling settings for more varied results."
            )


    gen_btn.click(
        fn=tts_stream,
        inputs=[text_in, temperature, top_p, repetition_penalty, state_audio],
        outputs=[audio_out, state_audio],
    )

    download_btn.click(
        fn=save_audio,
        inputs=[state_audio],
        outputs=[file_out],
    )

demo.queue()
demo.launch()