# Copyright (c) 2025 SparkAudio
#               2025 Xinsheng Wang (w.xinshawn@gmail.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import torch
import soundfile as sf
import logging
import gradio as gr
from datetime import datetime
from cli.SparkTTS import SparkTTS
from sparktts.utils.token_parser import LEVELS_MAP_UI
import spaces
from huggingface_hub import snapshot_download

def initialize_model(model_dir=snapshot_download("SparkAudio/Spark-TTS-0.5B"), device=0):
    """Load the model once at the beginning."""
    logging.info(f"Loading model from: {model_dir}")
    device = torch.device("cuda")
    model = SparkTTS(model_dir, device)
    return model

@spaces.GPU
def run_tts(
    text,
    model,
    prompt_text=None,
    prompt_speech=None,
    gender=None,
    pitch=None,
    speed=None,
    save_dir="example/results",
):
    """Perform TTS inference and save the generated audio."""
    logging.info(f"Saving audio to: {save_dir}")

    if prompt_text is not None:
        prompt_text = None if len(prompt_text) <= 1 else prompt_text

    # Ensure the save directory exists
    os.makedirs(save_dir, exist_ok=True)

    # Generate unique filename using timestamp
    timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
    save_path = os.path.join(save_dir, f"{timestamp}.wav")

    logging.info("Starting inference...")

    # Perform inference and save the output audio
    with torch.no_grad():
        wav = model.inference(
            text,
            prompt_speech,
            prompt_text,
            gender,
            pitch,
            speed,
        )

        sf.write(save_path, wav, samplerate=16000)

    logging.info(f"Audio saved at: {save_path}")

    return save_path, model  # Return model along with audio path

@spaces.GPU
def voice_clone(text, model, prompt_text, prompt_wav_upload, prompt_wav_record):
    """Gradio interface for TTS with prompt speech input."""
    # Determine prompt speech (from audio file or recording)
    prompt_speech = prompt_wav_upload if prompt_wav_upload else prompt_wav_record
    prompt_text = None if len(prompt_text) < 2 else prompt_text
    audio_output_path, model = run_tts(
        text, model, prompt_text=prompt_text, prompt_speech=prompt_speech
    )

    return audio_output_path, model

@spaces.GPU
def voice_creation(text, model, gender, pitch, speed):
    """Gradio interface for TTS with control over voice attributes."""
    pitch = LEVELS_MAP_UI[int(pitch)]
    speed = LEVELS_MAP_UI[int(speed)]
    audio_output_path, model = run_tts(
        text, model, gender=gender, pitch=pitch, speed=speed
    )
    return audio_output_path, model


def build_ui(model_dir, device=0):
    with gr.Blocks() as demo:
        # Initialize model
        model = initialize_model(model_dir, device="cuda")
        # Use HTML for centered title
        gr.HTML('<h1 style="text-align: center;">Spark-TTS by SparkAudio</h1>')
        with gr.Tabs():
            # Voice Clone Tab
            with gr.TabItem("Voice Clone"):
                gr.Markdown(
                    "### Upload reference audio or recording （上传参考音频或者录音）"
                )

                with gr.Row():
                    prompt_wav_upload = gr.Audio(
                        sources="upload",
                        type="filepath",
                        label="Choose the prompt audio file, ensuring the sampling rate is no lower than 16kHz.",
                    )
                    prompt_wav_record = gr.Audio(
                        sources="microphone",
                        type="filepath",
                        label="Record the prompt audio file.",
                    )

                with gr.Row():
                    text_input = gr.Textbox(
                        label="Text", lines=3, placeholder="Enter text here"
                    )
                    prompt_text_input = gr.Textbox(
                        label="Text of prompt speech (Optional; recommended for cloning in the same language.)",
                        lines=3,
                        placeholder="Enter text of the prompt speech.",
                    )

                audio_output = gr.Audio(
                    label="Generated Audio", autoplay=True, streaming=True
                )

                generate_buttom_clone = gr.Button("Generate")

                generate_buttom_clone.click(
                    voice_clone,
                    inputs=[
                        text_input,
                        gr.State(model),
                        prompt_text_input,
                        prompt_wav_upload,
                        prompt_wav_record,
                    ],
                    outputs=[audio_output, gr.State(model)],
                )

            # Voice Creation Tab
            with gr.TabItem("Voice Creation"):
                gr.Markdown(
                    "### Create your own voice based on the following parameters"
                )

                with gr.Row():
                    with gr.Column():
                        gender = gr.Radio(
                            choices=["male", "female"], value="male", label="Gender"
                        )
                        pitch = gr.Slider(
                            minimum=1, maximum=5, step=1, value=3, label="Pitch"
                        )
                        speed = gr.Slider(
                            minimum=1, maximum=5, step=1, value=3, label="Speed"
                        )
                    with gr.Column():
                        text_input_creation = gr.Textbox(
                            label="Input Text",
                            lines=3,
                            placeholder="Enter text here",
                            value="You can generate a customized voice by adjusting parameters such as pitch and speed.",
                        )
                        create_button = gr.Button("Create Voice")

                audio_output = gr.Audio(
                    label="Generated Audio", autoplay=True, streaming=True
                )
                create_button.click(
                    voice_creation,
                    inputs=[text_input_creation, gr.State(model), gender, pitch, speed],
                    outputs=[audio_output, gr.State(model)],
                )

    return demo


if __name__ == "__main__":
    demo = build_ui(model_dir=snapshot_download("SparkAudio/Spark-TTS-0.5B"), device=0)
    demo.queue().launch()