import random
import torch
import torchaudio
from einops import rearrange
import gradio as gr
import spaces
import os
import uuid
from transformers import pipeline

# Importing the model-related functions
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond

# Load the model outside of the GPU-decorated function
def load_model():
    print("Loading model...")
    model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
    print("Model loaded successfully.")
    return model, model_config

# 번역 모델 로드
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")

# Function to set up, generate, and process the audio
@spaces.GPU(duration=120)  # Allocate GPU only when this function is called
def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
    print(f"Original Prompt: {prompt}")

    # 한글 텍스트를 영어로 번역
    translated_prompt = translator(prompt, max_length=512)[0]['translation_text']
    print(f"Translated Prompt: {translated_prompt}")
    
    seed = random.randint(0, 2**63 - 1)
    random.seed(seed)
    torch.manual_seed(seed)
    print(f"Using seed: {seed}")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")

    # Fetch the Hugging Face token from the environment variable
    hf_token = os.getenv('HF_TOKEN')
    print(f"Hugging Face token: {hf_token}")

    # Use pre-loaded model and configuration
    model, model_config = load_model()
    sample_rate = model_config["sample_rate"]
    sample_size = model_config["sample_size"]

    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")

    model = model.to(device)
    print("Model moved to device.")

    # Set up text and timing conditioning
    conditioning = [{
        "prompt": translated_prompt,
        "seconds_start": 0,
        "seconds_total": seconds_total
    }]
    print(f"Conditioning: {conditioning}")

    # Generate stereo audio
    print("Generating audio...")
    output = generate_diffusion_cond(
        model,
        steps=steps,
        cfg_scale=cfg_scale,
        conditioning=conditioning,
        sample_size=sample_size,
        sigma_min=0.3,
        sigma_max=500,
        sampler_type="dpmpp-3m-sde",
        device=device
    )
    print("Audio generated.")

    # Rearrange audio batch to a single sequence
    output = rearrange(output, "b d n -> d (b n)")
    print("Audio rearranged.")

    # Peak normalize, clip, convert to int16
    output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
    print("Audio normalized and converted.")

    # Generate a unique filename for the output
    unique_filename = f"output_{uuid.uuid4().hex}.wav"
    print(f"Saving audio to file: {unique_filename}")

    # Save to file
    torchaudio.save(unique_filename, output, sample_rate)
    print(f"Audio saved: {unique_filename}")

    # Return the path to the generated audio file
    return unique_filename

css = """
footer {
    visibility: hidden;
}
"""

# Setting up the Gradio Interface
interface = gr.Interface(theme="Nymbo/Nymbo_Theme", css=css,
    fn=generate_audio,
    inputs=[
        gr.Textbox(label="프롬프트", placeholder="여기에 텍스트 프롬프트를 입력하세요"),
        gr.Slider(0, 47, value=30, label="오디오 길이 (초)"),
        gr.Slider(10, 150, value=100, step=10, label="디퓨전 단계 수"),
        gr.Slider(1, 15, value=7, step=0.1, label="CFG 스케일")
    ],
    outputs=gr.Audio(type="filepath", label="생성된 오디오"),
)

# Pre-load the model to avoid multiprocessing issues
model, model_config = load_model()

# Launch the Interface
interface.launch()