import os
import gradio as gr
import torch
import numpy as np
from transformers import pipeline
from diffusers import DiffusionPipeline
from pyannote.audio import Pipeline as PyannotePipeline
from dia.model import Dia
from dac.utils import load_model as load_dac_model
from accelerate import init_empty_weights, load_checkpoint_and_dispatch

# Retrieve HF_TOKEN from Secrets
HF_TOKEN = os.environ["HF_TOKEN"]

# Automatically shard across 4× L4 GPUs
device_map = "auto"

# 1. Load Descript Audio Codec (RVQ)
rvq = load_dac_model(tag="latest", model_type="44khz")
rvq.eval()
if torch.cuda.is_available():
    rvq = rvq.to("cuda")

# 2. Load Voice Activity Detection via Pyannote
vad_pipe = PyannotePipeline.from_pretrained(
    "pyannote/voice-activity-detection",
    use_auth_token=HF_TOKEN
)

# 3. Load Ultravox (speech-to-text + LLM) via Transformers
ultravox_pipe = pipeline(
    model="fixie-ai/ultravox-v0_4",
    trust_remote_code=True,
    device_map=device_map,
    torch_dtype=torch.float16
)

# 4. Load Audio Diffusion model via Diffusers
diff_pipe = DiffusionPipeline.from_pretrained(
    "teticio/audio-diffusion-instrumental-hiphop-256"
).to("cuda")

# 5. Load Dia TTS with meta-weight initialization and multi-GPU dispatch
with init_empty_weights():
    dia = Dia.from_pretrained("nari-labs/Dia-1.6B")
dia = load_checkpoint_and_dispatch(
    dia,
    "nari-labs/Dia-1.6B",
    device_map=device_map,
    dtype=torch.float16
)

# Inference function
def process_audio(audio):
    sr, array = audio
    array = array.numpy() if torch.is_tensor(array) else array

    # 2.1 VAD: segment speech regions (not used further here)
    _ = vad_pipe(array, sampling_rate=sr)

    # 1.1 RVQ encode/decode for discrete audio tokens
    x = torch.tensor(array).unsqueeze(0).to("cuda")
    codes = rvq.encode(x)
    decoded = rvq.decode(codes).squeeze().cpu().numpy()

    # 3. Ultravox ASR + LLM to generate response text
    ultra_out = ultravox_pipe({"array": decoded, "sampling_rate": sr})
    text = ultra_out.get("text", "")

    # 4. Diffusion-based prosody enhancement
    pros = diff_pipe(raw_audio=decoded)["audios"][0]

    # 5. Dia TTS synthesis with neutral emotion tag
    tts = dia.generate(f"[emotion:neutral] {text}")
    tts_np = tts.squeeze().cpu().numpy()
    tts_np = tts_np / np.max(np.abs(tts_np)) * 0.95

    return (sr, tts_np), text

# Gradio UI
with gr.Blocks(title="Maya AI 📈") as demo:
    gr.Markdown("## Maya-AI: Supernatural Conversational Agent")
    audio_in   = gr.Audio(source="microphone", type="numpy", label="Your Voice")
    send_btn   = gr.Button("Send")
    audio_out  = gr.Audio(label="AI’s Response")
    text_out   = gr.Textbox(label="Generated Text")
    send_btn.click(process_audio, inputs=audio_in, outputs=[audio_out, text_out])

if __name__ == "__main__":
    demo.launch()