import os
import time

import gradio as gr
import numpy as np
from dotenv import load_dotenv
from distil_whisper_fastrtc import get_stt_model
from fastapi import FastAPI
from fastrtc import (
    AdditionalOutputs,
    ReplyOnPause,
    Stream,
    get_tts_model,
    get_hf_turn_credentials,
)
from gradio.utils import get_space
from groq import Groq
from numpy.typing import NDArray

load_dotenv()
groq_client = Groq()
tts_model = get_tts_model()
stt_model = get_stt_model()
credentials = get_hf_turn_credentials(token=None)

# See "Talk to Claude" in Cookbook for an example of how to keep
# track of the chat history.
def response(
    audio: tuple[int, NDArray[np.int16 | np.float32]],
    chatbot: list[dict] | None = None,
):
    chatbot = chatbot or []
    messages = [{"role": d["role"], "content": d["content"]} for d in chatbot]
    start = time.time()
    text = stt_model.stt(audio)
    print("transcription", time.time() - start)
    print("prompt", text)
    chatbot.append({"role": "user", "content": text})
    yield AdditionalOutputs(chatbot)
    messages.append({"role": "user", "content": text})
    response_text = (
        groq_client.chat.completions.create(
            model="llama-3.1-8b-instant",
            max_tokens=512,
            messages=messages,  # type: ignore
        )
        .choices[0]
        .message.content
    )

    chatbot.append({"role": "assistant", "content": response_text})

    # Convert response to audio using TTS model
    for audio_chunk in tts_model.stream_tts_sync(response_text or ""):
        # Yield the audio chunk
        yield audio_chunk
    yield AdditionalOutputs(chatbot)


chatbot = gr.Chatbot(type="messages")
stream = Stream(
    modality="audio",
    mode="send-receive",
    handler=ReplyOnPause(response, input_sample_rate=16000),
    rtc_configuration=credentials,
    additional_outputs_handler=lambda a, b: b,
    additional_inputs=[chatbot],
    additional_outputs=[chatbot],
    concurrency_limit=5 if get_space() else None,
    time_limit=90 if get_space() else None,
    ui_args={"title": "LLM Voice Chat (Powered by Groq, and WebRTC ⚡️)"},
)

# Mount the STREAM UI to the FastAPI app
# Because I don't want to build the UI manually
app = FastAPI()
app = gr.mount_gradio_app(app, stream.ui, path="/")


if __name__ == "__main__":
    import os

    os.environ["GRADIO_SSR_MODE"] = "false"

    if (mode := os.getenv("MODE")) == "UI":
        stream.ui.launch(server_port=7860)
    else:
        stream.ui.launch(server_port=7860)