import io
from typing import List
import threading
from multiprocessing import Queue
from queue import Empty
from faster_whisper import WhisperModel

import logging
import sys

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)],
)

# Get a logger for your app
logger = logging.getLogger(__name__)


class AudioTranscriber(threading.Thread):
    def __init__(
        self,
        audio_queue: "Queue[io.BytesIO]",
        text_queue: "Queue[str]",
        language: str = "en",
        confidence_threshold: float = 0.5,
    ):
        super().__init__()
        self.audio_queue = audio_queue
        self.action_queue = text_queue
        self.daemon = True  # Thread will exit when main program exits
        self.max_buffer_size = 4
        self.language = language
        self.confidence_threshold = confidence_threshold
        self.buffer: List[io.BytesIO] = []
        self.transcriber = WhisperModel(
            "large",
            device="cuda",
            compute_type="int8",
        )

    def run(self):
        while True:
            try:
                # Wait for 1 second before timing out and checking again
                audio_chunk = self.audio_queue.get(timeout=1)

                self.buffer.append(audio_chunk)

                while len(self.buffer) >= self.max_buffer_size:
                    _ = self.buffer.pop(0)

                # Create a BytesIO object from the joined buffer
                joined_buffer = io.BytesIO(
                    b"".join([chunk.getvalue() for chunk in self.buffer])
                )

                segments, info = self.transcriber.transcribe(
                    joined_buffer, language=self.language
                )

                # Put the transcription results in the output queue
                for segment in segments:
                    if segment.no_speech_prob <= self.confidence_threshold:
                        self.action_queue.put(segment.text)
                        # Still print for debugging
                        logger.info(
                            f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}"
                        )
                    else:
                        self.action_queue.put("")

            except Empty:
                continue  # If queue is empty, continue waiting
            except Exception as e:
                logger.error(f"Error processing audio chunk: {e}")