from pathlib import Path
from typing import Callable, List

from openai import OpenAI
from openai.types.audio import TranscriptionVerbose
from pyannote.pipeline import Pipeline
from pydantic import BaseModel
from pydub import AudioSegment
from openai import BadRequestError


class TranscriptSegment(BaseModel):
    audio_file: str | Path
    speaker: str
    i: str
    start: float
    end: float
    transcript: TranscriptionVerbose


def get_transcripts(
    diarization: Pipeline,
    audio_segment: AudioSegment,
    openai_api_key: str,
    whisper_model: str,
    whisper_prompt: str,
    whisper_language: str | None,
    tmp_dir: Path,
    progress_callback: Callable[[int, int], None] | None = None,
) -> List[TranscriptSegment]:
    client = OpenAI(api_key=openai_api_key)

    transcripts = []

    # Count total segments
    total_segments = sum(1 for _ in diarization.speaker_diarization.itertracks())
    segment_index = 0

    for turn, i, speaker in diarization.speaker_diarization.itertracks(
        yield_label=True
    ):
        segment_index += 1

        if progress_callback:
            progress_callback(segment_index, total_segments)

        start = turn.start * 1000
        end = turn.end * 1000

        chunck = audio_segment[slice(start, end)]

        chunk_filename = tmp_dir.joinpath(f"segment-{start}.mp3")

        chunck.export(chunk_filename, format="mp3")

        audio_chunk_segment = open(chunk_filename, "rb")

        params = {
            "file": audio_chunk_segment,
            "model": whisper_model,
            "response_format": "verbose_json",
            "timestamp_granularities": ["segment"],
            "prompt": whisper_prompt,
        }

        if whisper_language:
            params["language"] = whisper_language

        try:
            transcript = client.audio.transcriptions.create(**params)
        except BadRequestError as e:
            if "too short" in e.message.lower():
                continue
            raise

        transcripts.append(
            TranscriptSegment(
                audio_file=chunk_filename,
                speaker=speaker,
                i=i,
                start=start,
                end=end,
                transcript=transcript,
            )
        )

    return transcripts