import argparse
import asyncio
import os

from dotenv import load_dotenv
from moviepy import VideoFileClip
from openai import OpenAI
from pydub import AudioSegment
from pydub.playback import play

DEFAULT_RESPONSES_INSTRUCTIONS = "You will be provided a video transcription for which you are to generate a blog post in Markdown format summarizing the video's contents."
TMP_AUDIO_PATH = "/tmp/video-summary-bot-tmp-audio.wav"

async def main():
    parser = argparse.ArgumentParser(description="Use AI models to summarize videos")
    parser.add_argument('--video-file', type=str, help="Path to the video to be summarized")
    parser.add_argument('--segment-duration', type=int, help="Lenght of audio segments")
    args = parser.parse_args()
    load_dotenv()
    VideoFileClip(args.video_file).audio.write_audiofile(TMP_AUDIO_PATH)
    audio = AudioSegment.from_wav(TMP_AUDIO_PATH)
    segments = [audio[i:i + args.segment_duration] for i in range(0, len(audio), args.segment_duration)]
    # TODO: Test OpenAI
    client = OpenAI()
    transcription = ' '.join([
        await client.audio.transcriptions.create(
            model=args.transcription_model,
            file=each
            ).text for each in segments
        ])
    summary = client.responses.create(
            model=args.responses_model,
            instructions=DEFAULT_RESPONSES_INSTRUCTIONS,
            input=transcription
            )
    return summary

if __name__ == '__main__':
    summary = main()
    print(summary)