OpenAI functions and clearer parameters

.env_example

@@ -1,2 +1,5 @@
-OPENAI_API_KEY=
-OPENAI_BASE_URL=
+OPENAI_API_KEY = 
+OPENAI_BASE_URL = 
+OPENAI_RESPONSES_INSTRUCTIONS = 
+SEGMENT_DURATION = 
+TMP_AUDIO_PATH = 

app.py

@@ -1,40 +1,48 @@
 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"
+PROMPT = "You will be provided a video transcription for which you are to generate a blog post in Markdown format summarizing the video contents."
 
-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,
+def main(args):
+    openai_client = OpenAI()
+    return summarize_transcription(
+            openai_client,
+            transcribe_audio(
+                openai_client,
+                get_audio_from_video(args.video_file_path)
+                )
+            )
+
+def get_audio_from_video(video_file_path):
+    tmp_audio_path = os.getenv('TMP_AUDIO_PATH', '/tmp/video_summary_bot_tmp_audio.wav')
+    VideoFileClip(video_file_path).audio.write_audiofile(tmp_audio_path)
+    return AudioSegment.from_wav(tmp_audio_path)
+
+def transcribe_audio(openai_client, audio):
+    segment_duration = int(os.getenv('SEGMENT_DURATION', 30000)),
+    transcription_model = os.getenv('OPENAI_TRANSCRIPTION_MODEL', 'whisper-1')
+    return ' '.join([
+        openai_client.audio.transcriptions.create(
+            model=transcription_model,
             file=each
-            ).text for each in segments
+            ).text for each in [audio[i:i + segment_duration] for i in range(0, len(audio), segment_duration)]
         ])
-    summary = client.responses.create(
-            model=args.responses_model,
-            instructions=DEFAULT_RESPONSES_INSTRUCTIONS,
+
+def summarize_transcription(openai_client, transcription):
+    responses_model = os.getenv('OPENAI_RESPONSES_MODEL', 'whisper-1')
+    return client.responses.create(
+            model=responses_model,
+            instructions=PROMPT,
             input=transcription
             )
-    return summary
 
 if __name__ == '__main__':
-    summary = main()
-    print(summary)
+    load_dotenv()
+    parser = argparse.ArgumentParser(description="Use AI models to summarize videos")
+    parser.add_argument('--video-file-path', type=str, help="Path to the video to be summarized")
+    main(parser.parse_args())