Execute application with Gitea Actions

.env_example

@@ -3,3 +3,4 @@ OPENAI_BASE_URL =
 OPENAI_RESPONSES_PROMPT = 
 SEGMENT_DURATION = 
 TMP_AUDIO_PATH = 
+TMP_VIDEO_PATH = 

.gitea/workflows/execute.yaml

@@ -0,0 +1,22 @@
+name: Execute video summary bot
+on:
+  workflow_dispatch:
+    inputs:
+      video-url:
+        description: "URL for the video to be analyzed"
+        required: true
+jobs:
+  Python:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      - name: Run applicaiton
+        run: python app.py

app.py

@@ -1,4 +1,3 @@
-import argparse
 import os
 
 from dotenv import load_dotenv
@@ -14,12 +13,24 @@ def main(args):
             openai_client,
             transcribe_audio(
                 openai_client,
-                get_audio_from_video(args.video_file_path)
+                get_audio_from_video(
+                    get_video_from_url()
+                    )
                 )
             )
 
-def get_audio_from_video(video_file_path):
+def get_video_from_url():
+    video_file_url = os.getenv('INPUT_VIDEO_URL')
+    video_file_path = os.getenv('TMP_VIDEO_PATH' , '/tmp/video_summary_bot_tmp_video.mp4')
+    request = requests.get(video_file_url)
+    with open(video_file_path, 'wb') as f:
+        for chunk in requests.get(video_file_url).iter_content(chunk_size=255):
+            if chunk:
+                f.write(chunk)
+
+def get_audio_from_video():
     tmp_audio_path = os.getenv('TMP_AUDIO_PATH', '/tmp/video_summary_bot_tmp_audio.wav')
+    video_file_path = os.getenv('TMP_VIDEO_PATH')
     VideoFileClip(video_file_path).audio.write_audiofile(tmp_audio_path)
     return AudioSegment.from_wav(tmp_audio_path)
 
@@ -44,6 +55,4 @@ def summarize_transcription(openai_client, transcription):
 
 if __name__ == '__main__':
     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())