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Gramps e9fc694970 🧠 Metadata finalization: integrated clip/session merge, persistent archive, and sequential title suffixing 
- Merged clip-level and session-level metadata into a unified object
- Stored notes.json inline and as a child field for structured access
- Implemented local NoSQL-style history archive for uploaded videos
- Added YouTube/PeerTube URL arrays to metadata post-upload
- Ensured sequential titling for multiple sessions on the same day
- Removed source folder after upload when DEBUG == False
2025-07-27 20:15:09 -07:00
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Create FUNDING.yml
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assets
🔧 v0.1.1 prep: staging upload + OpenAI integration code
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✅ Finalized YouTube upload flow: metadata archiving, title suffixing, and cleanup
2025-07-26 09:06:31 -07:00
.env.example
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.gitattributes
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.gitignore
🛠️ Initial YouTube upload and description generation: OAuth, montage flow, thumbnail setup, env config. Work in progress by gramps@llamachile.shop
2025-07-24 19:34:47 -07:00
authorize_youtube.py
🛠️ Initial YouTube upload and description generation: OAuth, montage flow, thumbnail setup, env config. Work in progress by gramps@llamachile.shop
2025-07-24 19:34:47 -07:00
launch.json
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LICENSE
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main.py
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python
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README.md
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README.txt
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sync_wiki.py
🧠 Metadata finalization: integrated clip/session merge, persistent archive, and sequential title suffixing
2025-07-27 20:15:09 -07:00
test_moviepy.py
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test_yt_ul.py
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2025-07-23 11:52:09 -07:00
upload_youtube_montage.py
🛠️ Initial YouTube upload and description generation: OAuth, montage flow, thumbnail setup, env config. Work in progress by gramps@llamachile.shop
2025-07-24 19:34:47 -07:00

README.md

Llama Chile Shop Video Automation Pipeline

This project automates the rendering, branding, and publishing of Fortnite gameplay clips for YouTube and PeerTube.

Features

  • Auto-detection of new stream folders
  • Dynamic title card overlay
  • Automated rendering and social post generation
  • Vertical & widescreen output

Setup

  1. Clone the repo.
  2. Create a .env file (see ENVIRONMENT.md for required keys).
  3. Install dependencies:
pip install -r requirements.txt
Description
Video Pipeline Automation The Video Pipeline project automates the process of extracting, processing, and uploading highlights from livestreams, primarily for Fortnite gameplay. This system integrates Eklipse.gg for AI-based highlight detection, DaVinci Resolve for video editing, and custom Python scripts for rendering and metadata management. The pipeline supports seamless uploads to YouTube and PeerTube, automatic social media announcements, and maintains a detailed history of video metadata for each session. Key Features: Automated highlight extraction from livestreams Dynamic title card generation with Fortnite-themed branding Streamlined YouTube and PeerTube uploads with dynamic descriptions Metadata tracking and session management Social media integration for automatic posts to Mastodon and Bluesky This project is designed to optimize video content creation and management for livestreamers, ensuring efficiency and consistency in processing and sharing content.
Readme GPL-3.0 108 KiB
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