🎬 Reddit Reading YouTube Shorts Generator

Automated, high-performance pipeline and Web Studio for generating and uploading viral 9:16 vertical YouTube Shorts from top Reddit posts and comments.

• 🌐 Reddit Scraping Engine: Dual-path scraper utilizing headless Chromium (Playwright) and public JSON API fallbacks to fetch top stories, comments, upvotes, and metadata with built-in deduplication history.
• 🎨 Dark Mode Card Renderer: HTML/CSS Jinja2 screenshot renderer creating Reddit post & comment cards (with pure-Python Pillow fallback).
• 🎙️ Neural TTS Voiceover: Microsoft Edge Neural TTS with natural male and female voices (Christopher, Guy, Ana, Jenny).
• 🎞️ GPU Hardware-Accelerated Compositor: Hardware encoding supporting NVIDIA NVENC (h264_nvenc), AMD AMF (h264_amf), and Intel QSV (h264_qsv) with automatic fallback to CPU libx264, rendering vertical 1080x1920 60/30fps videos.
• 💬 Viral Word-Level Dynamic Subtitles: On-screen animated captions ("Yellow Pill", "Neon Cyan Glow", "Minimalist White") synchronized with voiceover timestamps.
• 🎵 AI Soundtrack Generator: Meta MusicGen (facebook/musicgen-small) soundtrack synthesis with local DSP Lofi beat fallback.
• 🧠 Local LLM Metadata Engine: Automatically generates catchy titles, engaging descriptions, and hashtags using local Ollama models (e.g. Gemma 3).
• 📦 Batch Queue Mode: Generate and queue multiple shorts across custom subreddit pools.
• 🚀 YouTube Data API v3 Auto-Upload: Direct OAuth2 resumable upload flow with unlisted, public, or private visibility.
• 🖥️ Web Studio UI: Modern dark-mode FastAPI control room with live preview, video scrubber, background manager, and one-click YouTube API setup.

• Python 3.10+ (Python 3.11 recommended)
• FFmpeg (installed or automatically resolved via imageio-ffmpeg)
• Optional: GPU (NVIDIA with NVENC, AMD with AMF, or Intel with QuickSync) for hardware acceleration
• Optional: Ollama installed locally (ollama run gemma3:4b) for viral AI titles & descriptions

Clone the repository:

git clone https://github.com/BloopieBlair/RedditReadingApp.git
cd RedditReadingApp

Create and activate a virtual environment:

# Windows
python -m venv .venv
.\.venv\Scripts\activate

# Linux / macOS
python3 -m venv .venv
source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt
playwright install chromium

# Windows quick launcher
start.bat

# Or run via Python
python -m src.web.app

Open your browser and navigate to: http://127.0.0.1:8000

To enable automatic uploading of generated Shorts directly to your YouTube channel:

1. Click Enter YouTube API for Auto-Upload in the top-right header of the Web Studio.
2. In the setup modal, you can either:
   • Enter your OAuth Client ID & Client Secret, OR
   • Upload your client_secret.json file directly from Google Cloud Console.

1. Visit the Google Cloud Console.
2. Create a new project (e.g. Reddit-Shorts-Generator).
3. Under APIs & Services > Library, search for YouTube Data API v3 and click Enable.
4. Under APIs & Services > OAuth consent screen, select External, provide an app name, and add your Google email address under Test users.
5. Under APIs & Services > Credentials, click Create Credentials > OAuth client ID.
6. Select Desktop app as the application type.
7. Download the client_secret.json or copy the Client ID and Client Secret into the Web Studio modal.
8. Click Connect & Authorize YouTube to complete the one-time Google browser login.

You can easily use your own background gameplay videos (e.g. Minecraft parkour, Subway Surfers, GTA V stunt races, satisfying ASMR, etc.):

1. Drop your video into assets/backgrounds/ (supports .mp4, .mov, .webm, .mkv), OR
2. Select or upload it via the Web Studio UI in the Background Video selector.

• Universal Aspect Ratio Support: You can provide footage of any resolution or orientation (16:9 widescreen, 4:3, ultra-wide, or vertical 9:16).
• Automated Center Crop & Scaling: The background engine automatically computes aspect ratios, removes letterboxing, and rescales footage to crisp 1080×1920 (9:16 vertical) format.
• Randomized Dynamic Start Points: For long gameplay footage (e.g. 10–20 minute clips), the compositor automatically samples a fresh, random starting timestamp for each generated short so your channel videos remain dynamic and visually unique.
• Seamless Looping: If your background clip is shorter than the narrated story, the engine automatically loops the footage smoothly without frame drops.
• Persistent Library for Re-use: All placed backgrounds remain indexed in your local library for instant selection in both single-video and batch generation runs without needing to re-process.

You can also run every pipeline stage directly via the command line:

# Run full end-to-end pipeline in dry-run test mode
python main.py pipeline --subreddit AskReddit --dry-run

# Run full pipeline and upload to YouTube (unlisted)
python main.py pipeline --subreddit funny --upload --privacy unlisted

# Scrape top post and render HTML cards
python main.py scrape --subreddit AskReddit --limit 5

# Generate TTS voiceover from custom text
python main.py tts --text "This is a test Reddit story narration." --voice en-US-ChristopherNeural --output test.mp3

# Composite short video from existing scraped assets and audio
python main.py composite --post-json output/post.json --audio-dir output/audio/ --output output/short.mp4

# Upload an existing video to YouTube Shorts
python main.py upload --video output/short.mp4 --title "Incredible Reddit Story" --privacy unlisted

Run the test suite:

python -m pytest

RedditReadingApp/
├── assets/
│   ├── backgrounds/          # 9:16 background video assets
│   ├── music/                # AI generated & DSP soundtracks
│   ├── templates/            # Jinja2 card HTML templates
│   └── client_secret.json.example
├── src/
│   ├── scraper/              # Reddit scraper & card renderers
│   ├── tts/                  # Neural TTS voice engine
│   ├── video/                # Video compositor & subtitle overlays
│   ├── uploader/             # YouTube OAuth & upload manager
│   ├── web/                  # FastAPI web server & UI
│   ├── ai_generator.py       # Ollama/Gemma viral metadata generator
│   ├── cli.py                # Command line interface
│   ├── config.py             # App configurations & paths
│   ├── history.py            # Post deduplication tracker
│   ├── models.py             # Pydantic & dataclass schemas
│   └── pipeline.py           # Orchestration & batch pipeline
├── tests/                    # Unit, integration & adversarial test suite
├── .env.example              # Example environment variables
├── .gitignore                # Git exclusions
├── main.py                   # CLI entrypoint
├── pyproject.toml            # Project metadata & pytest configuration
├── requirements.txt          # Python dependencies
└── start.bat                 # Windows quickstart script

This project is free and open source. If it saved you time or you would like to support continued development, you can optionally support me on Ko-fi.

Donations do not unlock additional features or support. Every public release remains freely available here on GitHub.

This project is licensed under the MIT License - see the LICENSE file for details.