⚠️ Development Status: Edit Mind is currently in active development and not yet production-ready.
Expect incomplete features and occasional bugs. We welcome contributors to help us reach v1.0!

Edit Mind is a cross-platform desktop app that acts as an editor’s second brain.

It locally indexes your entire video library, generating deep metadata using AI analysis — including:

• 🎙 Full transcriptions
• 👤 Recognized faces
• 🎨 Dominant colors
• 📦 Detected objects
• 🔤 On-screen text (OCR)

This creates a fully searchable, offline-first video database, letting you find the exact shot you need in seconds.

Click to watch a walkthrough of Edit Mind's core features

When you add a video, Edit Mind runs a complete AI-powered local analysis pipeline:

1. 🎙 Full Transcription — Extracts and transcribes the audio track using a local OpenAI Whisper model for time-stamped dialogue.
2. 🎞 Scene Segmentation — Splits the video into 2-second “Scenes” for precise frame-level indexing.
3. 🧩 Deep Frame Analysis — Each Scene is analyzed by Python plugins to:
   • Recognize faces
   • Detect objects
   • Perform OCR (on-screen text)
   • Analyze colors and composition
4. 🧠 Data Consolidation — Aligns spoken text with visual content using timestamps.
5. 🔍 Vector Embedding & Storage — All extracted data (transcripts, tags, and metadata) are embedded using Google Text Embedding Models and stored locally in ChromaDB.
6. 🗣 Semantic Search Parsing — When you search in natural language (e.g. “show me all clips where Ilias looks happy”), Edit Mind uses Google Gemini 2.5 Pro to convert your search prompt into a structured JSON query.
   This query is then executed locally against the ChromaDB vector store to retrieve relevant scenes.

💡 Privacy by Design:
All video files, frames, and extracted metadata remain fully local.
The only cloud-based component is the Gemini API call for search prompt interpretation and Google text embedding generation — no raw video are ever uploaded.
In a future update, Edit Mind will include the option to use offline embedding and query models for completely disconnected operation.

• Advanced search filters (date range, camera type)
• Export rough cuts as an Adobe Premiere Pro and Final Cut Pro project
• Improved indexing performance

• New analysis plugins (e.g., audio event detection)
• Plugin documentation and examples

• Optional cloud sync for indexes
• Collaborative tagging and shared libraries
• Plugin marketplace

• Node.js v22+
• Python v3.9+
• Recommended Hardware: Multi-core CPU, modern GPU, and at least 8GB RAM.

# Clone the repo
git clone https://github.com/iliashad/edit-mind
cd edit-mind

npm install

cd python
python3.12 -m venv .venv                                                  
source .venv/bin/activate   # (macOS/Linux)
# .\.venv\Scripts\activate  # (Windows)
pip install -r requirements.txt
pip install chromadb
chroma run --host localhost --port 8000 --path .chroma_db

Create a .env file in the project root:

GEMINI_API_KEY=your_api_key_here

With the setup complete, you can start the application.

npm run start

To create a distributable package for your operating system, use the build command:

npm run build:mac

This will generate an installer or executable in the out/ directory, configured according to electron-builder.yml.

The project is organized to maintain a clear separation of concerns:

• app/: Contains all the React frontend code (pages, components, hooks, styles). This is the renderer process.
• lib/: Contains the core Electron application logic.
  • main/: The Electron main process entry point and core backend services.
  • preload/: The preload script for securely bridging the main and renderer processes.
  • conveyor/: A custom-built, type-safe IPC (Inter-Process Communication) system.
  • services/: Node.js services that orchestrate tasks like calling Python scripts.
• python/: Home to all Python scripts for AI/ML analysis, transcription, and more.
• resources/: Static assets that are not part of the web build, like the application icon.

To help you understand Edit Mind's resource requirements, here are real-world performance metrics from analyzing large video files.

• Hardware: M1 MacBook Max with 64 GB RAM
• Enabled Plugins:
  • ObjectDetectionPlugin
  • FaceRecognitionPlugin
  • ShotTypePlugin
  • EnvironmentPlugin
  • DominantColorPlugin

Note: The metrics below reflect frame analysis time and peak memory usage. Transcription and embedding score processing stages are not included in these measurements.

(Lower is better - 1.0× means processing takes the same time as video duration)

• Processing Speed: Approximately 2-3 hours of analysis time per hour of video content with all plugins enabled
• Memory Usage: Peak memory consumption ranges from 5-11 GB depending on video complexity and codec
• Codec Impact: HEVC videos show varied performance, likely due to differences in encoding parameters and scene complexity

💡 Performance Tips:

• Disable unused plugins to reduce processing time and memory usage
• Consider processing large files during off-hours
• Ensure sufficient RAM (16GB+ recommended for optimal performance)
• SSD storage significantly improves I/O performance during analysis

We welcome contributions of all kinds! Here are a few ways you can help:

• Reporting Bugs: If you find a bug, please open an issue.
• Improving the UI: Have ideas to make the interface better? We'd love to hear them.
• Creating a Plugin: The analysis pipeline is built on plugins. If you have an idea for a new analyzer (e.g., logo detection, audio event classification), this is a great place to start. Check out the existing plugins in the python/plugins/ directory to see how they work.

As an open-source project in its early stages, we are actively looking for contributors. Whether it's fixing bugs, adding new analysis plugins, or improving the UI, your help is invaluable.

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

This project was bootstrapped from the excellent guasam/electron-react-app template. It provided a solid foundation with a modern Electron, React, and Vite setup, which allowed us to focus on building the core features of Edit Mind.

While the core architecture is robust, the project is still in early development. Contributions are welcome in solving these key challenges to make the app production-ready.

1. Application Packaging & Distribution: The current setup is developer-focused. A major goal is to create a seamless, one-click installer for non-technical users. This involves bundling the Python environment, ML models, and all dependencies into the final Electron application for macOS, Windows, and Linux. Contributions in this area (e.g., using PyInstaller, managing model downloads) are highly welcome.

2. Performance on Consumer Hardware: The analysis pipeline is resource-intensive. While the code includes memory monitoring and optimizations, further work is needed to ensure smooth operation on a variety of consumer-grade machines. Key areas for improvement include:

   • Implementing a robust background queuing system for video processing.
   • Adding user-configurable "analysis levels" (e.g., "transcription only" vs. "full analysis").
   • Further optimization of the frame processing and ML inference steps.

3. Data Schema Evolution: As new plugins and features are added, the metadata schema for scenes will evolve. A long-term challenge is to implement a strategy for handling data migrations, allowing users to "upgrade" their existing indexed data to a new schema without having to re-index their entire library from scratch.

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