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Merge pull request #42 from animeshchaudhri/main
Docs: Rust backend
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‎README.md

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# Contents
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1. [`Backend`](#Backend)
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2. [`Project Description (PictoPy)`](#PictoPy)
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# PictoPy
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# Backend
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## Steps to run backend
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PictoPy is an advanced desktop gallery application that combines the power of Tauri, React, and Rust for the frontend with a Python backend for sophisticated image analysis and management.
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1. Head over to `/backend` after cloning
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2. Create a virtual env by running: `python3 -m venv venv`
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3. Activate the env by running `source venv/bin/activate` and install dependancies via `pip install requirments.txt`
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4. Start the server via `./run.sh` (for development mode add the flag `--test` [RECOMMENDED])
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5. The server should now be hosted on port `8000` on localhost.
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## Architecture
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## Some pointers for the backend
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### Frontend
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- **Tauri**: Enables building the desktop application
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- **React**: Used for creating the user interface
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- **Rust**: Powers the backend, which the frontend communicates with through Tauri's API
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- Entry point is `main.py` which calls the routes, currently there is a `/test` route for developing, to test an image, send a request to the backend (`localhost:8000/test/return`) with the following Body format:
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```
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{
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"path": "<Path to image>"
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}
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### Backend (Python)
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- **FastAPI**: Serves as the API framework
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- **SQLite**: Database for storing metadata and embeddings
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- **YOLO**: Used for object detection
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- **FaceNet**: Generates face embeddings
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- **ONNX Runtime**: Runs the models efficiently
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- **DBSCAN**: Performs clustering for face embeddings
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### Backend (Rust via Tauri)
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Handles file system operations and provides a secure bridge between the frontend and local system.
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## Features
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- Smart tagging of photos based on detected objects, faces, and their recognition
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- Traditional gallery features of album management
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- Advanced image analysis with object detection and facial recognition
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- Privacy-focused design with offline functionality
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- Efficient data handling and parallel processing
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- Smart search and retrieval
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- Cross-platform compatibility
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## Technical Stack
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| Component | Technology |
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| --------- | ---------- |
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| Frontend | React |
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| Desktop Framework | Tauri |
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| Rust Backend | Rust |
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| Python Backend | Python |
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| Database | SQLite |
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| Image Processing | OpenCV, ONNX Runtime |
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| Object Detection | YOLOv8 |
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| Face Recognition | FaceNet |
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| API Framework | FastAPI |
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| State Management | React Hooks |
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| Styling | Tailwind CSS |
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| Routing | React Router |
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| UI Components | Radix UI |
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| Build Tool | Vite |
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| Type Checking | TypeScript |
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## Setup
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### Frontend Setup
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#### Prerequisites
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- Node.js (LTS version recommended)
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- npm (comes with Node.js)
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- Rust (latest stable version)
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- Tauri CLI
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#### Installation
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1. Navigate to the frontend directory:
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```bash
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cd frontend
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```
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2. Install dependencies:
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```bash
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npm install
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```
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#### Running the Application
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```bash
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npm run tauri dev
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```
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Eg: try it out with path as`backend/tests/inputs/zidane.jpg`
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- A window should pop up showing the highlighted detections, simply press `q` to exit, or comment out the `cv2` lines in `test.py`
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- Change the type of inference by changing `model_path` variable in `app/test` route, by default there are two models available in the `app/models` directory (face and object deteciton).
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# PictoPy
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An open source image sorting tool designed to organize your image collection efficiently.
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#### Building for Production
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```bash
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npm run tauri build
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```
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## Features to be Implemented
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### Python Backend Setup
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1. **A dedicated Local Database:**
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- A dedicated local database for caching tags, significantly improving the speed and efficiency of image sorting.
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#### Installation
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1. Navigate to the backend directory:
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```bash
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cd backend
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```
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2. Set up a virtual environment (recommended)
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3. Install requirements:
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```bash
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pip install -r requirements.txt
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```
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2. **Image Recognition Library:**
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- Uses an advanced image recognition library for evaluating objects in images along with faces, ensuring a smoother and more accurate sorting process.
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#### Running the Backend
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For UNIX-based systems:
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```bash
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./run.sh --test
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```
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3. **Improved UI/UX:**
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- A smooth user interface for a more intuitive and pleasant experience.
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The backend should now be running on port 8000 by default.
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4. **Multiprocessing Support:**
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- Pictopy should supports multiprocessing, optimizing the sorting process and improving overall performance.
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## Additional Resources
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- [Tauri Documentation](https://tauri.app/v1/guides/)
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- [React Documentation](https://reactjs.org/docs/getting-started.html)
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- [FastAPI Documentation](https://fastapi.tiangolo.com/)
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## Troubleshooting
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If you encounter any issues, please check the respective documentation for Tauri, React, and FastAPI. For persistent problems, feel free to open an issue in the project repository.

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‎docs/backend/setup.md renamed to ‎docs/backend/backend_python/setup.md

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# Backend Setup
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# Python Backend Setup
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## Setup Directory
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!!! note "Base Directory"
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All commands executed below are with respect to the `backend/` directory
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All commands executed below are with respect to the `backend/` directory
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### Installing requirments
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We suggest setting up a virtual environment and run the following command
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```bash
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pip install -r requirements.txt
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```
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`.bat` and `.sh` formats.
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!!! note "UNIX Development"
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For UNIX based systems, to run in development mode run
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```bash
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For UNIX based systems, to run in development mode run
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`bash
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./run.sh --test
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```
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`
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The backend should now be successfully running on port 8000 by default. To change this modify the start-up scripts.

‎docs/backend/backend_rust/api.md

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## API Documentation
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The Rust backend provides the following commands that can be invoked from the frontend:
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### 1. get_folders_with_images
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- **Description**: Retrieves folders containing images from a specified directory.
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- **Parameters**:
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- `directory`: String
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- **Returns**: `Vec<PathBuf>`
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### 2. get_images_in_folder
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- **Description**: Gets all images in a specific folder.
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- **Parameters**:
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- `folder_path`: String
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- **Returns**: `Vec<PathBuf>`
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### 3. get_all_images_with_cache
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- **Description**: Retrieves all images, organized by year and month, with caching.
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- **Parameters**:
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- `directory`: String
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- **Returns**: `Result<HashMap<u32, HashMap<u32, Vec<String>>>, String>`
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### 4. get_all_videos_with_cache
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- **Description**: Retrieves all videos, organized by year and month, with caching.
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- **Parameters**:
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- `directory`: String
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- **Returns**: `Result<HashMap<u32, HashMap<u32, Vec<String>>>, String>`
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### 5. delete_cache
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- **Description**: Deletes all cached data.
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- **Parameters**: None
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- **Returns**: `bool`
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## Usage Example
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```javascript
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// In your frontend JavaScript/TypeScript code:
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import { invoke } from '@tauri-apps/api/tauri';
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// Example: Get all images with cache
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const imagesData = await invoke('get_all_images_with_cache', { directory: '/path/to/images' });
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// Example: Delete cache
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const cacheDeleted = await invoke('delete_cache');
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```
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## Key Components
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- **FileService**: Handles file system operations for images and videos.
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- **CacheService**: Manages caching of folders, images, and videos.
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- **FileRepository**: Interacts directly with the file system to retrieve file information.
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- **CacheRepository**: Handles reading from and writing to cache files.
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This backend architecture provides efficient file management and caching capabilities, enhancing the performance of image and video retrieval operations in the Tauri application.

‎docs/backend/backend_rust/setup.md

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# Rust Backend Setup
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## Prerequisites
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Before setting up the frontend, ensure you have the following installed:
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- [Node.js](https://nodejs.org/) (LTS version recommended)
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- npm (comes with Node.js)
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- [Rust](https://www.rust-lang.org/tools/install) (latest stable version)
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- [Tauri CLI](https://v2.tauri.app/start/prerequisites/)
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For a comprehensive guide on prerequisites, refer to the [Tauri Prerequisites](https://v2.tauri.app/start/prerequisites/) documentation.
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## Setup Directory
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!!! note "Base Directory"
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All commands executed below are with respect to the `frontend/` directory
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### Installing Dependencies
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1. Navigate to the frontend directory:
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```bash
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cd frontend
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```
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2. Install the project dependencies:
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```bash
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npm install
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```
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For more information on npm commands, see the [npm documentation](https://docs.npmjs.com/).
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## Running the Application
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To start the Tauri application in development mode, run:
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```bash
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npm run tauri dev
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```
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This command will:
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- Start the [Vite](https://vitejs.dev/) development server for the frontend
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- Compile the Rust backend
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- Launch the Tauri application window
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For more details on Tauri commands, check the [Tauri CLI documentation](https://tauri.app/v1/api/cli).
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!!! note "First Run"
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The first run might take longer as it needs to compile the Rust code.

‎docs/index.md

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<img src="assets/AOSSIE-logo.png" alt="AOSSIE Logo" style="display:flex; margin:0 auto; justify-content: center;">
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</div>
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This project was announced by [AOSSIE](https://aossie.org/), an umbrella organization and was to be implemented from scratch. It provides features such as object detection and face similarity,
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offering smart tagging capabilities for photos based on objects, faces.
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This project was announced by [AOSSIE](https://aossie.org/), an umbrella organization and was to be implemented from scratch. It provides features such as object detection and face similarity, offering smart tagging capabilities for photos based on objects, faces.
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<div style="display:flex; margin:0 auto; justify-content: center;">
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<div style="width:33%">
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<div style="width:25%">
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<h2>Overview</h2>
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<ul>
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<li><a href='./overview/features'>
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</li>
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</ul>
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</div>
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<div style="width:33%">
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<h2>Backend</h2>
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<div style="width:25%">
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<h2>Backend Python</h2>
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<ul>
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<li>
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<a href="./backend/setup">
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<a href="./backend/backend_python/setup">
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Setup
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</a>
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</li>
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<li>
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<a href="./backend/database">
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<a href="./backend/backend_python/database">
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Database
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</a>
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</li>
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<li>
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<a href="./backend/directory-structure">
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<a href="./backend/backend_python/directory-structure">
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Directory Structure
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</a>
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</li>
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<li>
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<a href="./backend/api">
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<a href="./backend/backend_python/api">
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API
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</a>
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</li>
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<li>
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<a href="./backend/image-processing">
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<a href="./backend/backend_python/image-processing">
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Image Processing
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</a>
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</li>
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</ul>
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</div>
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<div style="width:33%">
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<div style="width:25%">
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<h2>Backend Rust</h2>
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<ul>
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<li>
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<a href="./backend/backend_rust/setup">
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Setup
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</a>
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</li>
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<li>
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<a href="./backend/backend_rust/api">
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API
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</a>
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</li>
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</ul>
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</div>
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<div style="width:25%">
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<h2>Frontend</h2>
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</li>
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</ul>
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</div>
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</div>
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</div>

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