Bridge Bridge is a Cross-Platform Application designed to enable seamless interaction with open-source Vision Models and Large Language Models (LLMs). The application is built with performance and efficiency in mind, leveraging the power of Rust to deliver a robust interface for advanced AI and image analysis.

Features

• Model Integration: Supports various open-source Vision and Large Language Models that can be easily downloaded using Ollama.
• Cross-Platform Compatibility: Designed to work on multiple platforms with consistent performance.
• Efficient Image Processing: Users can upload images and interact with AI models to perform image analysis.
• Intuitive User Interface: Built using Vite and React to provide a responsive and user-friendly experience.
• Tauri Integration: Utilizes Tauri for building lightweight, secure desktop applications with Rust.

Tech Stack

• Rust: The core application is compiled using Rust, ensuring high performance and reliability.
• Vite: Fast and modern front-end build tool, providing an optimized development experience.
• React: A JavaScript library for building user interfaces, used for creating the front-end of the application.
• Tauri: A framework for building desktop applications using web technologies and Rust, ensuring cross-platform support.

Installation and Setup

Prerequisites

Before you start, ensure you have the following installed:

• Node.js (for Vite and React)
• Tauri CLI
• Rust (optional, if you want to contribute to the Rust backend)

Clone the Repository

git clone https://github.com/yourusername/bridge.git
cd bridge

Install Dependencies

Frontend (Vite & React)

cd frontend
npm install

Running the Application

Development Mode

To run the application in development mode, use the following command:

npm run tauri dev

Build for Production

To build the application for production, use:

npm run tauri build

This will compile the application into an executable that can be run on your platform.

Usage

1. Upload an Image: Use the image upload feature to send images for analysis.
2. Select a Model: Choose an open-source Vision Model or LLM to interact with.
3. Submit Queries: Ask questions or interact with the selected model using the intuitive interface.
4. View Results: The processed results will be displayed in the interface.