🧗‍♂️ CLaiMB

An intelligent, AI-powered mobile application designed to help rock climbers analyze routes, estimate grades, and receive personalized coaching simply by taking a photo of the climbing wall.

📖 Overview

ClAImb is built for climbers who want to push their limits. By leveraging advanced Vision AI (Roboflow), the app analyzes user-uploaded photos of climbing walls to detect holds, suggest route grades, and act as a digital climbing coach.

The system uses a modern Service-Oriented Architecture (SOA) with a React Native frontend and a scalable FastAPI backend running asynchronous background workers.

✨ Key Features

• Intelligent Route Analysis: Snap a photo of a wall, and the AI will analyze the hold types, wall angle, and route path.
• Automated Grade Detection: Get an estimated difficulty grade for unmarked or newly set gym routes.
• AI Beta & Coaching: Receive smart tips on how to approach the route based on the visual layout.
• Climbing History: Track your analyzed routes, past grades, and personal progression over time.

🛠 Tech Stack

Frontend:

• React Native (Expo)
• TypeScript

Backend:

• Python 3.x
• FastAPI (Service-Oriented Architecture)
• Celery & Redis (Asynchronous background task processing)
• AI/ML: Google Gemini / Roboflow integration

📂 Project Architecture

The repository is divided into two main environments:

├── frontend/                 # React Native Expo application
│   ├── app/                  # Expo Router file-based navigation
│   ├── components/           # Reusable UI components
│   └── utils/                # State management and API fetching
│
└── backend/                  # FastAPI Python application
    ├── routes/               # API endpoint definitions
    ├── services/             # Core business logic (Vision, Grading)
    ├── models/               # Database schemas and Pydantic models
    ├── tasks.py & worker.py  # Asynchronous job processing (Celery)
    └── server.py             # FastAPI application entry point

👁️ Overview

🚀 Getting Started

Prerequisites

• Node.js & npm (or yarn/bun)
• Python 3.9+
• Expo Go app installed on your mobile device
• API Keys for the Vision services (set in .env)

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

Start the FastAPI server

$ uvicorn server:app --reload

In a separate terminal, start the background worker

$ celery -A worker worker --loglevel=info

Frontend Setup

cd frontend
npm install

Start the Expo development server

npx expo start

Scan the QR code generated in the terminal using the Expo Go app on your phone to view the app.