PhotoWalk AI — Turning Screen Time Into Real-World Exploration
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
I built PhotoWalk AI, a web application that encourages people to go outside and turn a normal walk into a photography challenge.
The idea came from something pretty simple: we spend a lot of time looking at photos on our phones, but not enough time actually going outside and creating our own experiences.
PhotoWalk AI gives users a reason to explore their surroundings.
Users can create a PhotoWalk and get creative photography missions such as finding interesting shadows, reflections, colors, or other things around them. They then go outside, complete the mission, and upload their best photographs.
I also built a feature called Color Hunt, where multiple teams can participate in the same photography competition. Each team gets its own target color, and every member of the team goes outside to find and photograph things matching that color.
After the hunt, participants submit their best photographs. The system can evaluate the photographs based on things like color matching, composition, creativity, and technical quality. Teams are then ranked on a leaderboard.
The project also includes winner selection, rewards, and digital participation certificates.
The main idea behind the project is simple:
Instead of using technology only to consume content, use it as a reason to go outside and experience the real world.
PhotoWalk AI is mainly aimed at students, photography enthusiasts, college clubs, friends, and anyone who wants a more creative reason to spend time outdoors.
Demo
The project is currently available as a local web application.
The source code and complete implementation are available on GitHub:
https://github.com/uditsharma45/photowalk-ai
The repository contains both the frontend and backend implementation.
Code
GitHub Repository:
https://github.com/uditsharma45/photowalk-ai
The frontend is built using React and Vite.
The backend is built using FastAPI and Python, with SQLite for storing application data and local storage for uploaded photographs.
The project includes:
- PhotoWalk mission creation
- Outdoor Walk Mode
- Photo uploads
- Multi-team Color Hunt
- Team and participant management
- AI-assisted photo evaluation
- Photo scoring
- Team leaderboard
- Winner selection
- Rewards
- Digital certificates
- Backend API
- Automated tests
The backend currently has 51 passing tests, and the frontend production build also completes successfully.
How I Built It
I built PhotoWalk AI as a full-stack web application rather than just a simple AI demo.
The frontend handles the user experience, including creating missions, joining Color Hunts, uploading photographs, viewing scores, and checking the leaderboard.
The FastAPI backend handles the application logic, database operations, photo uploads, competition state, scoring, rewards, and certificates.
For the AI part, I created a separate vision-analysis layer so that the application is not tightly coupled to the rest of the system.
The AI evaluation is designed to look at several aspects of a submitted photograph:
- How well it matches the target color
- Composition
- Creativity
- Technical quality
The final photo score is calculated by the backend using weighted scoring:
- Color matching: 40%
- Composition: 25%
- Creativity: 20%
- Technical quality: 15%
For team competitions, the system aggregates participant results and creates a leaderboard so that teams can compete against each other.
I also added automated tests for the backend and kept the project structured so that additional AI providers or models can be added later.
The current AI integration uses an OpenAI-compatible vision provider. I have not yet completed a live production AI test because the configured API returned a rate-limit response, so I am not claiming that the live AI evaluation has been fully tested.
Why Does Open Innovation Matter?
For me, open innovation matters because I don't want PhotoWalk AI to be limited to one fixed way of doing things.
The project is open source, so other developers can look at the code, modify the missions, change the scoring system, add new competition formats, or experiment with different AI models.
The AI evaluation system was also separated from the rest of the application so that different vision models and providers can be explored without rebuilding the entire project.
This is especially useful for a project like PhotoWalk AI because image evaluation can be approached in many different ways. Developers could experiment with different models for judging composition, color matching, creativity, or even detecting objects in photographs.
My longer-term goal is to support open-weight and local vision models as well, so that the AI component can be run in different environments instead of depending on a single provider.
My Agent Session
I used AI-assisted development during the development of PhotoWalk AI.
I used it to help with implementation, debugging, architecture, frontend/backend integration, and testing, while I directed the project and decided what features the application should have.
I don't currently have a DevRelay session link to include here.
Prize Categories
I am not entering any additional partner prize category.
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