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🏡 SafeHouse

SafeHouse is an AI-powered web application that helps users evaluate the safety, health, and livability of their homes using image-based analysis. It detects environmental hazards, explains risks in simple language, and provides actionable next steps like repairs, services, and essential products.


💡 Problem

Most people move into or live in spaces without understanding hidden risks such as:

  • Mold and moisture damage
  • Water leaks
  • Unsafe electrical wiring
  • Structural hazards
  • Poor living conditions affecting mental and physical health

Traditional inspections are expensive, slow, and not accessible to everyone.


🚀 Solution

SafeHouse turns any living space into a real-time AI safety check system.

Users can upload images of their home, and SafeHouse will:

  • Detect potential hazards using AI vision
  • Explain issues in simple, non-technical language
  • Recommend specific repairs needed
  • Suggest nearby repair shops (with best-rated options)
  • Provide essential product recommendations (e.g., Amazon items)
  • Highlight accessibility and neurodivergent-friendly concerns

🧠 Key Features

  • 🖼️ AI-based hazard detection from images
  • 🛠️ Repair recommendations for identified issues
  • 📍 Nearby repair shop discovery + ranking
  • 🛒 Essential home product suggestions
  • 🧠 Simple explanations of risks and fixes
  • ♿ Neurodivergent-friendly structured outputs
  • 🏠 Move-in safety insights for renters & students

⚙️ How it Works

  1. User uploads an image of a room or space
  2. Computer vision model detects possible hazards
  3. LLM layer translates detections into human-readable insights
  4. System generates:
    • Risk summary
    • Repair actions
    • Local service recommendations
    • Essential product suggestions
  5. Results are displayed in a clean, structured UI

🧰 Built With

Python, JavaScript, React, Next.js, Flask, FastAPI, OpenAI API, Computer Vision, OpenCV, REST APIs, Geo-location API, Web scraping, Amazon API, HTML, CSS, TailwindCSS


⚠️ Challenges

  • Converting visual detections into meaningful real-world advice
  • Mapping AI outputs to actionable repair categories
  • Ranking hazards by severity in a useful way
  • Integrating external services (shops + products)
  • Keeping results fast and user-friendly

🏆 Accomplishments

  • Built an end-to-end AI-powered safety system
  • Combined computer vision with LLM reasoning
  • Designed actionable outputs (not just detection)
  • Integrated real-world repair and product ecosystems
  • Focused on accessibility and cognitive clarity

📚 What we learned

  • How to combine vision models with language models effectively
  • Designing AI for real-world decision support
  • Importance of accessibility in AI systems
  • Turning raw predictions into human impact

🚀 What's next

  • 📱 Real-time camera scanning mode
  • 🧠 Improved hazard detection accuracy
  • 🗺️ Smarter geo-aware repair recommendations
  • 🏠 “Home Safety Score” for rentals and apartments
  • 🤝 Integration with rental platforms
  • 🧑‍⚕️ Environmental health risk predictions

🔗 Links


🌍 Vision

SafeHouse aims to make every home a safe, understandable, and health-aware space — turning housing from a blind risk into a guided, AI-powered decision.

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