construction-safety-inspector

# Construction Site Safety Inspector

An LLM-powered system for automated construction site hazard detection and incident analysis.

## Overview

This system analyzes construction site photos and incident PDF reports to detect safety hazards, retrieve similar past accidents from a KOSHA database, and generate professional bilingual safety reports with citations.

Built as a final project for LLM-AE-AI course at Kyung Hee University.

## Features

- Vision Hazard Detection: Claude analyzes site photos and identifies safety violations with severity levels

- PDF Incident Analysis: Extracts and analyzes construction accident report PDFs

- RAG Pipeline: Searches 37 real KOSHA accident cases using hybrid BM25 and VoyageAI search

- Tool Use: classify_hazard() function assigns hazard type and KOSHA regulation codes

- Bilingual Reports: Professional safety reports in Korean and English with citations

- Urgent Prevention Alerts: Automatically fires alerts when HIGH severity hazards are detected

- 2D Hazard Visualization: Draws colored bounding boxes on site photos

- YOLO vs Claude Comparison: Side by side comparison showing why Claude beats YOLO

- Weekly Safety Summary: Management level weekly report of all inspections and alerts

- MCP Server: Exposes all tools via FastMCP for Claude Desktop integration

- PDF Report Generation: Professional PDF reports with metrics, images, and hazard cards

## Technology Stack

W1 - Prompt Engineering: Domain safety inspection system prompt

W2 - Claude API: Core backend for all AI operations

W3 - LLM-as-Judge: Evaluates report quality automatically

W4 - Tool Use: classify_hazard() function

W5 - RAG Pipeline: VoyageAI + BM25 + RRF on KOSHA PDFs

W6 - Vision, PDF, Citations, Caching: Photo analysis, document reading, cited output

W7 - MCP Server via FastMCP: Claude Desktop integration

## Project Structure

safety-inspector/

  app.py Streamlit web interface

  inspector.py Core AI pipeline

  rag_builder.py Builds RAG index from KOSHA PDFs

  yolo_compare.py YOLO vs Claude comparison

  mcp_server.py MCP server

  pdf_generator.py PDF report generation

  data/pdfs/ KOSHA accident PDFs

  outputs/ Generated reports and alerts

## Setup

1. Clone the repository

2. Create virtual environment: python -m venv venv

3. Activate: venv\Scripts\activate

4. Install dependencies: pip install -r requirements.txt

5. Create .env file with your API keys:

  ANTHROPIC_API_KEY=your_key_here

  VOYAGE_API_KEY=your_key_here

6. Download KOSHA PDFs into data/pdfs/

7. Build RAG index: python rag_builder.py

8. Run the app: streamlit run app.py

## Demo

Streamlit Web App:

streamlit run app.py

MCP Server:

npx @modelcontextprotocol/inspector python mcp_server.py

## Data Source

KOSHA construction accident case reports:

https://portal.kosha.or.kr

## Developer

Muhammad Ali

Student ID: 2026311007

Course: LLM-AE-AI

Professor: 백장운

Kyung Hee University

Graduate School of Architecture Engineering