🐛 BugBoard

AI-powered bug reporting and classification system. Users report bugs in natural language and the AI automatically classifies them by severity, module, reproduction steps, and suggested fix.

✨ Features

• Natural language bug reporting — users describe bugs the way they talk, no forms to fill
• AI classification — llama3.2 via Ollama classifies severity, module, reproduction steps and suggests a fix
• Real-time preview — the AI classifies the bug as you type, before you submit
• Automatic browser context — captures browser, OS, URL and screen resolution automatically
• Duplicate detection — detects similar bugs and links them to the original
• Dashboard — filter bugs by severity, module and status
• Metrics — charts showing bug trends by module, severity and timeline
• Embeddable widget — add BugBoard to any app with 2 lines of HTML

🛠️ Stack

Layer	Technology
Frontend	Angular 17+ + Tailwind CSS
Backend	Python + FastAPI
AI	Ollama + llama3.2 (runs locally)
Database	PostgreSQL
Containerization	Docker

🚀 Getting Started

Prerequisites

• Ollama installed and running
• Docker Desktop installed and running
• Node.js 18+ and npm
• Python 3.11+

1. Clone the repo

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

2. Start Ollama and pull the model

brew services start ollama
ollama pull llama3.2

3. Start PostgreSQL

docker run -d \
  --name bugboard-postgres \
  -e POSTGRES_DB=bugboard_db \
  -e POSTGRES_USER=bugboard \
  -e POSTGRES_PASSWORD=bugboard123 \
  -p 5432:5432 \
  postgres:16-alpine

4. Start the backend

cd backend
python3.11 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

Backend running at http://localhost:8000 API docs at http://localhost:8000/docs

5. Start the frontend

cd frontend/bugboard-app
npm install
ng serve

Frontend running at http://localhost:4200

📡 API Endpoints

Method	Endpoint	Description
POST	/api/bugs/	Report a bug — classifies with AI
POST	/api/bugs/preview	Real-time classification preview
GET	/api/bugs/	List all bugs with filters
GET	/api/bugs/:id	Get bug detail
PATCH	/api/bugs/:id/status	Update bug status
GET	/api/metrics/summary	Overall stats
GET	/api/metrics/by-severity	Bugs grouped by severity
GET	/api/metrics/by-module	Bugs grouped by module
GET	/api/metrics/timeline	Bug reports over time

🧩 Embeddable Widget

Add BugBoard to any web app with 2 lines:

<script src="https://your-bugboard-url.com/widget.js"></script>
<bug-report-widget app-id="your-app-name" />

The widget automatically captures:

• Browser and version
• Operating system
• Current URL
• Screen resolution

📁 Project Structure

bugboard/
├── backend/
│   ├── app/
│   │   ├── core/         # Config and settings
│   │   ├── database/     # SQLAlchemy connection
│   │   ├── models/       # PostgreSQL models
│   │   ├── routers/      # FastAPI endpoints
│   │   ├── schemas/      # Pydantic schemas
│   │   ├── services/     # Ollama integration
│   │   └── main.py
│   └── requirements.txt
└── frontend/
    └── bugboard-app/
        └── src/app/
            ├── core/         # Services and models
            └── features/
                ├── dashboard/
                ├── bug-detail/
                ├── bug-report/
                ├── metrics/
                └── widget/

🤖 How the AI works

1. User writes a bug in natural language
2. FastAPI sends the text to Ollama with a structured prompt
3. llama3.2 returns a JSON with severity, module, reproduction steps and suggested fix
4. The result is saved to PostgreSQL and shown in the dashboard

The prompt includes the available modules so the AI can classify correctly:

auth, payments, dashboard, profile, notifications, api, ui, database, other

👤 Author

Anderson Frias — @anndev

• YouTube: youtube.com/@anndev
• GitHub: github.com/anderj14

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Built with ❤️ using FastAPI, Angular, and llama3.2