FeedLoop

FeedLoop is a full-stack feedback management platform for teams, enabling managers and employees to exchange, request, and track feedback efficiently. It includes:

• FastAPI backend
• React frontend
• Dockerized setup for local development

---

1. Setup Instructions (Local Development)

Backend (Local)

1. Install Dependencies

cd backend
pip install -r requirements.txt

2. Configure Environment Variables

Create a .env file inside the backend/ directory:

MONGO_URI=mongodb://localhost:27017
DATABASE_NAME=feedback_db
SECRET_KEY=your_secret

3. Run the Backend Server

uvicorn app.main:app --reload

4. Initial Setup (Before Using Frontend)

Use Swagger UI or tools like Postman/Thunder Client:

• Open API Docs

  • Visit: http://localhost:8000/docs

• Register an Admin

  • Endpoint: POST /user/register
  • Sample Payload:

{
  "name": "ADMIN",
  "email": "[email protected]",
  "password": "admin123",
  "role": "admin"
}

• Bulk Register Users

  • Endpoint: POST /user/bulk-register?admin_email=[email protected]
  • Sample Payload:

[
  {
    "name": "Steve Rogers",
    "email": "[email protected]",
    "password": "avengers123",
    "role": "manager"
  },
  {
    "name": "Tony Stark",
    "email": "[email protected]",
    "password": "avengers123",
    "role": "manager"
  },
  {
    "name": "Natasha Romanoff",
    "email": "[email protected]",
    "password": "avengers123",
    "role": "employee"
  }
]

• Create a Team

  • Endpoint: POST /team/create?admin_email=[email protected]
  • Sample Payload:

{
  "manager_email": "[email protected]",
  "member_emails": ["[email protected]"]
}

Frontend

1. Install Dependencies

cd frontend
npm install

2. Start the Frontend

npm start

App will run at: http://localhost:3000

---

2. Optional: Docker Setup for Backend

Run Backend in Docker

cd backend
docker build .
docker compose up

Dockerfile (Backend)

FROM python:3

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt
RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY app /code/app
COPY .env /code/.env

EXPOSE 8000

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Docker Compose Configuration

This project uses Docker Compose to manage:

1. MongoDB

• Image: mongo
• Container Name: feedback_mongo
• Ports: 27017:27017
• Command: mongod --noauth (for development only)
• Volumes: mongo_data:/data/db

2. Backend

• Builds the image from Dockerfile
• Container Name: feedback_backend
• Ports: 8000:8000
• Environment Variables:

MONGO_URI=mongodb://mongo:27017
DATABASE_NAME=feedback_db

• Depends on: mongo (ensures MongoDB starts first)

Volumes Declaration

volumes:
  mongo_data:

---

3. Tech Stack and Design Decisions

Backend

• FastAPI
• MongoDB with Beanie ODM
• Pydantic
• Uvicorn
• CORS for cross-origin requests

Frontend

• React (CRA)
• Tailwind CSS
• Context API
• Fetch API

Design Decisions

• Roles: admin, manager, employee
• Supports anonymous and named feedback
• Managers get analytics and feedback dashboards
• Employees can request and acknowledge feedback
• Modular API structure: /user, /team, /feedback, /dashboard

---

4. Database Schema

User (users collection)

class UserDB(Document):
    name: str
    email: EmailStr
    password_hashed: str
    role: Literal["manager", "employee", "admin"]

Team (teams collection)

class TeamDB(Document):
    manager_email: EmailStr
    member_emails: List[EmailStr]
    created_at: datetime
    updated_at: datetime

Feedback (feedback collection)

class FeedbackDB(Document):
    created_by_email: str
    created_by_role: str
    is_anon: bool
    employee_email: str
    strengths: str
    areas_to_improve: str
    sentiment: Optional[Literal["positive", "negative", "neutral"]]
    tags: Optional[List[str]]
    status: Literal["requested", "draft", "submitted", "acknowledged"]
    requested_at: Optional[datetime]
    created_at: Optional[datetime]
    updated_at: Optional[datetime]
    acknowledged_at: Optional[datetime]

---

5. AI

Minimal use of AI tools (like ChatGPT) was made for:

• Refining documentation
• Clarifying code structure
• Improving naming consistency