ConnectIn

About

If you ever wanted though about having a 'coffee chat' with a cracked LinkedIn user but wouldn't know what to say, ConnectIn is for you!

Features

• AI-powered coffee chat question generation
• LinkedIn profile analysis
• Personalized networking conversation starters
• RESTful API backend
• Modern web interface

Technology Stack

Backend

• FastAPI
• Mistral 7B AI Model (via llama-cpp-python)
• Playwright for web scraping
• BeautifulSoup for HTML parsing

Frontend

• [frontend technologies here]

Developers

Backend Developer: Majock Bim

• API development
• AI model integration
• LinkedIn scraping implementation

Frontend Developer: Carson Carrasco

• [info here]

Getting Started

Prerequisites

• LinkedIn account credentials

Installation

1. Clone the repository:

git clone https://github.com/majockbim/connectin.git
cd connectin

2. Backend setup:

cd backend
pip install -r requirements.txt
python -m playwright install chromium

3. Environment configuration:

# Create .env file in backend directory
LINKEDIN_EMAIL=your_linkedin_email@example.com
LINKEDIN_PASSWORD=your_linkedin_password

4. Download the AI model:

   • Follow instructions in mistral_README.txt to install mistral-7b-instruct-v0.1.Q4_K_M.gguf in backend/app/models/

5. Start the backend server:

cd connectin
python -m backend.main

6. Frontend setup:

cd frontend
npm run dev

# frontend installation steps here

API Documentation

Once the backend is running, visit:

• API docs: http://localhost:8000/docs
• Health check: http://localhost:8000

Main Endpoint

POST /generate
Content-Type: application/json

{
  "learner_url": "https://linkedin.com/in/student-profile",
  "mentor_url": "https://linkedin.com/in/mentor-profile"
}

Project Structure

connectin/
├── backend/
│   ├── app/
│   ├── prompt_engine.py
│   │   ├── models/
│   │   │   └── mistral-7b-instruct-v0.1.Q4_K_M.gguf
│   │   └── utils/
│   │       └── scraper.py
│   ├── main.py
│   └── requirements.txt
│
├── frontend/
│   └── [Frontend files]
└── README.md

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

Acknowledgments

• Mistral AI for the language model
• LinkedIn for profile data
• FastAPI and Playwright communities

Honourable Mention: Junior Assani for creating the first (and only) pull request.