🎓 CampusConnect – Admission Enquiry Rasabot

An AI-powered chatbot built using the Rasa Framework to assist with college admission-related enquiries. The bot answers queries on admission processes, courses, fees, scholarships, hostel facilities, placements, and more. It includes a simple HTML/CSS/JavaScript frontend for easy web integration.

📌 Features

• Answers admission-related questions:

  • Course & department details
  • CET, COMED-K, Management Quota, NRI admissions
  • Fee structure & scholarship information
  • Hostel facilities, timings, and rules
  • Placement procedure & statistics
  • College location, infrastructure, and clubs

• REST API support for easy integration with other platforms

• Works with static website frontend or can be embedded into existing portals

• Extensible with more intents, entities, and custom actions

🛠 Tech Stack

• Python 3.8+
• Rasa Framework (NLU + Core)
• Anaconda (for environment management)
• HTML/CSS/JavaScript (Frontend UI)
• REST API for backend-frontend communication

⚙️ Installation & Setup

1️⃣ Create a Conda Environment

conda create -n clgbot python=3.8

2️⃣ Activate the Environment

conda activate clgbot

3️⃣ Install Rasa

pip install rasa

(Or use requirements.txt after cloning the project.)

4️⃣ Clone the Repository

git clone https://github.com/<your-username>/CampusConnect.git
cd CampusConnect

5️⃣ Install Project Dependencies

pip install -r requirements.txt

6️⃣ Verify Rasa Installation

rasa --version

🚀 Running the Chatbot

1️⃣ Train the Model

rasa train

2️⃣ Start Rasa Server

rasa run --enable-api --cors "*" --port 5005

3️⃣ (Optional) Start Actions Server

rasa run actions

4️⃣ Launch the Frontend

Navigate to the Frontend folder and run:

python -m http.server 8000

Then open:

http://localhost:8000

📂 Project Structure

COLLEGE_ENQUIRY_BOT/
│
├── .rasa/                 # Rasa project files
├── .vscode/               # VSCode settings
├── actions/               # Custom actions (Python)
├── chatbot-frontend/      # Alternate chatbot HTML UI
│   └── index.html
├── data/                  # NLU & Stories training data
├── Frontend/              # Main web frontend
│   ├── images/            # Static images
│   ├── index.html         # Chatbot UI
│   └── styles.css         # Frontend styling
├── models/                # Trained Rasa models
├── results/               # Output results/logs
├── tests/                 # Test cases
├── config.yml             # Rasa pipeline & policies
├── credentials.yml        # Channel credentials
├── domain.yml             # Intents, entities, responses
├── endpoints.yml          # Action endpoints
├── start.bat              # Startup script for Windows
└── stepstofollow.txt      # Step-by-step usage guide

📦 Dependencies

• Python 3.8+
• Rasa
• HTML, CSS, JavaScript
• (Frontend enhancements can include React, UUID, React Icons, Tailwind CSS if migrating to React UI)

📜 License

This project is licensed under the MIT License. You can find a copy of the license here.

🤝 Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m 'Add some feature'

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

Please make sure to update tests as appropriate.

📬 Contact

For any inquiries, suggestions, or feedback, please contact me at:

• Email: evitabarboza195@gmail.com
• GitHub: Evita Barboza