Christ College Chatbot

This is a Rasa-powered chatbot designed for Christ College to provide information about the institute, its courses, location, contact details, and more.

Project Structure

.
├── actions.py
├── config.yml
├── credentials.yml
├── data
│   ├── nlu.yml
│   ├── rules.yml
│   └── stories.yml
├── domain.yml
├── endpoints.yml
├── models
├── README.md
└── tests
    └── test_stories.yml

• actions.py: Contains custom actions for the bot.
• config.yml: Configuration for the Rasa pipeline and policies.
• credentials.yml: Credentials for connecting to messaging platforms.
• data/nlu.yml: Contains NLU training data.
• data/rules.yml: Contains rules for the bot's behavior.
• data/stories.yml: Contains training stories for the bot.
• domain.yml: Defines the bot's responses, intents, entities, slots, and actions.
• endpoints.yml: Configuration for connecting to custom actions.
• models: Directory where trained models are stored.
• tests/test_stories.yml: Test stories for verifying the bot's behavior.

Table of Contents

• Features
• Installation
• Usage
• Configuration
• Training the Model
• Running the Bot
• Connecting to Telegram
• License

Features

• Provides information about Christ College.
• Lists various courses offered.
• Provides location, contact number.

Installation

Prerequisites

• Python 3.7+
• pip
• Virtual environment (venv)
• Git

Steps

1. Clone the repository:

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Rasa:

   pip install rasa

Usage

1. Initialize Rasa:

   rasa init

2. Train the Rasa model:

   rasa train

3. Make changes in the NLU, domain, and stories files:

   • Edit data/nlu.yml for training examples.
   • Edit domain.yml for intents, responses, and actions.
   • Edit data/stories.yml for conversation flows.

4. Connect to ngrok on port 5005:

   ngrok http 5005

   Copy the HTTPS link provided by ngrok.

5. Configure Telegram:

   • Open credentials.yml and add your Telegram bot token and the ngrok HTTPS link.

   telegram:
     access_token: "YOUR_TELEGRAM_BOT_TOKEN"
     verify: "YOUR_TELEGRAM_BOT_NAME"
     webhook_url: "YOUR_NGROK_HTTPS_LINK/webhooks/telegram/webhook"

6. Train the Rasa model again:

   rasa train

Running the Bot

1. Run Rasa actions server:

   rasa run actions

2. Open a new terminal and run the Rasa server with API enabled and CORS support:

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

3. Run the chatbot on localhost 5005:

   rasa run -m models --enable-api --cors "*" --debug

Intents and Responses

Intents

• affirm
• bot_challenge
• deny
• goodbye
• greet
• location
• christ_course
• christ_intro
• mood_great
• mood_unhappy
• mathematics
• physics
• computerscience
• bba
• chemistry
• contact_number
• certifications
• first

Responses

The bot provides various responses to user queries, including course details, contact information, and more.

Example Conversations

User: Hi
Bot: Hi, thank you for contacting Christ College. How can I help you?

User: Tell me about your courses
Bot: We offer a variety of courses:

• BSC Mathematics
• BSC Physics
• BSC Computer Science
• BBA
• BSC Chemistry

User: Where is Christ College located?
Bot: CHRIST COLLEGE (AUTONOMOUS) IRINJALAKUDA, IRINJALAKUDA, THRISSUR, KERALA, 680121, INDIA

Custom Actions

Custom actions are implemented in actions.py. These actions can handle complex logic, integrate with external APIs, and provide dynamic responses.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.