Rasa Admissionbot

This chatbot is implemented using the Rasa framework, along with a frontend interface created using HTML, CSS, and JavaScript.

Project Structure

The project is organized into the following directories and files:

actions/: Contains custom action files for the chatbot.
data/: Includes training data for the NLU (Natural Language Understanding) model.
frontends/: Contains the frontend files for the user interface.
tests/: Contains test data for chatbot stories.
config.yml: Configuration file for Rasa NLU and Core models.
credentials.yml: Contains credentials for external services (if any).
domain.yml: Defines the chatbot's domain and actions.
endpoints.yml: Contains configuration for connecting to external services.
graph.html: HTML file for a graphical representation of chatbot interactions.
runrasa.sh: Shell script to run the Rasa server.
Dockerfile: Dockerfile for building the Rasa server container.
frontends/Dockerfile: Dockerfile for building the frontend container.
docker-compose.yml: Docker Compose file to run both the Rasa server and frontend.

Configuring the containers on the Azure VM

Create an Azure Linux VM. Use the configurations below:
- Open port 22 for ssh and port 80 to access the frontend
- Leave the other information in default and create the VM
- When the creation is done, click the network settings section and configure the outbound ports using this template

Running the containers on the VM

ssh into the virtual machine.
Clone this repository the virtual machine. git clone https://github.com/MESHEmugles/admissionbot
Make sure you have Docker and Docker compose installed.
Open a terminal and navigate to the project directory.

Running the Chatbot and Frontend

Change the value of server_IP in /frontends/static/js/components/chat.js:
- When testing locally, use localhost
- When hosted, use the IP of the server.
Run the docker compose commands to build the images and run the containers

# Make the models executable

chmod a+rwx models/

# Build the rasa and action_server containers
docker compose build [service_name] –-no-cache

# Start the containers
docker compose up -d --force-recreate

This will start both the Rasa server and the frontend in the same container. The Rasa server will be available at `http://{server_IP}:5005`, and the frontend will be available at `http://{server_IP}`.

Accessing the Chatbot

Open a web browser and navigate to http://{server_IP} to access the chatbot interface. You can interact with the chatbot by sending messages in the chat interface.

Customization

Customize the chatbot's behavior by editing the data/nlu.yml and data/stories.yml files.
Add new custom actions or modify existing ones in the actions/actions.py file.

Credits

This project is based on the Rasa framework and includes a simple frontend interface.

License

This project is licensed under the MIT License.

Feel free to customize the README to match your project's specific details and requirements.

CFCIfe/admissionbot