Building a Virtual Mental Health Chatbot Solution with ChatGPT
This story was originally published on the Neurons Lab blog on Jan 23, 2023
In today’s fast-paced world, mental health has become a critical issue. Stress, anxiety, and depression are all on the rise, and many people struggle to cope with these challenges. But there is hope. With the help of cutting-edge technologies like artificial intelligence (AI) and machine learning (ML), businesses can now build powerful solutions that can help to support and improve mental health.
One such solution is a virtual psychologist chatbot. This type of application can provide users with a safe and confidential space to talk about their mental health concerns and receive support and guidance from a trained professional. By leveraging the power of AI and ML, businesses can build virtual psychologist chatbot solutions that are highly effective and easily accessible to anyone with an internet connection.
One tool that can help businesses to build a virtual psychologist chatbot is ChatGPT. ChatGPT is a powerful language model that has been trained on a large dataset of human conversation and is able to generate human-like responses to user input. This makes ChatGPT an ideal choice for building a chatbot that can provide support and guidance to users seeking help with their mental health.
Nevertheless, building a chatbot solution can be challenging, so it is essential to have the right tools at your disposal. One tool that can help with the prototyping process is Streamlit. The tool is an open-source library that allows developers to quickly and easily build interactive, web-based applications.
This means businesses can use Streamlit to quickly and easily prototype their virtual psychologist chatbot solution, testing out different features and functionality until they find the perfect balance.
You can access the code for our solution on GitHub at the following link: GitHub repo.
Feel free to look at the code and consider using it to build your own mental health support solution. If you have any questions or need assistance, don’t hesitate to reach out to Neurons Lab.
Run Docker Container Locally
To build the Docker image for this project, use the Dockerfile in the ./app directory. You can do this using the docker build command.
For example, to build the image using the Dockerfile in the current directory, you can use the following command:
docker build -t chatgpt-streamlit .
This will build the image and tag it with the name chatgpt-streamlit.
To run the Docker container locally, you will need to have Docker installed on your machine. You can then use the docker run command to start the container.
Here is an example of how to run the container locally:
docker run -p 8501:8501 -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-e AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN \
-e AWS_DEFAULT_REGION=”us-east-1″ \
This will start the container and bind the container’s port 8501 to the host’s port 8501. You will also need to set the appropriate AWS access keys and session token as environment variables. The container will use the us-east-1 region as the default region.
Once the container is running, you can access the Streamlit application by visiting http://localhost:8501 in your web browser.
The Operationalization in the Cloud
Finally, after the prototype is ready, it’s important to consider how to deploy your solution to a production environment. AWS ECS clusters with CloudFormation are one way of deploying a solution. This allows businesses to take advantage of the scalability and reliability of the AWS cloud, while also making it easy to manage the entire lifecycle of their application.
AWS CloudFormation automates the creation and management of your ECS cluster. With CloudFormation, you can define your infrastructure as code, which makes it easy to deploy and update your solution as needed.
To deploy the CloudFormation stack for this project, use the ./cf.yaml file. You can do this using the AWS Management Console, the AWS CLI, or the AWS SDKs.
For example, to deploy the stack using the AWS CLI, you can use the following command with default values:
aws cloudformation create-stack \
– stack-name chatgpt-streamlit \
– template-body file://cf.yaml \
– capabilities CAPABILITY_IAM
In summary, if you’re a business looking to extend your products with AI/ML features, consider building a virtual psychologist chatbot using ChatGPT and Streamlit. With the right tools and a little bit of know-how, you can create a solution that helps to provide mental health support to those who need it most. And with AWS ECS and CloudFormation, you can deploy the solution in a production-ready environment.
If you are interested in receiving a personalized consultation, contact our team right now. We can help you with prototyping and deploying your own chatbot assistant.