Create a machine learning powered web app to answer questions
Use the Model Asset eXchange Question Answering Model to answer typed-in questions
In this code pattern, learn how to build a chatbot that answers a user’s questions by finding the answer in a college biology textbook.
Ever found yourself wondering what mitochondria are? Perhaps you are curious about how neurons communicate with each other? A Google search works well to answer your questions, but what about something still digestible, but more precise? This code pattern shows you how to build a chatbot that answers a user’s questions by finding the answer in a college biology textbook. In the pattern, the textbook used is Biology 2e by Mary Ann Clark, Matthew Douglas, and Jung Choi.
The web app uses the Model Asset eXchange (MAX) Question Answering Model to answer questions that are typed in by the user. The web application provides a chat-like interface that lets users type in questions, which are then sent to a Flask Python server. The back end sends the question and related body of text from the textbook to a REST endpoint exposed by the MAX model, which returns an answer to the question, displayed as a response from the chatbot. The model’s REST endpoint is set up using the Docker image provided on MAX.
When you have completed this code pattern, you understand how to:
- Build a Docker image of the Model Asset eXchange (MAX) Question Answering model
- Deploy a deep learning model with a REST endpoint
- Generate answers to questions using the MAX Model’s REST API
- Run a web application that using the model’s REST API
Find the detailed steps for this pattern in the README file. The steps show you how to:
- Deploy the model.
- Build the web app.
- Clone the repository.
- Install the dependencies.
- Start the server.