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NovaAI
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A language learning chatbot
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NovaAI
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# Nova AI   [](https://www.repostatus.org/#wip) <a href="https://www.loom.com/share/50b3de2c86054ed5a6115247818ab293"> <p>See a Loom Demo of NovaAI here</p> <img style="max-width:300px;" src="https://cdn.loom.com/sessions/thumbnails/50b3de2c86054ed5a6115247818ab293-with-play.gif"> </a> Try out Nova at: http://nova-ai.net/ (Udpate: no longer active) ## Overview Nova is an application that uses conversational AI for language learning to help users gain fluency in their target language (work in progress). Users can chat with Nova, practice specific scenarios, and track their learning progress. Features: - A language learning website implemented Flask - Chats with a conversational AI powered by GPT-3 - Grammar correction and other learning features ## Usage ### Installation The package uses `python3.9`. First clone the repo: ```bash git clone git@github.com:ConvAF/NovaAI.git cd chatbot ``` It is recommended to also set up and activate a virtual environment, for example using ```bash pip install virtualenv virtualenv venv source venv/bin/activate ``` Then, install the application and dependencies as a package in development mode using ```bash make install ``` which is equivalent to running ```bash pip install -e . ``` which installs the application as a package in developer (local edit) mode. ### Configuration Create a file `.env` at the project root (if it doesn't exist yet) and specify the following variables before running the application: Several variables are stored in the `.env` file and should be configured before running the application: - `OPENAI_API_KEY`: The api key for OpenAi. - `OPENAI_ENGINE`: Which GPT-3 engine to use. For testing, a simpler engine is used. Can be one of: ada (cheapest), babbage, curie, davinci (most expensive, most powerful) ### Running the application Before running the application, initialize the user database ```bash flask init-db ``` Start the application from the root project directory using ```bash flask run ``` If the application is installed, you can also start it from anywhere using ```bash export FLASK_APP=chatbot flask run ``` ## Deployment ### Deploying on AWS EC2 This describes how to deploy the app on an AWS EC2 instance using docker. First clone the repo: ```bash git clone git@github.com:ConvAF/NovaAI.git cd chatbot ``` Then build the image using ```bash docker build . --tag chatbot ``` To run the app (and keep running in background), start the container as ```bash docker run -d chatbot:latest ``` Be sure that the correct port (80 by default) is exposed on your instance such that the application is accessible from the outside (see notes below). # Developer Notes ### Useful commands - `flask run`: Run the application. - `flask init-db`: Initialize the database. - `flask shell`: Start an interactive Python shell in the application context, with an app instance imported ## Testing ### Running Unit Tests You can run tests using ```bash pytest ``` Add the `-v` flag to show the result for each test function. To measure the code coverage of the tests, use the `coverage` command: ```bash coverage run -m pytest ``` You can view the coverage report using ```bash coverage report # or coverage html ``` ### Testing in the Flask shell The flask application can also be tested from the shell. Start the shell with ```bash flask shell ``` In the shell, you can test endpoints like so: ```python >>> client = app.test_client() >>> client.get('/') <WrapperTestResponse streamed [200 OK]> >>> client.post( '/auth/register', data={'username': username, 'password': password} ) ``` ### Load testing with locust To test the application under user traffic, do ```bash cd tests/ locust ``` which will open a dashboard at `http://0.0.0.0:8089/`. There you can simulate users retrieving different endpoints. More complex fuctionality can be added in `tests/locustfile.py`. ### Profiling with py-spy To profile the app, first get the process id (`pid`) of the running flask app, for example using ```bash ps | grep flask ``` Then you can record the app's activity using ```bash py-spy record -o profile.svg --pid <pid> ``` You can interact with the app for a while (or run a `locust` load test), after a while stop the `py-spy` process, which will save a flame graph of the apps activity. ### Notes on manual deployment on AWS EC 2 The following outlines some steps if not using `docker` as described above #### Installing pytorch We encountered a bug when installing `pytorch` on an AWS EC2 instance, where the installation with `pip` did not finish. To circumvent this, use ```bash pip install --no-cache-dir torch ``` or, for all requirements, ```bash pip install -e . --no-cache-dir ``` #### Serving the app ```bash waitress-serve --call 'chatbot:create_app' ``` #### Ports on AWS EC2 waitress serves the app on port 8080. To redirect to port 80, use ```bash sudo iptables -t nat -I PREROUTING -p tcp --dport 80 -j REDIRECT --to-ports 8080 ``` ### Docker Docker image is specified in the docker file. Build the image using ```bash docker build . --tag chatbot ``` Start the container (and keep running in background) ```bash docker run -dp 80:80 chatbot:latest ``` For testing, run the container and delete after user with ```bash docker run -p 80:80 --rm chatbot:latest ``` (ports are not necessary on the server, but on mac you have to specify the ports `8080:80 ` because `80` is already in use). or in interactive mode to inspect the contents: ```bash docker run -it --rm chatbot:latest bash ```
