ML web app that can be deployed through Docker to analyze the sentiment of tweets. It uses Docker for the deployment and Streamlit for the web app.

The model used is a pre-trained model from Hugging Face. Concretely, it is a roBERTa model finetuned for sentiment analysis with the TweetEval benchmark. The model is available here.

This app expects 2 untracked files to be present in the project:

• .streamlit/secrets.toml with the following content:

password = "your_password"

This file is used to protect the app with a password. You can change the password to whatever you want or even deactivate the if check_password in app.py to simply avoid this step. I prefer to have a password to avoid random people accessing the app in case I'd choose to test it on the open.

• .env with the following content:

API_KEY=YOUR_API_KEY
API_KEY_SECRET=YOUR_API_KEY_SECRET
BEARER_TOKEN=YOUR_BEARER_TOKEN
ACCESS_TOKEN=YOUR_ACCESS_TOKEN
ACCESS_TOKEN_SECRET=YOUR_ACCESS_TOKEN_SECRET

This file is used to authenticate the app to the Twitter API. You can get these credentials by creating a Twitter developer account and creating a new app. You can find more information here.

In case you just want to run the app for analyzing single tweets instead of user accounts, I have created a second prompt that does not require the Twitter API credentials as it depends solely on the model and the text you input.

A docker-compose.yml file is provided to run the app. You can run it with:

docker-compose up

This should have created the webapp for you to explore and access it at https://localhost:8501.

Changes should be reflected in the web app automatically thanks to the volumes of the docker-compose.yml file.

To stop the app, you can use Ctrl+C or docker-compose down in case you are running it on daemon mode.

• Streamlit
• HuggingFace
• TweetEval
• Tweepy
• Twitter API