chattr

It's a twitter sentiment thing.

Basically does this

1. Scrapes tweets using twint.
2. Passes the scraped tweets through roBERTa , (Hugging Face 🤗), this gives sentiment probabilities.
3. All of the above is served using FastAPI.

Other than that

• Front end is built using React, a bit of Material-UI.
• Data served by the API is plotted using Chart.js.

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Running It

Steps for getting the code to do what's in the gif.

API Server

Installing Dependencies

• Install the dependencies in api/requirements.txt
• For torch I suggest following the steps here.
• Also if twint doesn't install properly using pip, use pip3 install --upgrade -e git+https://github.com/twintproject/twint.git@origin/master#egg=twint, this worked for me.

Running the api server

• uvicorn api.main:app --reload from the root of this repo.

If Hugging Face throws an error then rename files in api/core/model to:

model
└── cardiffnlp
    └── twitter-roberta-base-sentiment
        ├── config.json
        └── pytorch_model.bin

and files in api/core/tokenizer to:

tokenizer
└── cardiffnlp
    └── twitter-roberta-base-sentiment
        ├── config.json
        ├── merges.txt
        ├── special_tokens_map.json
        └── vocab.json

Note: these files are downloaded when the server runs.

Frontend

• npm install to install the dependencies.
• npm start to serve the frontend.

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Few more things

• API server should be served from port http://localhost:5000 and frontend server from http://localhost:3000.
• Impact is calculated using likes, retweets, and replies.
• This was done without much thought, more of an exercise than a project, not meant to be serious so a more than a few things may be broken.
• Tweet scores aren't aggregated, it would be more meaningful if it was.