- DOMAIN Industrial safety. NLP based Chatbot.
- CONTEXT The database comes from one of the biggest industry in Brazil and in the world. It is an urgent need for industries/companies around the globe to understand why employees still suffer some injuries/accidents in plants. Sometimes they also die in such environment.
- DATA DESCRIPTION This The database is basically records of accidents from 12 different plants in 03 different countries which every line in the data is an occurrence of an accident.
- Data: timestamp or time/date information
- Countries: which country the accident occurred (anonymised)
- Local: the city where the manufacturing plant is located (anonymised)
- Industry sector: which sector the plant belongs to
- Accident level: from I to VI, it registers how severe was the accident (I means not severe but VI means very severe)
- Potential Accident Level: Depending on the Accident Level, the database also registers how severe the accident could have been (due to other factors involved in the accident)
- Genre: if the person is male of female
- Employee or Third Party: if the injured person is an employee or a third party
- Critical Risk: some description of the risk involved in the accident
- Description: Detailed description of how the accident happened.
Link to download the dataset: https://www.kaggle.com/ihmstefanini/industrial-safety-and-health-analytics-database
PROJECT OBJECTIVE
Design a ML/DL based chatbot utility which can help the professionals to highlight the safety risk as per the incident description.
docker compose build
docker compose up -d
- The front end chat bot is implemented with the vite-reactJS and dockerized for easy deployment
- The back end developed using the flask framework. Flask is used load the pickle file and expose the api which will be consumed by the frond end
- NLP Model is developed using the Google Colab and detailed explanation and the development steps is explained in the .ipynb file
- .ipynb File
- Report File