Goal: Detect the language of a given text input using a trained machine learning model.
Technologies Used:
Scikit-learn: For training the machine learning model.
FastAPI: For building a REST API to serve predictions.
Pickle: For saving and loading the trained machine learning model.
Docker: For containerizing the FastAPI app.
Heroku: For deploying the API to the cloud.
Build and Train the Model:
Use scikit-learn to preprocess the text data and train a Naive Bayes classifier for language detection. Save the trained pipeline to a file using pickle. Create the FastAPI Backend:
Develop a REST API that accepts a text input and returns the predicted language. Deploy the API:
Use Docker to containerize the FastAPI app. Deploy the containerized app to Heroku for public access.
Screenshots:
