Machine-Learning-Model-Deployment-using-FastAPI-and-Streamlit

This is a sample project demonstrating how to deploy machine learning models using FastAPI and Streamlit. The project contains a web interface built using Streamlit where users can input data and get predictions from different machine learning models. The predictions are made by a FastAPI app which serves the machine learning models as APIs.

Project structure

The project has the following structure:

│   ├── api 
│   │   ├── model1.py 
│   │   ├── model2.py 
│   │   ├── model3.py 
│   ├── main.py 
├── models 
│   ├── model1.pkl 
│   ├── model2.pkl 
│   ├── model3.pkl 
├── streamlit_app 
│   ├── main.py 
├── README.md 

*app/api: Contains the FastAPI app and the code for each machine learning model API.\n

*app/main.py: Contains the FastAPI app startup and shutdown events.\n

*models: Contains the trained machine learning models in pickle format.

*streamlit_app: Contains the Streamlit web interface for the project.

*.gitignore: Specifies which files should be ignored by Git.

*README.md: This file.

Usage

To run the project, first install the dependencies:

pip install -r requirements.txt

Then, start the FastAPI app:

uvicorn main:app --reload

The API documentation can be accessed at http://localhost:8000/docs.

Finally, start the Streamlit app:

streamlit run streamlit_app/main.py

The web interface can be accessed at http://localhost:8501.

Contributing Contributions are welcome! If you find any issues or want to add a new feature, please open an issue or submit a pull request.

License This project is licensed under the MIT License - see the LICENSE.md file for details.