doctapool / career-accelerator-lp6-fast-api

📚 SUMMARY TABLE 📚

	Project's Name	Article Link	Deployment Link
1	-Embedding a Machine Learning Model into a Web Application	(https://medium.com/@richard.fiagbeati/application-for-predicting-sepsis-using-fastapi-ed1e90b43288)	(https://ausbel-sepsis-prediction-app-fastapi-1.hf.space/docs#/default/predict_sepsis_predict)

Sepsis Prediction APP using FASTAPI ✨

📚 Description

The project’s aim is to incorporate a proficiently trained machine learning model into our Sepsis Prediction Application. This will be achieved through the utilization of FastAPI and Docker documentation to seamlessly integrate the model.

📖 Table of Contents

• Installation
• Usage
• Acknowledgement
• contact
• Authors

🔧 Installation

To get started , follow these installation steps:

# setup the environment on windows by running the following code.
python -m venv venv; venv\Scripts\activate; python -m pip install -q --upgrade pip; python -m pip install -r requirements.txt  

 

#On Linux
python3 -m venv venv; source venv/bin/activate; python -m pip install -q --upgrade pip; python -m pip install -r requirements.txt

 

The Two commands are of the same structure 1.Activate the python environment 2.Upgrade pip to it current version 3.install the requirements located in requirements.txt: You should be at the root of your env

🚀 Usage

---

title: Sepsis Prediction App FastAPI 1 emoji: 💻 colorFrom: green colorTo: indigo sdk: docker pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

✨ Acknowledgments

I would like to express my gratitude to The Azubi Africa team for their valuable contributions to this project.

📞 Contact

For any questions, concerns, or suggestions regarding this project, please contact me at [email protected].

👥 Authors

This project is developed and maintained by: Richard Ausbel Fiagbeati