readme

README.md

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-# Sepsis Prediction API
-
-This repository contains a FastAPI-based web application that provides an API for predicting sepsis disease in patients based on input features. The API leverages a machine learning model trained on relevant data. The API allows users to submit patient data and receive a prediction along with confidence scores.
-
-## Getting Started
-
-To get started with the Sepsis Prediction API, follow the steps below:
-
-### Prerequisites
-
-- Docker: Make sure you have Docker installed on your machine.
-
-### Installation
-
-1. Clone this repository to your local machine:
-
-   ```bash
-   git clone https:https://huggingface.co/UholoDala
-
-2. Navigate to the repository directory:
-    cd sepsis_classic
-
-3. Build the Docker image:
-    docker build -t sepsis-prediction-api .
-
-4. Run the Docker container:
-    docker run -d -p 7860:7860 sepsis-prediction-api
-
-The API will now be accessible at http://localhost:7860.
-
-## API Endpoints
-
-### Root Endpoint
-    - URL: http://localhost:7860/
-    - Method: GET
-    - Description: Provides basic information about the Sepsis Prediction API.
-
-### Sepsis Classification Endpoint
-    - URL: http://localhost:7860/spaces/UholoDala/sepsis_classic/classify
-    - Method: POST
-    - Description: Accepts patient data and performs sepsis classification. Provides the prediction and confidence scores.
-
-## Usage
-You can interact with the API using tools like curl, web browsers, or API testing tools like Postman.
-
-### Example curl command to perform sepsis classification:
-    curl -X POST -H "Content-Type: application/json" -d '{
-  "PlasmaGlucose": 120,
-  "BloodWorkResult_1": 4,
-  "BloodPressure": 80,
-  "BloodWorkResult_2": 7,
-  "BloodWorkResult_3": 9,
-  "BodyMassIndex": 25.5,
-  "BloodWorkResult_4": 12.5,
-  "Age": 50
-}' http://localhost:7860/spaces/UholoDala/sepsis_classic/classify
-
-## Dependencies
-    - pytest
-    - scikit-learn
-    - fastapi[all]
-    - pydantic
-    - uvicorn
-    - pypi-json
-    - requests
-    - pandas
-    - tabulate
-
-This project is licensed under the MIT License.
-
-## Acknowledgments
-This project was developed as part of the Azubi Africa Data Analysis LP6 Project. We would like to thank all contributors for their valuable insights and efforts.
-
-For more information, feel free to contact me at [email protected] or [email protected].
-
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+Subproject commit 25aa7c6e861a8bb874aeef6d802f5013608c3205