Adoley/Sepsis-Prediction-Using-FastAPI

1from fastapi import FastAPI, Form2from pydantic import BaseModel3import pickle4import pandas as pd5import numpy as np6import uvicorn7import os8from sklearn.preprocessing import StandardScaler9import joblib10 11    12""" Creating the FastAPI Instance. i.e. foundation for our API,13which will be the main part of our project"""14 15app = FastAPI(title="API",description="API for sepsis prediction")16 17 18"""We load a machine learning model and a scaler that help us make predictions based on data."""19model = joblib.load('gbc.pkl',mmap_mode='r')20scaler = joblib.load('scaler.pkl',mmap_mode='r')21 22"""We define a function that will make predictions using our model and scaler."""23def predict(df, endpoint='simple'):24    # Scaling25    scaled_df = scaler.transform(df)26 27    # Prediction28    prediction = model.predict_proba(scaled_df)29    highest_proba = prediction.max(axis=1)30 31    predicted_labels = ["Patient does not have sepsis" if i == 0 else "Patient has Sepsis" for i in highest_proba]32    response = []33    for label, proba in zip(predicted_labels, highest_proba):34        output = {35            "prediction": label,36            "probability of prediction": str(round(proba * 100)) + '%'37        }38        response.append(output)39    return response40 41 42"""We create models for the data that our API will work with.43We define what kind of information the data will have.44It's like deciding what information we need to collect and how it should be organized."""45 46 47"""These classes define the data models used for API endpoints.48The 'Patient' class represents a single patient's data,49and the 'Patients' class represents a list of patients' data.50The Patients class also includes a class method return_list_of_dict()51that converts the Patients object into a list of dictionaries"""52 53class Patient(BaseModel):54    Blood_Work_R1: float = Form(...)55    Blood_Pressure: float = Form(...)56    Blood_Work_R3: float = Form(...)57    BMI: float = Form(...)58    Blood_Work_R4: float = Form(...)59    Patient_age: int = Form(...)60 61 62"""Next block of code defines different parts of our API and how it responds to different requests.63It sets up a main page with a specific message, provides a checkup endpoint to receive64optional parameters, and sets up prediction endpoints to receive medical data for making predictions,65either for a single patient or multiple patients."""66 67@app.get("/")68def root():69    return {"API": "This is an API for sepsis prediction."}70 71# Prediction endpoint (Where we will input our features)72@app.post("/predict")73def predict_sepsis(patient: Patient):74 75    # Make prediction76    data = pd.DataFrame(patient.dict(), index=[0])77    scaled_data = scaler.transform(data)78    parsed = predict(df=scaled_data)79    return {"output": parsed}80 81if __name__ == "__main__":82    os.environ["DEBUG"] = "True"  # Enable debug mode83    uvicorn.run("main:app", reload=True)84