main.py

# import libraries
from pydantic import BaseModel
import pandas as pd
import joblib
import uvicorn
import numpy as np
from fastapi import FastAPI, HTTPException,Query

app = FastAPI()

###create home
@app.get('/')
def home():
 return{'message':'Welcome to Sepsis Prediction Using Fastapi'}

## Load the model
model = joblib.load("src/rf_pipeline.joblib") 
 
# Endpoint for predicting sepsis using a GET request
@app.post("/predict")
def predict_sepsis(
 PRG: int = Query(..., description="Plasma_glucose"),
 PL: int = Query(..., description="Blood_Work_R1"),
 PR: int = Query(..., description="Blood_Pressure"), 
 SK: int = Query(..., description="Blood_Work_R2"),
 TS: int = Query(..., description="Blood_Work_R3"),
 M11: float = Query(..., description="BMI"),
 BD2: float = Query(..., description="Blood_Work_R4"),
 Age: int = Query(..., description="Age")
):
 try:
 # Convert input data to a dictionary
 input_data = {
 'PRG': PRG,
 'PL': PL,
 'PR': PR,
 'SK': SK,
 'TS': TS,
 'M11': M11,
 'BD2': BD2,
 'Age': Age, 
 }


 # Convert input_data to DataFrame
 input_data_df = pd.DataFrame([input_data])

 # Use the loaded model to make predictions

 prediction= model.predict(input_data_df)[0]
 
 sepsis_status = "patient has sepsis" if prediction == 1 else "Patient does not have sepsis"
 
 # Return the prediction
 return {"prediction": sepsis_status}
 
 except Exception as e:
 raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
 import uvicorn
 import nest_asyncio

 nest_asyncio.apply()

 uvicorn.run(app, host="127.0.0.1", port=8003, log_level="info")