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# 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") |