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# Importations
from typing import Union
from fastapi import FastAPI
import pickle
from pydantic import BaseModel
import pandas as pd
import os
import uvicorn
from fastapi import HTTPException, status
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
# Setup Section
# Create FastAPI instance
app = FastAPI(title="Sepsis Prediction API",
description="API for Predicting Sespsis ")
# A function to load machine Learning components to re-use
def Ml_loading_components(fp):
with open(fp, "rb") as f:
object = pickle.load(f)
return (object)
# Loading the machine learning components
DIRPATH = os.path.dirname(os.path.realpath(__file__))
ml_core_fp = os.path.join(DIRPATH, "ML", "ML_Model.pkl")
ml_components_dict = Ml_loading_components(fp=ml_core_fp)
# Defining the variables for each component
label_encoder = ml_components_dict['label_encoder'] # The label encoder
# Loaded scaler component
scaler = ml_components_dict['scaler']
# Loaded model
model = ml_components_dict['model']
# Defining our input variables
class InputData(BaseModel):
PRG: int
PL: int
BP: int
SK: int
TS: int
BMI: float
BD2: float
Age: int
"""
* PRG: Plasma glucose
* PL: Blood Work Result-1 (mu U/ml)
* PR: Blood Pressure (mmHg)
* SK: Blood Work Result-2(mm)
* TS: Blood Work Result-3 (muU/ml)
* M11: Body mass index (weight in kg/(height in m)^2
* BD2: Blood Work Result-4 (mu U/ml)
* Age: patients age(years)
"""
# Index route
@app.get("/")
def index():
return {'message': 'Hello, Welcome to My Sepsis Prediction FastAPI'}
# Create prediction endpoint
@app.post("/predict")
def predict(df: InputData):
# Prepare the feature and structure them like in the notebook
df = pd.DataFrame([df.dict().values()], columns=df.dict().keys())
print(f"[Info] The inputed dataframe is : {df.to_markdown()}")
age = df['Age']
print(age)
# Scaling the inputs
df_scaled = scaler.transform(df)
# Prediction
raw_prediction = model.predict(df_scaled)
if raw_prediction == 0:
raise HTTPException(status_code=status.HTTP_200_OK,
detail="The patient will Not Develop Sepsis")
elif raw_prediction == 1:
raise HTTPException(status_code=status.HTTP_200_OK,
detail="The patient Will Develop Sepsis")
else:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Prediction Error")
if __name__ == "__main__":
uvicorn.run("main:app", reload=True)
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