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from fastapi import FastAPI, HTTPException
import uvicorn
#from typing import List, Literal
from pydantic import BaseModel, Field
import pandas as pd
import pickle, os
#setup
# Get the directory of the current file (FastAPI application file)
DIRPATH = os.path.dirname(os.path.realpath(__file__))
# Construct the path to ml.pkl relative to the current file using forward slashes
ml_core_fp = os.path.join(DIRPATH, "../model/ml.pkl")
#useful functions
def load_ml_components(fp):
"load the ml components to re-use in app"
with open(fp, 'rb') as file:
obj = pickle.load(file)
return obj
# Loading: Execute and instantiate ml components
ml_components_dict = load_ml_components(fp = ml_core_fp)
pipeline = ml_components_dict["pipeline"]
encoder = ml_components_dict["encoder"]
# API
app = FastAPI(
title= "Sepsis classification API"
)
# Input for Modelling
class Sepsis_Pred(BaseModel):
PRG: int = Field(..., description='Plasma glucose')
PL: int = Field(..., description='Blood Work Result-1 (mu U/ml)')
PR: int = Field(..., description='Blood Pressure (mm Hg)')
SK: int = Field(..., description='Blood Work Result-2 (mm)')
TS: int = Field(..., description='Blood Work Result-3 (mu U/ml)')
M11: float = Field(..., description='Body mass index (weight in kg/(height in m)^2)')
BD2: float = Field(..., description='Blood Work Result-4 (mu U/ml)')
Age: int = Field(..., description='Patient age (years)')
Insurance: int = Field(..., description='If a patient holds a valid insurance card')
@app.get("/")
def root():
return {
"Info": "Sepsis classification API : This API classifies whether a patient will develop sepsis based on various test results"
}
@app.post("/classify_patient")
def sepsis_classification(sepsis_pred: Sepsis_Pred):
try:
#Dataframe creation
df = pd.DataFrame([sepsis_pred.model_dump()])
print(f'df: {df}')
# ML prediction
prediction = pipeline.predict(df)
# Get the index of the predicted class (0 or 1 in binary classification)
predicted_class_index = prediction[0]
confidence_score = pipeline.predict_proba(df)
# Retrieve the confidence score for the predicted class
confidence_score_predicted_class = confidence_score[0][predicted_class_index]
print(f"confidence_score: {confidence_score}")
execution_message = "Execution successful"
# encoded prediction
decoded_prediction = encoder.inverse_transform([prediction])[0]
return {"execution message": execution_message, "patient_diagnosis": decoded_prediction, "confidence_score": confidence_score_predicted_class}
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred during prediction {str(e)}")
if __name__ == "__main__":
uvicorn.run("main:app", reload=True) |