| from fastapi import FastAPI |
| from pydantic import BaseModel |
| import pickle |
| import pandas as pd |
| import numpy as np |
| import uvicorn |
| import os |
| from sklearn.preprocessing import StandardScaler |
| import joblib |
|
|
| |
| """ Creating the FastAPI Instance. i.e. foundation for our API, |
| which will be the main part of our project""" |
|
|
| app = FastAPI(title="API") |
|
|
|
|
| """We load a machine learning model and a scaler that help us make predictions based on data.""" |
| model = joblib.load(r'C:\Users\viole\OneDrive\Documents\sepsis P6\ml\gbc.pkl', mmap_mode='r') |
| scaler = joblib.load(r'C:\Users\viole\OneDrive\Documents\sepsis P6\ml\scaler.pkl', mmap_mode='r') |
|
|
| """We define a function that will make predictions using our model and scaler.""" |
| def predict(df, endpoint='simple'): |
| |
| scaled_df = scaler.transform(df) |
|
|
| |
| prediction = model.predict_proba(scaled_df) |
| highest_proba = prediction.max(axis=1) |
|
|
| predicted_labels = ["Patient does not have sepsis" if i == 0 else "Patient has Sepsis" for i in highest_proba] |
| response = [] |
| for label, proba in zip(predicted_labels, highest_proba): |
| output = { |
| "prediction": label, |
| "probability of prediction": str(round(proba * 100)) + '%' |
| } |
| response.append(output) |
| return response |
|
|
|
|
| """We create models for the data that our API will work with. |
| We define what kind of information the data will have. |
| It's like deciding what information we need to collect and how it should be organized.""" |
|
|
|
|
| """These classes define the data models used for API endpoints. |
| The 'Patient' class represents a single patient's data, |
| and the 'Patients' class represents a list of patients' data. |
| The Patients class also includes a class method return_list_of_dict() |
| that converts the Patients object into a list of dictionaries""" |
|
|
| class Patient(BaseModel): |
| Blood_Work_R1: float |
| Blood_Pressure: float |
| Blood_Work_R3: float |
| BMI: float |
| Blood_Work_R4: float |
| Patient_age: int |
|
|
|
|
| """Next block of code defines different parts of our API and how it responds to different requests. |
| It sets up a main page with a specific message, provides a checkup endpoint to receive |
| optional parameters, and sets up prediction endpoints to receive medical data for making predictions, |
| either for a single patient or multiple patients.""" |
|
|
| @app.get("/") |
| def root(): |
| return {"API": "This is an API for sepsis prediction."} |
|
|
| |
| @app.post("/predict") |
| def predict_sepsis(patient: Patient): |
|
|
| |
| data = pd.DataFrame(patient.dict(), index=[0]) |
| scaled_data = scaler.transform(data) |
| parsed = predict(df=scaled_data) |
| return {"output": parsed} |
|
|
|
|
| if __name__ == "__main__": |
| os.environ["DEBUG"] = "True" |
| uvicorn.run("main:app", reload=True) |
|
|
