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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
app = FastAPI(title="API")
"""We load a machine learning model and a scaler that help us make predictions based on data."""
model = joblib.load('model.pkl',mmap_mode='r')
scaler = joblib.load('scaler.pkl',mmap_mode='r')
def predict(df, endpoint='simple'):
# Scaling
scaled_df = scaler.transform(df)
# Prediction
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
class Patient(BaseModel):
Blood_Work_R1: float
Blood_Pressure: float
Blood_Work_R3: float
BMI: float
Blood_Work_R4: float
Patient_age: int
@app.get("/")
def root():
return {"API": "This is an API for sepsis prediction."}
# Prediction endpoint (Where we will input our features)
@app.post("/predict")
def predict_sepsis(patient: Patient):
# Make prediction
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" # Enable debug mode
uvicorn.run("main:app", reload=True)
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