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from fastapi import FastAPI | |
from pydantic import BaseModel | |
import joblib | |
import numpy as np | |
class SepssisInput(BaseModel): | |
PRG:float | |
PL:float | |
PR:float | |
SK:float | |
TS:float | |
M11:float | |
Age:float | |
Insurance:int | |
model=joblib.load("models/sepssis_model_v1.pkl") | |
app=FastAPI() | |
def home_page(): | |
return "Welcome to the API " | |
def inference_sepssis(sepssis_features:SepssisInput): | |
input_data=np.array([[ | |
sepssis_features.PRG, | |
sepssis_features.PL, | |
sepssis_features.PR, | |
sepssis_features.SK, | |
sepssis_features.TS, | |
sepssis_features.M11, | |
sepssis_features.Age, | |
sepssis_features.Insurance | |
]]) | |
label_predict=model.predict(input_data) | |
sepssis_mapping={ | |
0:"Negative", | |
1:"Positive" | |
} | |
clase_predict=sepssis_mapping[label_predict[0]] | |
return {"Predict_for_Sepssis":clase_predict} | |