Upload main.py

src/main.py

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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)