Upload main.py

main.py

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+# import libraries
+from pydantic import BaseModel
+import pandas as pd
+import joblib
+import uvicorn
+import numpy as np
+from fastapi import FastAPI, HTTPException,Query
+
+app = FastAPI()
+
+###create home
+@app.get('/')
+def home():
+ return{'message':'Welcome to Sepsis Prediction Using Fastapi'}
+
+## Load the model
+model = joblib.load("src/rf_pipeline.joblib") 
+ 
+# Endpoint for predicting sepsis using a GET request
+@app.post("/predict")
+def predict_sepsis(
+ PRG: int = Query(..., description="Plasma_glucose"),
+ PL: int = Query(..., description="Blood_Work_R1"),
+ PR: int = Query(..., description="Blood_Pressure"), 
+ SK: int = Query(..., description="Blood_Work_R2"),
+ TS: int = Query(..., description="Blood_Work_R3"),
+ M11: float = Query(..., description="BMI"),
+ BD2: float = Query(..., description="Blood_Work_R4"),
+ Age: int = Query(..., description="Age")
+):
+ try:
+ # Convert input data to a dictionary
+ input_data = {
+ 'PRG': PRG,
+ 'PL': PL,
+ 'PR': PR,
+ 'SK': SK,
+ 'TS': TS,
+ 'M11': M11,
+ 'BD2': BD2,
+ 'Age': Age, 
+ }
+
+
+ # Convert input_data to DataFrame
+ input_data_df = pd.DataFrame([input_data])
+
+ # Use the loaded model to make predictions
+
+ prediction= model.predict(input_data_df)[0]
+ 
+ sepsis_status = "patient has sepsis" if prediction == 1 else "Patient does not have sepsis"
+ 
+ # Return the prediction
+ return {"prediction": sepsis_status}
+ 
+ except Exception as e:
+ raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+ import uvicorn
+ import nest_asyncio
+
+ nest_asyncio.apply()
+
+ uvicorn.run(app, host="127.0.0.1", port=8003, log_level="info")