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")