Create main.py

main.py

@@ -0,0 +1,71 @@
+from fastapi import FastAPI, HTTPException, Query
+import pandas as pd
+import joblib
+
+ 
+
+app = FastAPI()
+
+ 
+
+# Load the sepsis prediction model
+model = joblib.load('XGB.joblib')
+
+ 
+
+@app.get("/")
+async def read_root():
+    return {"message": "Sepsis Prediction API using FastAPI"}
+
+ 
+
+def classify(prediction):
+    if prediction == 0:
+        return "Patient does not have sepsis"
+    else:
+        return "Patient has sepsis"
+
+ 
+
+@app.get("/predict/")
+async def predict_sepsis(
+    prg: float = Query(..., description="Plasma glucose"),
+    pl: float = Query(..., description="Blood Work Result-1 (mu U/ml)"),
+    pr: float = Query(..., description="Blood Pressure (mm Hg)"),
+    sk: float = Query(..., description="Blood Work Result-2 (mm)"),
+    ts: float = Query(..., description="Blood Work Result-3 (mu U/ml)"),
+    m11: float = Query(..., description="Body mass index (weight in kg/(height in m)^2"),
+    bd2: float = Query(..., description="Blood Work Result-4 (mu U/ml)"),
+    age: int = Query(..., description="Patient's age (years)")
+):
+    input_data = [prg, pl, pr, sk, ts, m11, bd2, age]
+
+ 
+
+    input_df = pd.DataFrame([input_data], columns=[
+        "Plasma glucose", "Blood Work Result-1", "Blood Pressure",
+        "Blood Work Result-2", "Blood Work Result-3",
+        "Body mass index", "Blood Work Result-4", "Age"
+    ])
+
+ 
+
+    pred = model.predict(input_df)
+    output = classify(pred[0])
+
+ 
+
+    response = {
+        "prediction": output
+    }
+
+ 
+
+    return response
+
+ 
+
+# Run the app using Uvicorn
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=7860)