error handling

api/index.py

@@ -1,27 +1,46 @@
-from fastapi import FastAPI, Query
+from fastapi import FastAPI, Query, HTTPException
+from pydantic import ValidationError
 import pickle
+import os
 
 app = FastAPI()
 
-# loading the saved model
-diabetes_model = pickle.load(open('diabetes_model.sav', 'rb'))
+try:
+    if not os.path.exists('diabetes_model.sav'):
+        raise FileNotFoundError("Model file not found.")
+    with open('diabetes_model.sav', 'rb') as model_file:
+        diabetes_model = pickle.load(model_file)
+except (FileNotFoundError, pickle.UnpicklingError) as e:
+    diabetes_model = None
+    print(f"Error loading model: {e}")
 
 @app.get('/api/diabetes_prediction')
 async def diabetes_predd(
-    pregnancies: int = Query(...),
-    Glucose: int = Query(...),
-    BloodPressure: int = Query(...),
-    SkinThickness: int = Query(...),
-    Insulin: int = Query(...),
-    BMI: float = Query(...),
-    DiabetesPedigreeFunction: float = Query(...),
-    Age: int = Query(...)
+    pregnancies: int = Query(..., ge=0, description="Number of pregnancies"),
+    Glucose: int = Query(..., ge=0, description="Plasma glucose concentration"),
+    BloodPressure: int = Query(..., ge=0, description="Diastolic blood pressure (mm Hg)"),
+    SkinThickness: int = Query(..., ge=0, description="Triceps skin fold thickness (mm)"),
+    Insulin: int = Query(..., ge=0, description="2-hour serum insulin (mu U/ml)"),
+    BMI: float = Query(..., ge=0, description="Body mass index (weight in kg/(height in m)^2)"),
+    DiabetesPedigreeFunction: float = Query(..., ge=0, description="Diabetes pedigree function"),
+    Age: int = Query(..., ge=0, description="Age (years)")
 ):
+    if diabetes_model is None:
+        raise HTTPException(status_code=500, detail="Model is not loaded or available.")
+
     input_list = [pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]
 
-    prediction = diabetes_model.predict([input_list])
-
-    if prediction[0] == 0:
-        return {'result': 'The person is not diabetic'}
-    else:
-        return {'result': 'The person is diabetic'}
+    try:
+        prediction = diabetes_model.predict([input_list])
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error during prediction: {str(e)}")
+
+    if len(prediction) == 0:
+        raise HTTPException(status_code=500, detail="Prediction result is empty.")
+
+    result = 'The person is not diabetic' if prediction[0] == 0 else 'The person is diabetic'
+    return {'result': result}
+
+@app.exception_handler(Exception)
+async def global_exception_handler(request, exc):
+    return HTTPException(status_code=500, detail=f"An unexpected error occurred: {exc}")

requirements.txt

@@ -1,5 +1,4 @@
 fastapi
 uvicorn
 pydantic
-scikit-learn
-requests
+scikit-learn