Update app.py

app.py

@@ -1,35 +1,42 @@
-from fastapi import FastAPI
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel, Field, computed_field
-import numpy as np
-from typing import Literal, Annotated
-import pickle 
-import math
-import pandas as pd
-
-# import the ML model 
-with open('delivery_time_model.pkl','rb') as f:
-    model = pickle.load(f)
-    
-app = FastAPI()
-
-
-# pydantic model build to validate the input data
-
-class UserInput(BaseModel):
-    age : Annotated[int,Field(...,ge = 18, lt = 120,description = 'Age of the delivery person')]
-    rating : Annotated[float,Field(...,ge = 1, le = 6 ,description = 'Delivery person Ratings')]
-    distance : Annotated[int,Field(...,gt = 0,description = 'Total Distance to be covered')]
-    
-    
-@app.post('/predict')
-def predict_time(data: UserInput):
-    features = np.array([[data.age, data.rating, data.distance]])
-    prediction = model.predict(features)
-
-    prediction_value = math.ceil(float(prediction[0]))
-
-    return JSONResponse(
-        status_code=200,
-        content={"Predicted Delivery Time in Minutes": prediction_value}
-    )
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field, computed_field
+import numpy as np
+from typing import Literal, Annotated
+import pickle 
+import math
+import pandas as pd
+
+# import the ML model 
+with open('delivery_time_model.pkl','rb') as f:
+    model = pickle.load(f)
+    
+app = FastAPI()
+
+@app.get('/')
+def home():
+    return {'message' : 'Delivery time estimation API '}
+
+@app.get('/health')
+def healthcheck():
+    return {'status' : 'OK'}
+
+# pydantic model build to validate the input data
+
+class UserInput(BaseModel):
+    age : Annotated[int,Field(...,ge = 18, lt = 120,description = 'Age of the delivery person')]
+    rating : Annotated[float,Field(...,ge = 1, le = 6 ,description = 'Delivery person Ratings')]
+    distance : Annotated[int,Field(...,gt = 0,description = 'Total Distance to be covered')]
+    
+    
+@app.post('/predict')
+def predict_time(data: UserInput):
+    features = np.array([[data.age, data.rating, data.distance]])
+    prediction = model.predict(features)
+
+    prediction_value = math.ceil(float(prediction[0]))
+
+    return JSONResponse(
+        status_code=200,
+        content={"Predicted Delivery Time in Minutes": prediction_value}
+    )