Update src/app.py

src/app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException, Query
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import pickle
 import pandas as pd
@@ -10,7 +10,7 @@ app = FastAPI(
 )
 
 # Load the model and key components
-with open('model_and_key_components.pkl', 'rb') as file:
+with open('/model_and_key_components.pkl', 'rb') as file:
     loaded_components = pickle.load(file)
 
 loaded_model = loaded_components['model']
@@ -19,14 +19,14 @@ loaded_scaler = loaded_components['scaler']
 
 # Define the input data structure using Pydantic BaseModel
 class InputData(BaseModel):
-    PRG: int = Query(..., title="Patient's Pregnancy Count", description="Enter the number of pregnancies.", example=2)
-    PL: float = Query(..., title="Platelet Count", description="Enter the platelet count.", example=150.0)
-    PR: float = Query(..., title="Pulse Rate", description="Enter the pulse rate.", example=75.0)
-    SK: float = Query(..., title="Skin Thickness", description="Enter the skin thickness.", example=25.0)
-    TS: int = Query(..., title="Triceps Skin Fold Thickness", description="Enter the triceps skin fold thickness.", example=30)
-    M11: float = Query(..., title="Insulin Level", description="Enter the insulin level.", example=120.0)
-    BD2: float = Query(..., title="BMI", description="Enter the Body Mass Index (BMI).", example=32.0)
-    Age: int = Query(..., title="Age", description="Enter the patient's age.", example=35)
+    PRG: int
+    PL: float
+    PR: float
+    SK: float
+    TS: int
+    M11: float
+    BD2: float
+    Age: int
 
 # Define the output data structure using Pydantic BaseModel
 class OutputData(BaseModel):
@@ -55,6 +55,7 @@ async def root():
     message = "Welcome to your Sepsis Classification API! Click [here](/docs) to access the API documentation."
     return {"message": message}
 
+
 @app.post("/predict/", response_model=OutputData)
 async def predict_sepsis(input_data: InputData):
     try:
@@ -62,10 +63,11 @@ async def predict_sepsis(input_data: InputData):
         sepsis_status = make_predictions(input_data_scaled_df)
         return {"Sepsis": sepsis_status}
     except Exception as e:
+
         # Handle exceptions and return an error response
         raise HTTPException(status_code=500, detail=str(e))
 
 if __name__ == "__main__":
     import uvicorn
     # Run the FastAPI application on the local host and port 8000
-    uvicorn.run(app, host="127.0.0.1", port=8000)
+    uvicorn.run(app, host="127.0.0.1", port=8000)