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Update src/app.py
Browse files- src/app.py +13 -11
src/app.py
CHANGED
@@ -1,4 +1,4 @@
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import pickle
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import pandas as pd
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@@ -10,7 +10,7 @@ app = FastAPI(
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)
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# Load the model and key components
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with open('model_and_key_components.pkl', 'rb') as file:
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loaded_components = pickle.load(file)
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loaded_model = loaded_components['model']
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@@ -19,14 +19,14 @@ loaded_scaler = loaded_components['scaler']
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# Define the input data structure using Pydantic BaseModel
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class InputData(BaseModel):
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PRG: int
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PL: float
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PR: float
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SK: float
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TS: int
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M11: float
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BD2: float
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Age: int
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# Define the output data structure using Pydantic BaseModel
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class OutputData(BaseModel):
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@@ -55,6 +55,7 @@ async def root():
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message = "Welcome to your Sepsis Classification API! Click [here](/docs) to access the API documentation."
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return {"message": message}
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@app.post("/predict/", response_model=OutputData)
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async def predict_sepsis(input_data: InputData):
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try:
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@@ -62,10 +63,11 @@ async def predict_sepsis(input_data: InputData):
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sepsis_status = make_predictions(input_data_scaled_df)
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return {"Sepsis": sepsis_status}
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except Exception as e:
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# Handle exceptions and return an error response
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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# Run the FastAPI application on the local host and port 8000
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uvicorn.run(app, host="127.0.0.1", port=8000)
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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import pickle
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import pandas as pd
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)
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# Load the model and key components
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with open('/model_and_key_components.pkl', 'rb') as file:
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loaded_components = pickle.load(file)
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loaded_model = loaded_components['model']
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# Define the input data structure using Pydantic BaseModel
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class InputData(BaseModel):
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PRG: int
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PL: float
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PR: float
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SK: float
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TS: int
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M11: float
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BD2: float
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Age: int
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# Define the output data structure using Pydantic BaseModel
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class OutputData(BaseModel):
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message = "Welcome to your Sepsis Classification API! Click [here](/docs) to access the API documentation."
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return {"message": message}
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@app.post("/predict/", response_model=OutputData)
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async def predict_sepsis(input_data: InputData):
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try:
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sepsis_status = make_predictions(input_data_scaled_df)
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return {"Sepsis": sepsis_status}
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except Exception as e:
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# Handle exceptions and return an error response
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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# Run the FastAPI application on the local host and port 8000
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uvicorn.run(app, host="127.0.0.1", port=8000)
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