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add app.py
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app.py
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from fastapi import FastAPI, HTTPException
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import uvicorn
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#from typing import List, Literal
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from pydantic import BaseModel, Field
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import pandas as pd
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import pickle, os
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#setup
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# Get the directory of the current file (FastAPI application file)
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DIRPATH = os.path.dirname(os.path.realpath(__file__))
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# Construct the path to ml.pkl relative to the current file using forward slashes
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ml_core_fp = os.path.join(DIRPATH, "../model/ml.pkl")
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#useful functions
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def load_ml_components(fp):
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"load the ml components to re-use in app"
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with open(fp, 'rb') as file:
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obj = pickle.load(file)
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return obj
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# Loading: Execute and instantiate ml components
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ml_components_dict = load_ml_components(fp = ml_core_fp)
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pipeline = ml_components_dict["pipeline"]
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encoder = ml_components_dict["encoder"]
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# API
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app = FastAPI(
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title= "Sepsis classification API"
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)
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# Input for Modelling
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class Sepsis_Pred(BaseModel):
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PRG: int = Field(..., description='Plasma glucose')
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PL: int = Field(..., description='Blood Work Result-1 (mu U/ml)')
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PR: int = Field(..., description='Blood Pressure (mm Hg)')
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SK: int = Field(..., description='Blood Work Result-2 (mm)')
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TS: int = Field(..., description='Blood Work Result-3 (mu U/ml)')
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M11: float = Field(..., description='Body mass index (weight in kg/(height in m)^2)')
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BD2: float = Field(..., description='Blood Work Result-4 (mu U/ml)')
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Age: int = Field(..., description='Patient age (years)')
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Insurance: int = Field(..., description='If a patient holds a valid insurance card')
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@app.get("/")
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def root():
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return {
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"Info": "Sepsis classification API : This API classifies whether a patient will develop sepsis based on various test results"
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}
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@app.post("/classify_patient")
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def sepsis_classification(sepsis_pred: Sepsis_Pred):
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try:
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#Dataframe creation
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df = pd.DataFrame([sepsis_pred.model_dump()])
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print(f'df: {df}')
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# ML prediction
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prediction = pipeline.predict(df)
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# Get the index of the predicted class (0 or 1 in binary classification)
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predicted_class_index = prediction[0]
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confidence_score = pipeline.predict_proba(df)
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# Retrieve the confidence score for the predicted class
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confidence_score_predicted_class = confidence_score[0][predicted_class_index]
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print(f"confidence_score: {confidence_score}")
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execution_message = "Execution successful"
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# encoded prediction
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decoded_prediction = encoder.inverse_transform([prediction])[0]
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return {"execution message": execution_message, "patient_diagnosis": decoded_prediction, "confidence_score": confidence_score_predicted_class}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"An error occurred during prediction {str(e)}")
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if __name__ == "__main__":
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uvicorn.run("main:app", reload=True)
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