add app.py

app.py

@@ -0,0 +1,97 @@
+from fastapi import FastAPI, HTTPException
+import uvicorn
+#from typing import List, Literal
+from pydantic import BaseModel, Field
+import pandas as pd
+import pickle, os
+
+ #setup
+# Get the directory of the current file (FastAPI application file)
+DIRPATH = os.path.dirname(os.path.realpath(__file__))
+
+# Construct the path to ml.pkl relative to the current file using forward slashes
+ml_core_fp = os.path.join(DIRPATH, "../model/ml.pkl")
+
+
+#useful functions
+def  load_ml_components(fp):
+    "load the ml components to re-use in app"
+    with open(fp, 'rb') as file:
+        obj = pickle.load(file)
+        return obj
+    
+
+# Loading: Execute and instantiate ml components
+ml_components_dict = load_ml_components(fp = ml_core_fp)
+
+pipeline = ml_components_dict["pipeline"]
+
+encoder =  ml_components_dict["encoder"]
+
+
+# API
+app = FastAPI(
+    title= "Sepsis classification API"
+)
+
+# Input for Modelling 
+class Sepsis_Pred(BaseModel):
+
+    PRG: int = Field(..., description='Plasma glucose')
+    PL: int = Field(..., description='Blood Work Result-1 (mu U/ml)')
+    PR: int = Field(..., description='Blood Pressure (mm Hg)')
+    SK: int = Field(..., description='Blood Work Result-2 (mm)')
+    TS: int = Field(..., description='Blood Work Result-3 (mu U/ml)')
+    M11: float = Field(..., description='Body mass index (weight in kg/(height in m)^2)')
+    BD2: float = Field(..., description='Blood Work Result-4 (mu U/ml)')
+    Age: int = Field(..., description='Patient age (years)')
+    Insurance: int = Field(..., description='If a patient holds a valid insurance card')
+
+
+@app.get("/")
+def root():
+    return {
+        "Info": "Sepsis classification API : This API classifies whether a patient will develop sepsis based on various test results"
+    }
+
+
+@app.post("/classify_patient")
+def sepsis_classification(sepsis_pred: Sepsis_Pred):
+
+    try:
+
+        #Dataframe creation
+        df = pd.DataFrame([sepsis_pred.model_dump()])
+
+        print(f'df: {df}')
+       
+        # ML prediction
+        prediction = pipeline.predict(df)
+
+        # Get the index of the predicted class (0 or 1 in binary classification)
+        predicted_class_index = prediction[0]
+
+        confidence_score = pipeline.predict_proba(df)
+
+        # Retrieve the confidence score for the predicted class
+
+        confidence_score_predicted_class = confidence_score[0][predicted_class_index]
+
+
+        print(f"confidence_score: {confidence_score}")
+
+        execution_message = "Execution successful"
+
+
+        # encoded prediction
+        decoded_prediction = encoder.inverse_transform([prediction])[0]
+
+    
+        return {"execution message": execution_message, "patient_diagnosis": decoded_prediction, "confidence_score": confidence_score_predicted_class}
+    
+    
+    except Exception as e: 
+        raise HTTPException(status_code=500, detail=f"An error occurred during prediction {str(e)}")
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", reload=True)