Upload main.py

main.py

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+from fastapi import FastAPI, Query, HTTPException
+import joblib
+from pydantic import BaseModel
+import pandas as pd
+
+
+pipeline = joblib.load('./sepsis_classification_pipeline.joblib')
+encoder = joblib.load('./label_encoder.joblib')
+model = joblib.load('./random_forest_model.joblib')
+
+app = FastAPI()
+
+
+class features(BaseModel):
+    Age: int
+    Body_Mass_Index_BMI: float
+    Diastolic_Blood_Pressure: float
+    Plasma_Glucose: float
+    Triceps_Skinfold_Thickness: float
+    Elevated_Glucose: float
+    Diabetes_Pedigree_Function: float
+    Insulin_Levels: float
+
+
+@app.post("/predict")
+async def predict_sepsis(item: features):
+    try:
+        # Convert input data to DataFrame
+        input_data = pd.DataFrame([item.dict()])
+
+        # input_data = pipeline.named_steps.preprocessor.transform(input_data)
+
+        # Make predictions using the model
+        predictions = pipeline.predict(input_data)
+
+        # Decode predictions using the label encoder
+        decoded_predictions = encoder.inverse_transform(predictions)
+
+        return {"prediction": f'Patient is {decoded_predictions[0]}'}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))