Upload main.py

main.py

@@ -0,0 +1,90 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+import numpy as np
+import uvicorn
+import os
+
+# call the app
+app = FastAPI(title="API")
+
+
+# Load the model and scaler
+#def load_model_and_scaler():
+def load_model():
+    with open("model.pkl", "rb") as f1, open("scaler.pkl", "rb") as f2:
+        return pickle.load(f1), pickle.load(f2)
+
+model = load_model
+scaler = ()
+
+#model, scaler = load_model_and_scaler()
+
+def predict(df, endpoint="simple"):
+    # Scaling
+    scaled_df = scaler.transform(df)
+
+    # Prediction
+    prediction = model.predict_proba(scaled_df)
+
+    highest_proba = prediction.max(axis=1)
+
+    predicted_labels = ["Patient does not have sepsis" if i == 0 else f"Patient has sepsis" for i in highest_proba]
+    print(f"Predicted labels: {predicted_labels}")
+    print(highest_proba)
+
+    response = []
+    for label, proba in zip(predicted_labels, highest_proba):
+        output = {
+            "prediction": label,
+            "probability of prediction": str(round(proba * 100)) + '%'
+        }
+        response.append(output)
+
+    return response
+
+
+class Patient(BaseModel):
+    Blood_Work_R1: int
+    Blood_Pressure: int
+    Blood_Work_R3: int
+    BMI: float
+    Blood_Work_R4: float
+    Patient_age: int
+
+class Patients(BaseModel):
+    all_patients: list[Patient]
+
+    @classmethod
+    def return_list_of_dict(cls, patients: "Patients"):
+        patient_list = []
+        for patient in patients.all_patients:
+            patient_dict = patient.dict()
+            patient_list.append(patient_dict)
+        return patient_list
+    
+# Endpoints
+# Root Endpoint
+@app.get("/")
+def root():
+    return {"API": "This is an API for sepsis prediction."}
+
+# Prediction endpoint
+@app.post("/predict")
+def predict_sepsis(patient: Patient):
+    # Make prediction
+    data = pd.DataFrame(patient.dict(), index=[0])
+    parsed = predict(df=data)
+    return {"output": parsed}
+
+# Multiple Prediction Endpoint
+@app.post("/predict_multiple")
+def predict_sepsis_for_multiple_patients(patients: Patients):
+    """Make prediction with the passed data"""
+    data = pd.DataFrame(Patients.return_list_of_dict(patients))
+    parsed = predict(df=data, endpoint="multi")
+    return {"output": parsed}
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", reload=True)