Upload main.py

main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import pandas as pd
+import numpy as np
+import uvicorn
+import os
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+    
+""" Creating the FastAPI Instance. i.e. foundation for our API,
+which will be the main part of our project"""
+
+app = FastAPI(title="API")
+
+
+"""We load a machine learning model and a scaler that help us make predictions based on data."""
+model = joblib.load('gbc.pkl',mmap_mode='r')
+scaler = joblib.load('scaler.pkl',mmap_mode='r')
+
+"""We define a function that will make predictions using our 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 "Patient has Sepsis" for i in 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
+
+
+"""We create models for the data that our API will work with.
+We define what kind of information the data will have.
+It's like deciding what information we need to collect and how it should be organized."""
+
+
+"""These classes define the data models used for API endpoints.
+The 'Patient' class represents a single patient's data,
+and the 'Patients' class represents a list of patients' data.
+The Patients class also includes a class method return_list_of_dict()
+that converts the Patients object into a list of dictionaries"""
+
+class Patient(BaseModel):
+    Blood_Work_R1: float
+    Blood_Pressure: float
+    Blood_Work_R3: float
+    BMI: float
+    Blood_Work_R4: float
+    Patient_age: int
+
+
+"""Next block of code defines different parts of our API and how it responds to different requests.
+It sets up a main page with a specific message, provides a checkup endpoint to receive
+optional parameters, and sets up prediction endpoints to receive medical data for making predictions,
+either for a single patient or multiple patients."""
+
+@app.get("/")
+def root():
+    return {"API": "This is an API for sepsis prediction."}
+
+# Prediction endpoint (Where we will input our features)
+@app.post("/predict")
+def predict_sepsis(patient: Patient):
+
+    # Make prediction
+    data = pd.DataFrame(patient.dict(), index=[0])
+    scaled_data = scaler.transform(data)
+    parsed = predict(df=scaled_data)
+    return {"output": parsed}
+
+
+if __name__ == "__main__":
+    os.environ["DEBUG"] = "True"  # Enable debug mode
+    uvicorn.run("main:app", reload=True)