Update main.py

main.py

@@ -8,18 +8,16 @@ 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')
+model = joblib.load('model.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)
@@ -39,17 +37,8 @@ def predict(df, endpoint='simple'):
     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
@@ -59,10 +48,7 @@ class Patient(BaseModel):
     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():