import pandas as pd 
import numpy as np
import pickle
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

#Load the saved fine-tuned model
loaded_model = pickle.load(open(filename, 'rb'))

#load preprocessed live case data 
data = pd.read_csv(settings.DATA_PATH) #read_csv, read_parquet or read_sql as per the format of the data

#scale the data 
ss = StandardScaler()
features_scaled = ss.fit_transform(data)

#generate risk scores and save them to a csv
result = loaded_model.predict_proba(features)
pd.DataFrame(result).to_csv("path/to/file.csv") #Alternatively, you can save the results in the 'data' file you loaded in line 9 and then submit the file to CommCare using submit_data.py 
