Hi,
It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑
1st of all for building a custom REST api service is easy by extending the %CSP.REST
Creating a REST Service Manually
Let's Start !😂
1. Create a class datagen.restservice which extends %CSP.REST
Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json"
}
2. Add a function genpatientcsv() to generate the patient data, and package it into csv string
Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json"
ClassMethod genpatientcsv() As %String [ Language = python ]
{
# w ##class(datagen.restservice).genpatientcsv()
# python.exe -m pip install faker
# python.exe -m pip install pandas
from faker import Faker
import random
import pandas as pd
from io import StringIO
# Initialize Faker
fake = Faker()
def generate_patient(patient_id):
return {
"PatientID": patient_id,
"Name": fake.name(),
"Gender": random.choice(["Male", "Female"]),
"DOB": fake.date_of_birth(minimum_age=0, maximum_age=100).strftime("%Y-%m-%d"),
"City": fake.city(),
"Phone": fake.phone_number(),
"Email": fake.email(),
"BloodType": random.choice(["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"]),
"Diagnosis": random.choice(["Hypertension", "Diabetes", "Asthma", "Healthy", "Flu"]),
"Height_cm": round(random.uniform(140, 200), 1),
"Weight_kg": round(random.uniform(40, 120), 1),
}
# Generate 10 patients
patients = [generate_patient(i) for i in range(1, 11)]
# Convert to DataFrame
df = pd.DataFrame(patients)
# Convert to CSV string (without saving to file)
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
csv_string = csv_buffer.getvalue()
return csv_string
}
}
you may test the function in the terminal by typing
w ##class(datagen.restservice).genpatientcsv()
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3. Add a function GetMyDataCSV() in Python for populating the csv string as a csv file and then output through the REST api service. This can be achieve by,
3.1. calling the patient data generate function to get the csv string
3.2. set the %response.ContentType = "text/csv"
3.3. set the header "Content-Disposition" value to "attachment; filename=mydata.csv"
3.4. write the generated csv string as output
remember to pip install the related libraries
Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json"
ClassMethod GetMyDataCSV() As %Status
{
Set tCSVString = ##class(datagen.restservice).genpatientcsv()
Set %response.ContentType = "text/csv"
Do %response.SetHeader("Content-Disposition","attachment; filename=mydata.csv")
W tCSVString
Quit $$$OK
}
ClassMethod genpatientcsv() As %String [ Language = python ]
{
# w ##class(datagen.restservice).genpatientcsv()
# python.exe -m pip install faker
# python.exe -m pip install pandas
from faker import Faker
import random
import pandas as pd
from io import StringIO
# Initialize Faker
fake = Faker()
def generate_patient(patient_id):
return {
"PatientID": patient_id,
"Name": fake.name(),
"Gender": random.choice(["Male", "Female"]),
"DOB": fake.date_of_birth(minimum_age=0, maximum_age=100).strftime("%Y-%m-%d"),
"City": fake.city(),
"Phone": fake.phone_number(),
"Email": fake.email(),
"BloodType": random.choice(["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"]),
"Diagnosis": random.choice(["Hypertension", "Diabetes", "Asthma", "Healthy", "Flu"]),
"Height_cm": round(random.uniform(140, 200), 1),
"Weight_kg": round(random.uniform(40, 120), 1),
}
# Generate 10 patients
patients = [generate_patient(i) for i in range(1, 11)]
# Convert to DataFrame
df = pd.DataFrame(patients)
# Convert to CSV string (without saving to file)
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
csv_string = csv_buffer.getvalue()
return csv_string
}
}
4. Add the route to this function and compile the class
Class datagen.restservice Extends %CSP.REST
{
Parameter CONTENTTYPE = "application/json"
XData UrlMap [ XMLNamespace = "http://www.intersystems.com/urlmap" ]
{
<Routes>
<Route Url="/export/patientdata" Method="GET" Call="GetMyDataCSV"/>
</Routes>
}
ClassMethod GetMyDataCSV() As %Status
{
Set tCSVString = ##class(datagen.restservice).genpatientcsv()
Set %response.ContentType = "text/csv"
Do %response.SetHeader("Content-Disposition","attachment; filename=mydata.csv")
W tCSVString
Quit $$$OK
}
ClassMethod genpatientcsv() As %String [ Language = python ]
{
# w ##class(datagen.restservice).genpatientcsv()
# python.exe -m pip install faker
# python.exe -m pip install pandas
from faker import Faker
import random
import pandas as pd
from io import StringIO
# Initialize Faker
fake = Faker()
def generate_patient(patient_id):
return {
"PatientID": patient_id,
"Name": fake.name(),
"Gender": random.choice(["Male", "Female"]),
"DOB": fake.date_of_birth(minimum_age=0, maximum_age=100).strftime("%Y-%m-%d"),
"City": fake.city(),
"Phone": fake.phone_number(),
"Email": fake.email(),
"BloodType": random.choice(["A+", "A-", "B+", "B-", "AB+", "AB-", "O+", "O-"]),
"Diagnosis": random.choice(["Hypertension", "Diabetes", "Asthma", "Healthy", "Flu"]),
"Height_cm": round(random.uniform(140, 200), 1),
"Weight_kg": round(random.uniform(40, 120), 1),
}
# Generate 10 patients
patients = [generate_patient(i) for i in range(1, 11)]
# Convert to DataFrame
df = pd.DataFrame(patients)
# Convert to CSV string (without saving to file)
csv_buffer = StringIO()
df.to_csv(csv_buffer, index=False)
csv_string = csv_buffer.getvalue()
return csv_string
}
}
OK, now our code is ready. 😁 The next thing is to add the REST service to the web application
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Input your Path, Namespace, and Rest service class name, and then Save
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Assign the proper application role to this web application (because I am lazy, I just simply assign %All for testing 🤐)
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OK everything is ready!!😁 Let's test the REST api!!!😂
Input the following path in a bowser
http://localhost/irishealth/csp/mpapp/export/patientdata
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It trigger a file download, the file name is mydata.csv😗
Let's check the file 😊
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Yeah!!! Work well!! 😁😁
Thank you so much for the reading. 😉
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