Keywords: ChatGPT, COS, Lookup Table, IRIS, AI
Purpose
It has been more than a month since I imagined how ChatGPT would automate some daily engineering works. Time seems warped these days. GPT-4 came out last week, able to process 32K tokens (around 25K words on average) and handle image inputs, compared with ChatGPT's 4K token limit. It seems last month felt as if last year, and last year as last century. Before sending ChatGPT away, I just wish to share some quick appreciations on how it actually helped, already, and to recap some possible quick wins or gaps out of GPTs .
Task(s)
One of the simplest possible tasks could be a Lookup table, right?
Occasionally you might also have to turn tediously long CSV or Excel code tables into an Ensemble/I4H Lookup table in XML, manually. Then clinical teams changed it, then changed again and again.
Can ChatGPT help? Can I just tell it what I want, then he gave me a tool to just turn that messy long CSV into a XML lookup table? Here below is a quick try.
Tests:
The following prompt is sent in ChatGPT:
Prompt:
You are a program to automatically convert a CSV file into a XML lookup table file.
The input is a comma delimited CSV file with 3 data columns , such as:
Blood Science ,XCOM,CANCELLED/REJECTED SAMPLE
Blood Science ,ADX ,ADD ON TEST COMMENT
Blood Transfusion,DARA,ALERT FOR DARA/CAMELLIA
Blood Transfusion, BT1 ,REJECT SAMPLE UNLABELLED
The output should be a XML file in the form of:
<?xml version="1.0"?>
<lookupTable>
<entry table="HIE.ICE.FilterOBR4" key="XCOM">Blood Science</entry>
<entry table="HIE.ICE.FilterOBR4" key="ADX ">Blood Science</entry>
<entry table="HIE.ICE.FilterOBR4" key="DARA">Blood Transfusion</entry>
<entry table="HIE.ICE.FilterOBR4" key="BT1">Blood Transfusion</entry>
</lookupTable>
Please note: the sequence of the XML attribute in each root should be kept as they are
ChatGPT:
Here's a Python script that reads in the CSV file, processes it, and outputs the resulting XML file:
import csv
def convert_csv_to_xml(input_file, output_file):
"""
Convert a CSV file to an XML lookup table file.
Parameters:
input_file (str): Path to the input CSV file.
output_file (str): Path to the output XML file.
Returns:
int: 0 if the conversion was successful, 1 if there was an error.
"""
try:
lookup_table = {}
with open(input_file, "r") as csvfile:
csvreader = csv.reader(csvfile)
for row in csvreader:
category = row[0].strip()
key = row[1].strip()
value = row[2].strip()
lookup_table[key] = category
with open(output_file, "w") as xmlfile:
xmlfile.write('<?xml version="1.0"?>\n')
xmlfile.write('<lookupTable>\n')
for key in lookup_table:
xmlfile.write(f'<entry table="HIE.ICE.FilterOBR4" key="{key}">{lookup_table[key]}</entry>\n')
xmlfile.write('</lookupTable>\n')
return 0
except Exception as e:
print("Error:", e)
return 1
This script first reads in the CSV file using the csv module and creates a dictionary to hold the lookup table data. It then processes each row of the CSV file and adds the relevant data to the dictionary.
Finally, the script generates the XML file by iterating through the dictionary and writing the appropriate XML tags and attributes to the output file. Note that the f string syntax is used to dynamically insert the key and category data into the XML tags.
So it gave a Python code by default, without specific asking. Fine, I ran it briefly in a Jupyter notebook, it checked out, except a tiny tweak and a bit local testing. It also stripped out the blanks. It gave a tool allowing me handling really lengthy CSVs, without being limited to token size.
In this specific case, I'd say it increased my productivity about 10+ times, particularly when you need to handle revised CSVs quite a few times over next few weeks.
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