ChatGPT - is there a use within RAFAC?

Non Air Cadets Computers and stuff
82

It was a fairly simple part, but I literally gave it one model and one prompt and the info it gave back was pretty reasonable.

I’m now trying to get it to do some fariyl complex PDF edits. We have some parts that are over 3m long, and our bigest machine is 2m. They have loads of holes in. We’ve agreed with client to cut them in an area near the middle where there are less holes and weld back together. I’m trying to get it to take 1 pdf drawing and create 2 new parts cut in a sensible location.

It can sort of do it. But not quite. With a bit of tinkering I think I could get it to work.

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83

I am mulling over whether GenAI is able to parse (if that’s the right word) cadet measurements into the nearest set of Nato sizes.

Has anyone tried this?

84

That should be easy, I’d have thought. I don’t suppose you have some sample size data?

85

Great idea.

86

Wait out

87

I don’t have the JSP and wouldn’t put it into a GenAI anyway but this link seems to cover the main sizes (but not the new smaller sizes eg 150cm height shirts)

https://www.johnsonsofleeds.co.uk/british-army-size-chart/

So my thinking was if a cadet submits a form saying they are say 168h and whatever chest it will pick the right size (ie 170/80 or 170/96) but will be clever enough not to pick a 160/80.

This could be done with a spreadsheet of course but it needs some quite complex formulae.

88

I meant some sample cadet data. I’ve already started, and it’s pulled the nato sizes its self. Just need to test it!

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89

Let me see what I can do, I might need to PM you

90

Anonymise it. No names! Just some sizes. And I’ll have a play.

91

Thing I’ve seen with a lot of the published nato sizes (by surplus places) is they don’t match the actual sizes on the garments.

92

I have a uniform size list already from a while ago. :slight_smile:

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93

I dont think you even need AI for it.

Some medium complexity python could probably do it. Therefore in theory it could be a default element of cadet portal/units

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94

Try 91 w 75 inside leg

175h 90 chest

76w 78 inside leg

160h 78 chest

The first one I pulled up, the parent had of course used inches instead of cm, resulting in a very small cadet

95

You probably could, my coding never went beyond filling a screen with rude words using BASIC

96

This is where ChatGPT comes in!

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97

Where I think Ai (vs just code) would help is to suggest the best fit and next best fit based on the garment sizes.

It would need to know that PCS has more leeway than No2 dress too.

I’m using Forms to collect the sizing so in either Ai or Python the data capture is automated.

98

I mean, use AI to write the code that does the work.

I’m currently trying to get it to give me a spreadsheet where on one sheet you put in all the cadets and size, and it spits out the appropriate nato sizes that need ordering. In my case, going for a normally round up approach.

I think it’s doable without any macros, just some well written excel formula!

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99

Chat GPT could probably write the code to do it. If you provide a reference table

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100

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Already on it :wink:

If I get it working I’ll send it over, but need to cook dinner first!

101

RAF Uniform NSN Finder

Based on British Army size chart from Johnsons of Leeds

Step 1: Define NSN data for uniform items

This is an example – you would replace with the actual mapping from the chart

uniform_data = {
“beret”: [
{“size_cm”: 54, “nsn”: “8405-99-123-4567”},
{“size_cm”: 55, “nsn”: “8405-99-123-4568”},
{“size_cm”: 56, “nsn”: “8405-99-123-4569”},
],
“shirt”: [
{“collar_in”: 14, “nsn”: “8405-99-234-5678”},
{“collar_in”: 15, “nsn”: “8405-99-234-5679”},
{“collar_in”: 16, “nsn”: “8405-99-234-5680”},
],
“trousers”: [
{“waist_in”: 30, “leg_in”: 30, “nsn”: “8405-99-345-6789”},
{“waist_in”: 32, “leg_in”: 32, “nsn”: “8405-99-345-6790”},
{“waist_in”: 34, “leg_in”: 32, “nsn”: “8405-99-345-6791”},
]
}

Step 2: Function to find best match based on closest measurement

def find_best_match(item_list, criteria):
best_match = None
smallest_diff = float(“inf”)
for item in item_list:
diff = 0
for key, value in criteria.items():
if key in item:
diff += abs(item[key] - value)
if diff < smallest_diff:
smallest_diff = diff
best_match = item
return best_match

Step 3: Get user measurements

print(“Enter your measurements:”)
height = int(input("Height (cm): "))
head_size = int(input("Head size (cm): "))
collar_size = float(input("Collar size (inches): "))
chest_size = int(input("Chest size (inches): "))
waist_size = int(input("Waist size (inches): "))
seat_size = int(input("Seat size (inches): "))
leg_size = int(input("Leg length (inches): "))

Step 4: Match NSNs

beret_match = find_best_match(uniform_data[“beret”], {“size_cm”: head_size})
shirt_match = find_best_match(uniform_data[“shirt”], {“collar_in”: collar_size})
trousers_match = find_best_match(uniform_data[“trousers”], {“waist_in”: waist_size, “leg_in”: leg_size})

Step 5: Output recommendations

print(“\nBest NSN recommendations for your measurements:”)
print(f"Beret: {beret_match[‘nsn’]} (Size {beret_match[‘size_cm’]} cm)“)
print(f"Shirt: {shirt_match[‘nsn’]} (Collar {shirt_match[‘collar_in’]} in)”)
print(f"Trousers: {trousers_match[‘nsn’]} (Waist {trousers_match[‘waist_in’]} in, Leg {trousers_match[‘leg_in’]} in)")