Input path: /home/debian/html/nutritwin/output_llm/673b89ea6d6d3/input.json
Output path: /home/debian/html/nutritwin/output_llm/673b89ea6d6d3/output.json
Input text:
DB path: __deriveddata__/DerivedObjects/Data/KcalMeDB_fr.sl3
Picto path: __deriveddata__/DerivedObjects/Data/PictoMatcherNetNG_fr.json
Sport grounding path: __deriveddata__/DerivedObjects/Data/DerivedSportMET.json
==================================================================================================================================
Prompt from user:
==================================================================================================================================
###########################################
# For image extraction, GPT4 is used #
###########################################
==================================== Prompt =============================================
In the image, identify all the foods and the beverages.
For each of them, identify the "name", the "type", the "quantity", if it exists, the "brand" and the "cooking" mode.
"Portions", like "tranche", are quantities.
Ignore what it is not connected to nutrition, beverage or food.
When the "brand" is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "".
Identify what "type" of food.
Identify the "company" to which the "brand" belongs.
Estimate the "weight" in grams or centiliters for each result.
Identify the time is the current time, map it on the closest case: "petit-déjeuner", "déjeuner", "grignotage" or "dîner".
When the "name" has synonyms, use the most common name, example: "yaourt" is more common than "yogourt".
Format the result for each ingredient of food & beverage in french in JSON in an array of tuples {"name":, "quantity":, "weight":, "cooking":, "brand":, "company":, "type":, "time":, "event": "declaration"}.
=========================================================================================
Image recognition....
------------------------------ LLM Raw response -----------------------------
[
{
"name": "baguette",
"quantity": "portion",
"weight": "50",
"cooking": "cuit au four",
"brand": "",
"company": "",
"type": "pain",
"time": "grignotage",
"event": "declaration"
}
]
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
[
{
"name": "baguette",
"quantity": "portion",
"weight": "50",
"cooking": "cuit au four",
"brand": "",
"company": "",
"type": "pain",
"time": "grignotage",
"event": "declaration"
}
]
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "baguette", "quantity": "portion", "weight": "50", "cooking": "cuit au four", "brand": "", "company": "", "type": "pain", "time": "grignotage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'baguette', 'quantity': 'portion', 'weight': '50', 'cooking': 'cuit au four', 'brand': '', 'company': '', 'type': 'pain', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'baguette', 'quantity': 'portion', 'weight': '50', 'cooking': 'cuit au four', 'brand': '', 'company': '', 'type': 'pain', 'time': 'grignotage', 'event': 'declaration'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% baguette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Baguette - baguette - - - 13304 - - - KCA#4c58eb37d72a06c3eb6b4eca05a7eafc
Baguette Beurrée - baguette beurree - - - 6728 - - - KCA#81e38c2ee900b3581038d641b4a91c7f
Baguette Beurre Confiture - baguette beurre confiture - - - 5301 - - - KCA#d399b90a645a52039b2f409debeaa686
Pain, Baguette Courante - pain baguette courante - - - 1505 - - - KCA#b8216428789ad27dd11c8e9619cd3ad8
Pain, Baguette sans Sel - pain baguette san sel - - - 144 - - - KCA#9792a779f917b0a8bf1fc9c776628d0a
Sandwich Baguette - sandwich baguette - - - 0 - - - CIQ#ecdbce2254ce082246ccea95b54322d3
Sandwich Baguette - sandwich baguette - jambon, beurre - - 544 - - - CIQ#bd804df922badefbc8215232b9b741aa
Sandwich Baguette - sandwich baguette - salami, beurre - - 59 - - - CIQ#92b9f1c35fd21237d9716ba633faf6c3
Sandwich Baguette - sandwich baguette - jambon emmental - - 0 - - - CIQ#a3044be4730437e3137525aaa8469e38
Sandwich Baguette - sandwich baguette - pâté, cornichons - - 138 - - - CIQ#dffdf1e5117ae64f00c22627ab3670f2
Sandwich Baguette - sandwich baguette - camembert, beurre - - 23 - - - CIQ#0080a72a2d54a3ea5e04c0c631ac01fd
Sandwich Baguette - sandwich baguette - saucisson, beurre - - 0 - - - CIQ#64a51f36b8fcf7fb6aa69713d78a7477
Sandwich Baguette - sandwich baguette - saumon fumé, beurre - - 191 - - - CIQ#f319acba3059dd568c3ec0b09ffee8cd
Sandwich Baguette - sandwich baguette - thon, maïs, crudités - - 0 - - - CIQ#6bc3fa7c33408c471a521687ea57c022
Sandwich Baguette - sandwich baguette - jambon, emmental, beurre - - 5174 - - - CIQ#d4b56c51ab3ed4856726dc5540397da5
Sandwich Baguette - sandwich baguette - merguez, ketchup moutarde - - 71 - - - CIQ#5f4dda655b3b11f243dc5af84eb97c1f
Sandwich Baguette - sandwich baguette - crudités diverses, mayonnaise - - 17 - - - CIQ#80a019abb0e07979e1fadd760efb9be0
Sandwich Baguette - sandwich baguette - oeuf, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#ce1d206be86434bec3c26d455f9689e4
Sandwich Baguette - sandwich baguette - porc, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#8e8c84ec0b67513a1590bfedccec16eb
Sandwich Baguette - sandwich baguette - thon, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#2216cbeb818ce287e644567239e12d90
----------------------------------------------------
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Baguette', 'normName': ' baguette ', 'comment': '', 'normComment': '', 'rank': 13304, 'id': 'KCA#4c58eb37d72a06c3eb6b4eca05a7eafc', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['BAG.w25', 'PAI.w30'], 'type': 'pain', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.333042621612549}
----------------------------------------------------------------------------------
LLM CPU Time: 3.333042621612549