Input path: /home/debian/html/nutritwin/output_llm/674b5ca9db0e4/input.json
Output path: /home/debian/html/nutritwin/output_llm/674b5ca9db0e4/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":"samoussa", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"},
    {"name":"nems", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"}
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {"name":"samoussa", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"},
    {"name":"nems", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"}
]
------------------------------------------------------

------------------------ After simplification ------------------------
[    {"name":"samoussa", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"},    {"name":"nems", "quantity":"2", "weight":"~50g par pièce", "cooking":"frit", "brand":"", "company":"", "type":"entrée ou snack", "time":"grignotage", "event":"declaration"}]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'samoussa', 'quantity': '2', 'weight': '~50g par pièce', 'cooking': 'frit', 'brand': '', 'company': '', 'type': 'entrée ou snack', 'time': 'grignotage', 'event': 'declaration'}, {'name': 'nems', 'quantity': '2', 'weight': '~50g par pièce', 'cooking': 'frit', 'brand': '', 'company': '', 'type': 'entrée ou snack', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'samoussa', 'quantity': '2', 'weight': '~50g par pièce', 'cooking': 'frit', 'brand': '', 'company': '', 'type': 'entrée ou snack', '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 '% samoussa %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Samoussa -  samoussa  -  -  - 1248 -  -  - KCA#c87e8cf549082512cff5f3d85d586cf3
Samossa ou Samoussa -  samossa ou samoussa  -  -  - 0 -  -  - CIQ#85d86bdfc26563e098403da941e1968d
----------------------------------------------------


----------- result to be analyzed -----------
{'name': 'nems', 'quantity': '2', 'weight': '~50g par pièce', 'cooking': 'frit', 'brand': '', 'company': '', 'type': 'entrée ou snack', '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 '% nem %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Nem ou Pâté Impérial -  nem ou pate imperial  -  -  - 2810 -  -  - CIQ#5c0c6f1ae0916138681ae4edcc4e4419
Nem ou Pâté Impérial -  nem ou pate imperial  - au porc -  - 0 -  -  - CIQ#13d934ff60e4ec9a23b32d57106491b0
Nem ou Pâté Impérial -  nem ou pate imperial  - au poulet -  - 0 -  -  - CIQ#5a7c515cd194dda0f3b7934695d1b78e
Nem ou Pâté Impérial -  nem ou pate imperial  - aux crevettes et/ou au crabe -  - 0 -  -  - CIQ#4dc0e233f94dbc011d1597427b5d55b7
Sauce pour Nems à Base de Nuoc-mam Dilué -  sauce pour nem base de nuoc mam dilue  -  -  - 0 -  -  - CIQ#68d76cc02b44928be15871a78b9d94f0
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Samoussa', 'normName': ' samoussa ', 'comment': '', 'normComment': '', 'rank': 1248, 'id': 'KCA#c87e8cf549082512cff5f3d85d586cf3', 'quantity': '2', 'quantityLem': '2', 'pack': ['SAM.w35'], 'type': 'entrée ou snack', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'grignotage', 'event': 'declaration', 'serving': 'SAM-200', 'posiNormName': 0}, {'name': 'Nem ou Pâté Impérial', 'normName': ' nem ou pate imperial ', 'comment': '', 'normComment': '', 'rank': 2810, 'id': 'CIQ#5c0c6f1ae0916138681ae4edcc4e4419', 'quantity': '2', 'quantityLem': '2', 'pack': ['CYL.w60'], 'type': 'entrée ou snack', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'grignotage', 'event': 'declaration', 'serving': 'CYL-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.687882661819458}
----------------------------------------------------------------------------------
LLM CPU Time: 3.687882661819458