Input path: /home/debian/html/nutritwin/output_llm/67213ecb0594f/input.json
Output path: /home/debian/html/nutritwin/output_llm/67213ecb0594f/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": "soda",
    "quantity": "1 verre",
    "weight": "250",
    "cooking": "",
    "brand": "Coca-Cola",
    "company": "The Coca-Cola Company",
    "type": "boisson gazeuse",
    "time": "",
    "event": "declaration"
  }
]

Note: L'heure actuelle n'est pas affichée dans l'image, donc le champ "time" ne peut pas être déterminé. De plus, sans contexte supplémentaire, il est difficile de déterminer à quel moment de la journée cette boisson est consommée (petit-déjeuner, déjeuner, grignotage ou dîner).
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
  {
    "name": "soda",
    "quantity": "1 verre",
    "weight": "250",
    "cooking": "",
    "brand": "Coca-Cola",
    "company": "The Coca-Cola Company",
    "type": "boisson gazeuse",
    "time": "",
    "event": "declaration"
  }
]

Note: L'heure actuelle n'est pas affichée dans l'image, donc le champ "time" ne peut pas être déterminé. De plus, sans contexte supplémentaire, il est difficile de déterminer à quel moment de la journée cette boisson est consommée (petit-déjeuner, déjeuner, grignotage ou dîner).
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "soda",    "quantity": "1 verre",    "weight": "250",    "cooking": "",    "brand": "Coca-Cola",    "company": "The Coca-Cola Company",    "type": "boisson gazeuse",    "time": "",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'soda', 'quantity': '1 verre', 'weight': '250', 'cooking': '', 'brand': 'Coca-Cola', 'company': 'The Coca-Cola Company', 'type': 'boisson gazeuse', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'soda', 'quantity': '1 verre', 'weight': '250', 'cooking': '', 'brand': 'Coca-Cola', 'company': 'The Coca-Cola Company', 'type': 'boisson gazeuse', 'time': '', '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 '% soda %' AND V_NormTrademark LIKE '%coca cola%' 


------------- Found solution (max 20) --------------
Soda -  soda  -  - The Coca-Cola Company - 0 - 5000112609608 - 5000112609608 - OFF#13d7eb865ddc60c5bc7c14a3060ce9c7
Soda -  soda  -  - The Coca-Cola Company - 0 - 5000112634761 - 5000112609608 - OFF#9d35d64bcebd2b1370bfdf4368c60ef5
Royal Soda Orange -  royal soda orange  -  - The Coca-Cola Company - 0 - 3292090000306 - 3292090000306 - OFF#09c9e4fbbd3b3a1d611540c4e028070c
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Soda', 'normName': ' soda ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#13d7eb865ddc60c5bc7c14a3060ce9c7', 'quantity': '1 verre', 'quantityLem': '1 verre', 'pack': ['VX1', 'BI4', 'VA2', 'VA3', 'GOB', 'C3B', 'C33', 'C15', 'SOD', 'VA4', 'VFF'], 'type': 'boisson gazeuse', 'gtin': '5000112609608', 'gtinRef': '5000112609608', 'brand': 'The Coca-Cola Company', 'time': '', 'event': 'declaration', 'serving': 'BI4-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 6.533581256866455}
----------------------------------------------------------------------------------
LLM CPU Time: 6.533581256866455