Input path: /home/debian/html/nutritwin/output_llm/66feb68b0a8be/input.json
Output path: /home/debian/html/nutritwin/output_llm/66feb68b0a8be/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": "cassoulet", "quantity": "portion", "weight": "300", "cooking": "cuit", "brand": "", "company": "", "type": "plat principal", "time": "déjeuner", "event": "declaration"}]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[{"name": "cassoulet", "quantity": "portion", "weight": "300", "cooking": "cuit", "brand": "", "company": "", "type": "plat principal", "time": "déjeuner", "event": "declaration"}]
------------------------------------------------------

------------------------ After simplification ------------------------
[{"name": "cassoulet", "quantity": "portion", "weight": "300", "cooking": "cuit", "brand": "", "company": "", "type": "plat principal", "time": "déjeuner", "event": "declaration"}]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'cassoulet', 'quantity': 'portion', 'weight': '300', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'cassoulet', 'quantity': 'portion', 'weight': '300', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'déjeuner', '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 '% cassoulet %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Cassoulet -  cassoulet  -  -  - 29 -  -  - CIQ#e01947bd461f77b6f8ebff86f85f2d43
Cassoulet au Porc -  cassoulet porc  -  -  - 0 -  -  - CIQ#999832e2c7b188a4ec70f4c435447f27
Cassoulet Conserve -  cassoulet conserve  -  -  - 125 -  -  - KCA#97c7c138f43ab7bf29671497edc17019
Cassoulet au Canard ou Oie -  cassoulet canard ou oie  -  -  - 0 -  -  - CIQ#3a83a9d330dbcf828fb95ddc6c498fad
Lapin en Cassoulet -  lapin en cassoulet  -  -  - 2 -  -  - KCA#e75003e6a15bc8bba0b9c48bc33044d9
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Cassoulet', 'normName': ' cassoulet ', 'comment': '', 'normComment': '', 'rank': 29, 'id': 'CIQ#e01947bd461f77b6f8ebff86f85f2d43', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['APL.w250'], 'type': 'plat principal', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': 'APL-10', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.797614812850952}
----------------------------------------------------------------------------------
LLM CPU Time: 2.797614812850952