Input path: /home/debian/html/nutritwin/output_llm/674af7c1a341d/input.json
Output path: /home/debian/html/nutritwin/output_llm/674af7c1a341d/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 -----------------------------
```json
[
  {
    "name": "compote de pommes",
    "quantity": "portion",
    "weight": "200",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "dessert ou goûter",
    "time": "petit-déjeuner ou grignotage",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "compote de pommes",
    "quantity": "portion",
    "weight": "200",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "dessert ou goûter",
    "time": "petit-déjeuner ou grignotage",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "compote de pommes",    "quantity": "portion",    "weight": "200",    "cooking": "",    "brand": "",    "company": "",    "type": "dessert ou goûter",    "time": "petit-déjeuner ou grignotage",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'compote de pommes', 'quantity': 'portion', 'weight': '200', 'cooking': '', 'brand': '', 'company': '', 'type': 'dessert ou goûter', 'time': 'petit-déjeuner ou grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'compote de pommes', 'quantity': 'portion', 'weight': '200', 'cooking': '', 'brand': '', 'company': '', 'type': 'dessert ou goûter', 'time': 'petit-déjeuner ou 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 '% compote de pomme %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Compote de Pomme -  compote de pomme  -  -  - 18523 -  -  - CIQ#b03321a86ac3c7bdbf1fdd06152fe3a4
Compote de Pomme -  compote de pomme  - appertisée -  - 26 -  -  - KCA#4abbe95b97dc51099f7c74281eb56b7e
Compote de Pomme -  compote de pomme  - allégée en sucres -  - 0 -  -  - CIQ#4c6f85d09f34c5881f71baaee4367d62
----------------------------------------------------

ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
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': 'Compote de Pomme', 'normName': ' compote de pomme ', 'comment': '', 'normComment': '', 'rank': 18523, 'id': 'CIQ#b03321a86ac3c7bdbf1fdd06152fe3a4', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['RAM.w200', 'YA5.w120'], 'type': 'dessert ou goûter', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner ou grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.6468191146850586}
----------------------------------------------------------------------------------
LLM CPU Time: 2.6468191146850586