Input path: /home/debian/html/nutritwin/output_llm/671e2c98c0520/input.json
Output path: /home/debian/html/nutritwin/output_llm/671e2c98c0520/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": "Bacon fumé",
    "quantity": "paquet",
    "weight": "",
    "cooking": "fumé",
    "brand": "Herta",
    "company": "Nestlé",
    "type": "charcuterie",
    "time": "",
    "event": "declaration"
  }
]
```

Note: The "weight" and "time" (related to meal time) can't be precisely determined from the image provided. The weight is not visible in the image, and the meal time is not specified, so it has been left empty.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "Bacon fumé",
    "quantity": "paquet",
    "weight": "",
    "cooking": "fumé",
    "brand": "Herta",
    "company": "Nestlé",
    "type": "charcuterie",
    "time": "",
    "event": "declaration"
  }
]
```

Note: The "weight" and "time" (related to meal time) can't be precisely determined from the image provided. The weight is not visible in the image, and the meal time is not specified, so it has been left empty.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "Bacon fumé",    "quantity": "paquet",    "weight": "",    "cooking": "fumé",    "brand": "Herta",    "company": "Nestlé",    "type": "charcuterie",    "time": "",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Bacon fumé', 'quantity': 'paquet', 'weight': '', 'cooking': 'fumé', 'brand': 'Herta', 'company': 'Nestlé', 'type': 'charcuterie', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'Bacon fumé', 'quantity': 'paquet', 'weight': '', 'cooking': 'fumé', 'brand': 'Herta', 'company': 'Nestlé', 'type': 'charcuterie', '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 '% bacon fume %' AND V_NormTrademark LIKE '%herta%' 


------------- Found solution (max 20) --------------
Bacon Fumé -  bacon fume  -  - Herta - 0 - 2154250454286 - 2154250454286 - OFF#d83c7e36712e21c16745b7e90cacc710
Bacon Fumé -  bacon fume  -  - Herta - 0 - 7613034569769 - 2154250454286 - OFF#9b7d11c0b3914f9dba77293ef20274b8
Bacon Fumé -  bacon fume  -  - Herta - 0 - 3154230040286 - 2154250454286 - OFF#06b3063e67520d4dc578555fe3c52480
Filet de Bacon Fumé -  filet de bacon fume  -  - Herta - 0 - 3154230089209 - 3154230089209 - OFF#ebde9a5193122b953632084298f6fcfc
----------------------------------------------------

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': 'Bacon Fumé', 'normName': ' bacon fume ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#d83c7e36712e21c16745b7e90cacc710', 'quantity': 'paquet', 'quantityLem': 'paquet', 'pack': ['BAC.w10'], 'type': 'charcuterie', 'gtin': '2154250454286', 'gtinRef': '2154250454286', 'brand': 'Herta', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.9138917922973633}
----------------------------------------------------------------------------------
LLM CPU Time: 3.9138917922973633