Input path: /home/debian/html/nutritwin/output_llm/6743235b02d51/input.json
Output path: /home/debian/html/nutritwin/output_llm/6743235b02d51/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": "quiche",
    "quantity": "portion",
    "weight": "150",
    "cooking": "cuit",
    "brand": "",
    "company": "",
    "type": "plat principal",
    "time": "déjeuner",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "quiche",
    "quantity": "portion",
    "weight": "150",
    "cooking": "cuit",
    "brand": "",
    "company": "",
    "type": "plat principal",
    "time": "déjeuner",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "quiche",    "quantity": "portion",    "weight": "150",    "cooking": "cuit",    "brand": "",    "company": "",    "type": "plat principal",    "time": "déjeuner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'quiche', 'quantity': 'portion', 'weight': '150', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'quiche', 'quantity': 'portion', 'weight': '150', '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 '% quiche %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Quiche 'Maison' -  quiche maison  -  -  - 5344 -  -  - KCA#2b2f2d701dbd724bd4bac8763fb2725a
Quiche Lorraine -  quiche lorraine  -  -  - 1665 -  -  - CIQ#8950efe19268de254f0eb3cae952270f
Quiche aux Asperges -  quiche au asperge  -  -  - 125 -  -  - KCA#eeb2b8ff2884e7faa6ecb8b23952dd94
Quiche en Boulangerie -  quiche en boulangerie  -  -  - 956 -  -  - KCA#36df4e8befac1e3b224a43be6220b394
Quiche aux Poireaux Individuelle -  quiche au poireau individuelle  -  -  - 671 -  -  - KCA#f63c1559553bb964bee77b520d0443c5
Tarte ou Quiche Salée -  tarte ou quiche salee  -  -  - 0 -  -  - CIQ#c39e47efefaa591439370a84c211f920
----------------------------------------------------

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': "Quiche 'Maison'", 'normName': ' quiche maison ', 'comment': '', 'normComment': '', 'rank': 5344, 'id': 'KCA#2b2f2d701dbd724bd4bac8763fb2725a', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['QUI.w100.p6'], 'type': 'plat principal', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.23746395111084}
----------------------------------------------------------------------------------
LLM CPU Time: 3.23746395111084