Input path: /home/debian/html/nutritwin/output_llm/67346c9378335/input.json
Output path: /home/debian/html/nutritwin/output_llm/67346c9378335/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": "œufs brouillés", 
    "quantity": "portion", 
    "weight": "100", 
    "cooking": "brouillés", 
    "brand": "", 
    "company": "", 
    "type": "plat principal", 
    "time": "petit-déjeuner", 
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "œufs brouillés", 
    "quantity": "portion", 
    "weight": "100", 
    "cooking": "brouillés", 
    "brand": "", 
    "company": "", 
    "type": "plat principal", 
    "time": "petit-déjeuner", 
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "œufs brouillés",     "quantity": "portion",     "weight": "100",     "cooking": "brouillés",     "brand": "",     "company": "",     "type": "plat principal",     "time": "petit-déjeuner",     "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'œufs brouillés', 'quantity': 'portion', 'weight': '100', 'cooking': 'brouillés', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'œufs brouillés', 'quantity': 'portion', 'weight': '100', 'cooking': 'brouillés', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'petit-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 '% oeuf brouille %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Oeufs Brouillés -  oeuf brouille  -  -  - 5704 -  -  - KCA#e7821dbd6e4eed7e40207749566215c6
Oeufs Brouillés au Parme -  oeuf brouille parme  -  -  - 21 -  -  - KCA#46f4f89df78ef4f353027ad54bc58fb2
Oeufs Brouillés au Piment -  oeuf brouille piment  -  -  - 13 -  -  - KCA#206dad6382c9ccfc755dff2b1ea88b47
Oeufs Brouillés à la Bressane -  oeuf brouille bressane  -  -  - 7 -  -  - KCA#3977cd58d40def04a5618de6e2cfc76a
Croque Monsieur aux Oeufs Brouillés -  croque monsieur au oeuf brouille  -  -  - 3 -  -  - KCA#0d6d602aab5ed85a471d72a5fba11639
----------------------------------------------------

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': 'Oeufs Brouillés', 'normName': ' oeuf brouille ', 'comment': '', 'normComment': '', 'rank': 5704, 'id': 'KCA#e7821dbd6e4eed7e40207749566215c6', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['OE2.w60', 'OEU.w60'], 'type': 'plat principal', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.070000886917114}
----------------------------------------------------------------------------------
LLM CPU Time: 4.070000886917114