Input path: /home/debian/html/nutritwin/output_llm/6703c955a832f/input.json
Output path: /home/debian/html/nutritwin/output_llm/6703c955a832f/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": "pâtes",
    "quantity": "",
    "weight": "500",
    "cooking": "bouillies",
    "brand": "",
    "company": "",
    "type": "féculent",
    "time": "déjeuner",
    "event": "declaration"
  }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
  {
    "name": "pâtes",
    "quantity": "",
    "weight": "500",
    "cooking": "bouillies",
    "brand": "",
    "company": "",
    "type": "féculent",
    "time": "déjeuner",
    "event": "declaration"
  }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "pâtes",    "quantity": "",    "weight": "500",    "cooking": "bouillies",    "brand": "",    "company": "",    "type": "féculent",    "time": "déjeuner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pâtes', 'quantity': '', 'weight': '500', 'cooking': 'bouillies', 'brand': '', 'company': '', 'type': 'féculent', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'pâtes', 'quantity': '', 'weight': '500', 'cooking': 'bouillies', 'brand': '', 'company': '', 'type': 'féculent', '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 '% pate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Pâté -  pate  -  -  - 35 -  -  - CIQ#afa9f7f047da1f15de2883f037186a92
Pâtes -  pate  - sans gluten, à base de riz et maïs, à l'eau, non salées -  - 0 -  -  - CIQ#fbb4c57fdca55e795247628ccb5aecdd
Pâtes -  pate  - sans gluten, à base de lentilles corail, à l'eau, non salées -  - 0 -  -  - CIQ#b616881505a8dc3bb22f36ba73c591e5
Pâté Breton -  pate breton  -  -  - 0 -  -  - CIQ#7bf7cf124b0a4bd2e2ef3a9a0a499589
Pâtes Cuites -  pate cuite  -  -  - 40303 -  -  - KCA#5f79f58611165eed8a9639bfa123a9ca
Pâté de Foie -  pate de foie  -  -  - 754 -  -  - KCA#a5e2912dd9f9cde202e6768375fa2481
Pâté de Tête -  pate de tete  -  -  - 191 -  -  - KCA#f90aa2ff530cc5bc04459e1ca2ba4490
Pâtes Sèches -  pate seche  - aux oeufs, crues -  - 0 -  -  - CIQ#52cf76f71ceae840a6e8cfb7bb87401e
Pâtes Sèches -  pate seche  - sans gluten, crues -  - 0 -  -  - CIQ#a6df809c43c5e8ea99c2290e16e50a23
Pâtes Sèches -  pate seche  - au blé complet, crues -  - 0 -  -  - CIQ#2cd29b7b7d0a8beffb2a20bdcd5b67d9
Pâtes Sèches -  pate seche  - aux oeufs, non salées -  - 0 -  -  - CIQ#475f5a3e0ebed8ce058915c8c0e2488a
Pâtes Sèches -  pate seche  - sans gluten, non salées -  - 0 -  -  - CIQ#a83a046d5cb792a1634de34a8b103f8c
Pâtes Sèches -  pate seche  - au blé complet, non salées -  - 0 -  -  - CIQ#086a2b5c3417a99bed48fb94c6f8e347
Pâte d'Amande -  pate amande  -  -  - 753 -  -  - CIQ#7c0811ad432704e3560ead7d11dcc54b
Pâté de Lapin -  pate de lapin  -  -  - 228 -  -  - CIQ#cd9ac9416e8376ef0d33dc474b22d8d1
Pâte de Fruits -  pate de fruit  -  -  - 904 -  -  - CIQ#ddc417db85ad45f7b63c72987afd1efd
Pâté en Croûte -  pate en croute  -  -  - 69 -  -  - CIQ#e2118c3e025007fd1644c613af45b0cf
Pâté de Gibier -  pate de gibier  -  -  - 62 -  -  - CIQ#68811d74011dd1931c6725029c3ec0d8
Pâté Ardennais -  pate ardennai  -  -  - 33 -  -  - KCA#1c1510a6deb74a99fe2687d0ba87d678
Pâtes Fraîches -  pate fraiche  - aux oeufs, crues -  - 0 -  -  - CIQ#9afbc65919a12bd31e467b9e01a43777
----------------------------------------------------

ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
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': 'Pâté', 'normName': ' pate ', 'comment': '', 'normComment': '', 'rank': 35, 'id': 'CIQ#afa9f7f047da1f15de2883f037186a92', 'quantity': '', 'quantityLem': '', 'pack': ['TR5.w150'], 'type': 'féculent', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.7310752868652344}
----------------------------------------------------------------------------------
LLM CPU Time: 3.7310752868652344