Input path: /home/debian/html/nutritwin/output_llm/66f3a2d86f386/input.json
Output path: /home/debian/html/nutritwin/output_llm/66f3a2d86f386/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": "eau",
    "quantity": "1",
    "weight": "250",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson",
    "time": "indéterminé",
    "event": "declaration"
  }
]
```
Note: The weight is estimated based on the common capacity of a mug which is approximately 250 milliliters. The exact time of day for the meal associated with this picture is indeterminate as it could be used for various occasions.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "eau",
    "quantity": "1",
    "weight": "250",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson",
    "time": "indéterminé",
    "event": "declaration"
  }
]
```
Note: The weight is estimated based on the common capacity of a mug which is approximately 250 milliliters. The exact time of day for the meal associated with this picture is indeterminate as it could be used for various occasions.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "eau",    "quantity": "1",    "weight": "250",    "cooking": "",    "brand": "",    "company": "",    "type": "boisson",    "time": "indéterminé",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'eau', 'quantity': '1', 'weight': '250', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson', 'time': 'indéterminé', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'eau', 'quantity': '1', 'weight': '250', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson', 'time': 'indéterminé', '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 '% eau %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Eau -  eau  -  -  - 10064 -  -  - KCA#08cfe774cbf7476b1e582734c7082ecd
Eau de Vie -  eau de vie  -  -  - 210 -  -  - CIQ#2397ddba68eefec7e38e3a061b6060e3
Eau de Coco -  eau de coco  -  -  - 574 -  -  - CIQ#4f6cfd4687e4da85c9063e194dd3113b
Eau Minérale -  eau minerale  -  -  - 0 -  -  - CIQ#682a311be3fc15a20a88c168408e5304
Eau Minérale -  eau minerale  - aliment moyen -  - 160 -  -  - KCA#69addfd353e07f633ee05c6be8ac5d4d
Eau Minérale -  eau minerale  - plate, aliment moyen -  - 18 -  -  - CIQ#9f35a4198a700eac62fe4d1dc426f1a4
Eau Minérale -  eau minerale  - gazeuse, aliment moyen -  - 28 -  -  - CIQ#38da155cfd970d21ba9f4b87294b96df
Eau Minérale -  eau minerale  - ou de source aromatisée agrumes -  - 33 -  -  - KCA#47ee70f086c3080428426febc2426e8c
Eau Minérale -  eau minerale  - ou de source aromatisée, arôme autre qu'agrumes -  - 36 -  -  - KCA#0daeef02b69e5526427bc855f1ec3111
Eau Minérale -  eau minerale  - embouteillée, faiblement minéralisée, aliment moyen -  - 0 -  -  - CIQ#a8b887f21f002cd8ddbda99766ee5ec4
Eau de Source -  eau de source  - embouteillée, aliment moyen -  - 0 -  -  - CIQ#b6c1ba3e6cb4c788d63711a9b869730b
Eau du Robinet -  eau robinet  -  -  - 273 -  -  - CIQ#4c4a29ce4ec63b6cfc6bc3914ccf7056
Eau Minérale Dax -  eau minerale da  - embouteillée, non gazeuse, moyennement minéralisée, Dax, 40 -  - 0 -  -  - CIQ#a07a880ef627fa44150fe5583484549d
Eau de Vie de Vin -  eau de vie de vin  - type armagnac, cognac -  - 0 -  -  - CIQ#c0440021ea15aa2abf11853bbd2191a4
Eau Minérale Néro -  eau minerale nero  - embouteillée, non gazeuse, faiblement minéralisée, Grèce -  - 0 -  -  - CIQ#8ab34da104cb5b744e0ad6eaece161a6
Eau Minérale Avra -  eau minerale avra  - embouteillée, non gazeuse, faiblement minéralisée, Grèce -  - 0 -  -  - CIQ#b0465b7ee2f045df840aac281b388253
Eau Minérale Luso -  eau minerale luso  - embouteillée, non gazeuse, très faiblement minéralisée, Portugal -  - 0 -  -  - CIQ#45d467ce96aa14e71c62e6ca943f5621
Eau Minérale Eden -  eau minerale eden  - La Goa, embouteillée, non gazeuse, faiblement minéralisée, Suisse -  - 0 -  -  - CIQ#341195c07e8f951269157ecad800778a
Eau Minérale Ogeu -  eau minerale ogeu  - embouteillée, gazeuse, faiblement minéralisée, Ogeu-les-Bains, 64 -  - 0 -  -  - CIQ#14fc742b6db6af7dce1a08288d62ddf6
Eau Minérale Vals -  eau minerale val  - embouteillée, gazeuse, moyennement minéralisée, Vals-les-Bains, 07 -  - 0 -  -  - CIQ#11be70594fa1e46c35dca065d17b5ca6
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Eau', 'normName': ' eau ', 'comment': '', 'normComment': '', 'rank': 10064, 'id': 'KCA#08cfe774cbf7476b1e582734c7082ecd', 'quantity': '1', 'quantityLem': '1', 'pack': ['VAE', 'VX1', 'VA2', 'GOB', 'VA4', 'VA4', 'VA3'], 'type': 'boisson', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'indéterminé', 'event': 'declaration', 'serving': 'VAE-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.5025875568389893}
----------------------------------------------------------------------------------
LLM CPU Time: 3.5025875568389893