Input path: /home/debian/html/nutritwin/output_llm/67423144183e9/input.json
Output path: /home/debian/html/nutritwin/output_llm/67423144183e9/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": "courgette",
        "quantity": "2 unités",
        "weight": "",
        "cooking": "crue",
        "brand": "",
        "company": "",
        "type": "légume",
        "time": "",
        "event": "declaration"
    }
]
```

The image shows two courgettes. There are no visible brand markings, preparation, packaging, or indicators of weight. Since there is no food or beverages typically associated with a specific time of day such as coffee, orange juice, or toast, I cannot determine the time of day or the corresponding meal-type event.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
    {
        "name": "courgette",
        "quantity": "2 unités",
        "weight": "",
        "cooking": "crue",
        "brand": "",
        "company": "",
        "type": "légume",
        "time": "",
        "event": "declaration"
    }
]
```

The image shows two courgettes. There are no visible brand markings, preparation, packaging, or indicators of weight. Since there is no food or beverages typically associated with a specific time of day such as coffee, orange juice, or toast, I cannot determine the time of day or the corresponding meal-type event.
------------------------------------------------------

------------------------ After simplification ------------------------
[    {        "name": "courgette",        "quantity": "2 unités",        "weight": "",        "cooking": "crue",        "brand": "",        "company": "",        "type": "légume",        "time": "",        "event": "declaration"    }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'courgette', 'quantity': '2 unités', 'weight': '', 'cooking': 'crue', 'brand': '', 'company': '', 'type': 'légume', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'courgette', 'quantity': '2 unités', 'weight': '', 'cooking': 'crue', 'brand': '', 'company': '', 'type': 'légume', '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 '% courgette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Courgette -  courgette  - pulpe et peau -  - 6197 -  -  - CIQ#8f15e99f917acaea5e5d4d09c1ac70fd
Courgette -  courgette  - pulpe et peau, crue -  - 10037 -  -  - CIQ#293f06655b9594a266b55f179a16bd56
Courgette -  courgette  - pulpe et peau, rôtie/cuite au four -  - 0 -  -  - CIQ#2a09434ec4c982719db3b87fb986b92a
Courgettes Frites -  courgette frite  -  -  - 207 -  -  - KCA#959d0fa787188921ad4497a575b243ab
Courgettes Farcies -  courgette farcie  -  -  - 862 -  -  - KCA#77e254b2cd7edd2930ea260315eff594
Courgettes Mousseline -  courgette mousseline  -  -  - 168 -  -  - KCA#25d0446a01b3d8dd52d503827adba452
Courgettes à l'Orientale -  courgette orientale  -  -  - 26 -  -  - KCA#67d86f19e38566e0d5b257f32b627f4d
Courgettes Façon Actifry -  courgette facon actifry  -  -  - 15 -  -  - KCA#10c8891e84e0c283919c921e9acde855
Courgettes Farcies au Maigre -  courgette farcie maigre  -  -  - 42 -  -  - KCA#c6d1d8514e1d9b2ea736c6751ba2e452
Flan de Courgette -  flan de courgette  -  -  - 890 -  -  - KCA#9809af1c34ea8cd82f667ff2d233bf58
Pâtes aux Courgettes et à la Ricotta -  pate au courgette ricotta  -  -  - 86 -  -  - KCA#b083d2bc60b9e47b8d1416c1969b5f6d
Purée de Courgettes Pomme de Terre -  puree de courgette pomme de terre  -  -  - 96 -  -  - KCA#33431af664194d56f5845a8d1fa010a9
Gratin de Courgettes à la Bolognaise -  gratin de courgette bolognaise  -  -  - 99 -  -  - KCA#3a9a287b43639c40798a6e25743e7505
Beignets de Courgettes -  beignet de courgette  -  -  - 147 -  -  - KCA#d826bc4b23eebc71f3a93e4321e1632f
Omelette aux Courgettes -  omelette au courgette  -  -  - 125 -  -  - KCA#4b746e9c0feef9bca3c7252301d6c95d
Lentilles Brunes à la Courgette et au Chorizo -  lentille brune courgette chorizo  -  -  - 6 -  -  - KCA#488b3d0b5e6b068cb458940010656fd5
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Courgette', 'normName': ' courgette ', 'comment': 'pulpe et peau', 'normComment': ' pulpe peau ', 'rank': 6197, 'id': 'CIQ#8f15e99f917acaea5e5d4d09c1ac70fd', 'quantity': '2 unités', 'quantityLem': '2', 'pack': ['CO3.w300'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'CO3-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.699324607849121}
----------------------------------------------------------------------------------
LLM CPU Time: 4.699324607849121