Input path: /home/debian/html/nutritwin/output_llm/66e57118bae07/input.json
Output path: /home/debian/html/nutritwin/output_llm/66e57118bae07/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": "potiron",
        "quantity": "1",
        "weight": "",
        "cooking": "à cuire",
        "brand": "",
        "company": "",
        "type": "légume",
        "time": "",
        "event": "declaration"
    }
]
```
Note: Since weight is not specified and there's no standard reference point in the image, it is left blank. The image does not provide any information on the time or the corresponding meal (petit-déjeuner, déjeuner, grignotage, dîner), hence that information is also left blank.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
    {
        "name": "potiron",
        "quantity": "1",
        "weight": "",
        "cooking": "à cuire",
        "brand": "",
        "company": "",
        "type": "légume",
        "time": "",
        "event": "declaration"
    }
]
```
Note: Since weight is not specified and there's no standard reference point in the image, it is left blank. The image does not provide any information on the time or the corresponding meal (petit-déjeuner, déjeuner, grignotage, dîner), hence that information is also left blank.
------------------------------------------------------

------------------------ After simplification ------------------------
[    {        "name": "potiron",        "quantity": "1",        "weight": "",        "cooking": "à cuire",        "brand": "",        "company": "",        "type": "légume",        "time": "",        "event": "declaration"    }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'potiron', 'quantity': '1', 'weight': '', 'cooking': 'à cuire', 'brand': '', 'company': '', 'type': 'légume', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'potiron', 'quantity': '1', 'weight': '', 'cooking': 'à cuire', '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 '% potiron %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Potiron -  potiron  - rôti/cuit au four -  - 0 -  -  - CIQ#92a3ffd4b15de3d941edb4b13066c49a
Potiron Briarde -  potiron briarde  -  -  - 8 -  -  - KCA#a3cb98ed76f5abf1dd04213eb3ec9ba5
Soupe au Potiron -  soupe potiron  -  -  - 145 -  -  - CIQ#6f75550105dd45c4009ada47f18dd0cd
Soupe au Potiron -  soupe potiron  - déshydratée reconstituée -  - 0 -  -  - CIQ#a6019ca2a15471273b3e97eea7b2d50a
Purée de Potiron -  puree de potiron  -  -  - 474 -  -  - KCA#a530f4909bba07941d57ea667726259d
Pappardelle au Potiron et à la Sauge -  pappardelle potiron sauge  -  -  - 2 -  -  - KCA#16aaeb82c26e780519b014d809a12d00
Purée de Pomme de Terre et Potiron -  puree de pomme de terre potiron  -  -  - 41 -  -  - KCA#3c5f2cb6affb3eb783f2568ee97fdf30
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Potiron', 'normName': ' potiron ', 'comment': 'rôti/cuit au four', 'normComment': ' roti/cuit four ', 'rank': 0, 'id': 'CIQ#92a3ffd4b15de3d941edb4b13066c49a', 'quantity': '1', 'quantityLem': '1', 'pack': ['LEG.w150'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'LEG-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.86140513420105}
----------------------------------------------------------------------------------
LLM CPU Time: 4.86140513420105