Input path: /home/debian/html/nutritwin/output_llm/672ca4bce8c24/input.json
Output path: /home/debian/html/nutritwin/output_llm/672ca4bce8c24/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": "crabe surimi",
        "quantity": "",
        "weight": "estimation 150-200g",
        "cooking": "cuit",
        "brand": "",
        "company": "",
        "type": "fruits de mer",
        "time": "grignotage",
        "event": "declaration"
    }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
    {
        "name": "crabe surimi",
        "quantity": "",
        "weight": "estimation 150-200g",
        "cooking": "cuit",
        "brand": "",
        "company": "",
        "type": "fruits de mer",
        "time": "grignotage",
        "event": "declaration"
    }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[    {        "name": "crabe surimi",        "quantity": "",        "weight": "estimation 150-200g",        "cooking": "cuit",        "brand": "",        "company": "",        "type": "fruits de mer",        "time": "grignotage",        "event": "declaration"    }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'crabe surimi', 'quantity': '', 'weight': 'estimation 150-200g', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'fruits de mer', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'crabe surimi', 'quantity': '', 'weight': 'estimation 150-200g', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'fruits de mer', 'time': 'grignotage', '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 '% crabe surimi %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


Second 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_NormAggr LIKE '% crabe surimi %' AND V_NormTrademark LIKE '%%' 


------------- Found solution (max 20) --------------
Coquille Crabe et Surimi -  coquille crabe surimi  -  - Auchan - 0 - 3254568017407 - 3254568017407 - OFF#d243b677d59ee576a43e59e6b89e346f
----------------------------------------------------

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': 'Coquille Crabe et Surimi', 'normName': ' coquille crabe surimi ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#d243b677d59ee576a43e59e6b89e346f', 'quantity': '', 'quantityLem': '', 'pack': ['POI.w350'], 'type': 'fruits de mer', 'gtin': '3254568017407', 'gtinRef': '3254568017407', 'brand': 'Auchan', 'time': 'grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 9}], 'activity': [], 'response': {}}, 'cputime': 3.6508076190948486}
----------------------------------------------------------------------------------
LLM CPU Time: 3.6508076190948486