Input path: /home/debian/html/nutritwin/output_llm/67431d6fe3cb4/input.json
Output path: /home/debian/html/nutritwin/output_llm/67431d6fe3cb4/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": "endive",
    "quantity": "",
    "weight": "",
    "cooking": "crue",
    "brand": "",
    "company": "",
    "type": "légume",
    "time": "grignotage",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

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

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

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

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


------------- Found solution (max 20) --------------
Endive -  endive  - crue -  - 0 -  -  - CIQ#c9461ebf9e50ce9c4870c75eda51fa7e
Endive -  endive  - rôtie/cuite au four -  - 0 -  -  - CIQ#cfbace3f9abdd920eb03851948cb5ad2
Endives Roties -  endive rotie  -  -  - 164 -  -  - KCA#d5640e1c6d39d93bd34a3fbe03f085b0
Endives Braisées -  endive braisee  -  -  - 712 -  -  - KCA#2baf4cba05f671f35117316b40fa765f
Endives Meunière -  endive meuniere  -  -  - 23 -  -  - KCA#1f33cd25fe0dbfcb3d3afdb7dae639a5
Endives Surprise -  endive surprise  -  -  - 9 -  -  - KCA#a6a020ed2298844c2bbc58f681c53ab5
Endives Parisiennes -  endive parisienne  -  -  - 7 -  -  - KCA#3dd1bce598559b51d5e932f72313d501
Salade d'Endives -  salade endive  -  -  - 3059 -  -  - KCA#22f49bd5d9db2619011406f0bcce4e4b
Gratin d'Endives et Jambon -  gratin endive jambon  -  -  - 672 -  -  - KCA#5943f9f3a617979b609eceb93b42a94e
----------------------------------------------------

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': 'Endive', 'normName': ' endive ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 0, 'id': 'CIQ#c9461ebf9e50ce9c4870c75eda51fa7e', 'quantity': '', 'quantityLem': '', 'pack': ['APL.w150'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.429769992828369}
----------------------------------------------------------------------------------
LLM CPU Time: 2.429769992828369