Input path: /home/debian/html/nutritwin/output_llm/66065e67ae98b/input.json
Output path: /home/debian/html/nutritwin/output_llm/66065e67ae98b/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: 
==================================================================================================================================
=================================================================================> OK
###########################################
#   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": "fromage",
    "quantity": "portion",
    "weight": "env. 30g",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "produit laitier",
    "time": "grignotage",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "fromage",
    "quantity": "portion",
    "weight": "env. 30g",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "produit laitier",
    "time": "grignotage",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "fromage",    "quantity": "portion",    "weight": "env. 30g",    "cooking": "",    "brand": "",    "company": "",    "type": "produit laitier",    "time": "grignotage",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'fromage', 'quantity': 'portion', 'weight': 'env. 30g', 'cooking': '', 'brand': '', 'company': '', 'type': 'produit laitier', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% fromage %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 10) --------------
Fromage -  -  - 23096 -  - KCA#e2646df35885ba5fc75c406a551c9fbc
Fromage 45% MG -  -  - 6874 -  - KCA#14ed2b0745972d44df97c5d52a44ac69
Fromage 20% MG -  -  - 1124 -  - KCA#e32d6c98bf1d5f0a3c853a8f6bb7c3b3
Fromage 70% MG -  -  - 494 -  - KCA#351b50fec02ae7c43d964985ac9086c6
Fromage de Tête -  -  - 258 -  - CIQ#a80997979cdf84066ed5ed98f0291aef
Fromage de Chèvre -  -  - 4537 -  - KCA#87a40b8f006dcb11aafd4e97014ed3f4
Fromage de Brebis - pâte pressée -  - 0 -  - KCA#58787aec327646598cc7785b49eea77a
Fromage de Brebis - pâte molle à croûte fleurie -  - 0 -  - KCA#a463c1fc485a4f9d296ce6817ce2c361
Fromage de Chèvre - lactique affiné, au lait cru type Crottin -  - 250 -  - KCA#2c01ba493c1fac82cabb393f8f3648a7
Fromage Frais 0% MG -  -  - 519 -  - KCA#88f1992eded597fa4d19465f74683774
----------------------------------------------------

ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Fromage', 'normName': ' fromage ', 'comment': '', 'normComment': '', 'rank': 23096, 'id': 'KCA#e2646df35885ba5fc75c406a551c9fbc', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['CAM.w20', 'GRU.w20', 'MIM.w20', 'ROC.w20', 'CH2.w20'], 'type': 'produit laitier', 'gtin': '', 'brand': '', 'time': 'grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 4.8834075927734375}
----------------------------------------------------------------------------------
LLM CPU Time: 4.8834075927734375