Input path: /home/debian/html/nutritwin/output_llm/6735e32d8973e/input.json
Output path: /home/debian/html/nutritwin/output_llm/6735e32d8973e/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": "risotto",
    "quantity": "portion",
    "weight": "250",
    "cooking": "cuit",
    "brand": "",
    "company": "",
    "type": "plat principal",
    "time": "dîner",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "risotto",
    "quantity": "portion",
    "weight": "250",
    "cooking": "cuit",
    "brand": "",
    "company": "",
    "type": "plat principal",
    "time": "dîner",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "risotto",    "quantity": "portion",    "weight": "250",    "cooking": "cuit",    "brand": "",    "company": "",    "type": "plat principal",    "time": "dîner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'risotto', 'quantity': 'portion', 'weight': '250', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'dîner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'risotto', 'quantity': 'portion', 'weight': '250', 'cooking': 'cuit', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'dîner', '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 '% risotto %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Risotto -  risotto  - aux légumes -  - 0 -  -  - CIQ#b56358e61f75765f68d8d92c6bcfb15e
Risotto -  risotto  - aux fromages -  - 3 -  -  - CIQ#aaa258081c38edb52743abde7ccf0d95
Risotto -  risotto  - aux fruits de mer -  - 0 -  -  - CIQ#48d69fb9ea5cd1b79e77dc63ee3943e0
Risotto aux Seiches -  risotto au seiche  -  -  - 212 -  -  - KCA#e993f444d7416b112a143b11f383e540
Risotto aux Lentilles -  risotto au lentille  -  -  - 21 -  -  - KCA#29c4bcb6943102686676e3b1e68b3176
Risotto à la Milanaise -  risotto milanaise  -  -  - 280 -  -  - KCA#8e664ca5aaa229259e526f3241c4ca47
Risotto aux Betteraves -  risotto au betterave  - et à la roquette -  - 9 -  -  - KCA#dfb88f6aa624f0c1011b6e69bfa34b69
Risotto aux Artichauts -  risotto au artichaut  - petits pois et Champignons -  - 19 -  -  - KCA#8d5a8b587108c9f467d8d7323356210b
Timbales de Risotto Milanais au Pesto -  timbale de risotto milanai pesto  - au pesto -  - 0 -  -  - KCA#44d1e84ae6f0f232fb6abbfe42ca162c
----------------------------------------------------

ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
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': 'Risotto', 'normName': ' risotto ', 'comment': 'aux légumes', 'normComment': ' au legume ', 'rank': 0, 'id': 'CIQ#b56358e61f75765f68d8d92c6bcfb15e', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['BOR.w250'], 'type': 'plat principal', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'dîner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.604327917098999}
----------------------------------------------------------------------------------
LLM CPU Time: 2.604327917098999