Input path: /home/debian/html/nutritwin/output_llm/67167983ad0b6/input.json
Output path: /home/debian/html/nutritwin/output_llm/67167983ad0b6/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": "nuggets de poulet", 
    "quantity": "20", 
    "weight": "", 
    "cooking": "frit",
    "brand": "McNuggets", 
    "company": "McDonald's", 
    "type": "plat principal ou en-cas", 
    "time": "grignotage", 
    "event": "declaration"
  }
]
``` 
Note: The weight is not visible, and the exact time of the day (petit-déjeuner, déjeuner, grignotage, dîner) cannot be determined without more context. Nuggets de poulet are a common snack or meal component, so "grignotage" may fit, but they can also be eaten at lunch or dinner.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "nuggets de poulet", 
    "quantity": "20", 
    "weight": "", 
    "cooking": "frit",
    "brand": "McNuggets", 
    "company": "McDonald's", 
    "type": "plat principal ou en-cas", 
    "time": "grignotage", 
    "event": "declaration"
  }
]
``` 
Note: The weight is not visible, and the exact time of the day (petit-déjeuner, déjeuner, grignotage, dîner) cannot be determined without more context. Nuggets de poulet are a common snack or meal component, so "grignotage" may fit, but they can also be eaten at lunch or dinner.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "nuggets de poulet",     "quantity": "20",     "weight": "",     "cooking": "frit",    "brand": "McNuggets",     "company": "McDonald's",     "type": "plat principal ou en-cas",     "time": "grignotage",     "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'nuggets de poulet', 'quantity': '20', 'weight': '', 'cooking': 'frit', 'brand': 'McNuggets', 'company': "McDonald's", 'type': 'plat principal ou en-cas', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'nuggets de poulet', 'quantity': '20', 'weight': '', 'cooking': 'frit', 'brand': 'McNuggets', 'company': "McDonald's", 'type': 'plat principal ou en-cas', '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 '% nugget de poulet %' AND V_NormTrademark LIKE '%mcnuggets%' 


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 '% nugget de poulet %' AND V_NormTrademark LIKE '%mcnuggets%' 


Third 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 '% nugget de poulet %' AND V_NormAggr LIKE '% mcnuggets %' AND V_NormAggr LIKE '% mcdonald %' 

-------------------------------------------
------ERROR--------------------------------
No solution for query: 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 '% nugget de poulet %' AND V_NormAggr LIKE '% mcnuggets %' AND V_NormAggr LIKE '% mcdonald %' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 5.3954668045043945}
----------------------------------------------------------------------------------
LLM CPU Time: 5.3954668045043945