Input path: /home/debian/html/nutritwin/output_llm/6726a2d353705/input.json
Output path: /home/debian/html/nutritwin/output_llm/6726a2d353705/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": "vin",
    "quantity": "",
    "weight": "750",
    "cooking": "",
    "brand": "Boiserie",
    "company": "",
    "type": "boisson alcoolisée",
    "time": "dîner",
    "event": "declaration"
  }
]
```

Veuillez noter que le poids donné pour le vin est une estimation basée sur la taille standard d'une bouteille de vin. Habituellement, une bouteille de vin contient 750 millilitres.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "vin",
    "quantity": "",
    "weight": "750",
    "cooking": "",
    "brand": "Boiserie",
    "company": "",
    "type": "boisson alcoolisée",
    "time": "dîner",
    "event": "declaration"
  }
]
```

Veuillez noter que le poids donné pour le vin est une estimation basée sur la taille standard d'une bouteille de vin. Habituellement, une bouteille de vin contient 750 millilitres.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "vin",    "quantity": "",    "weight": "750",    "cooking": "",    "brand": "Boiserie",    "company": "",    "type": "boisson alcoolisée",    "time": "dîner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vin', 'quantity': '', 'weight': '750', 'cooking': '', 'brand': 'Boiserie', 'company': '', 'type': 'boisson alcoolisée', 'time': 'dîner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'vin', 'quantity': '', 'weight': '750', 'cooking': '', 'brand': 'Boiserie', 'company': '', 'type': 'boisson alcoolisée', '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 '% vin %' AND V_NormTrademark LIKE '%boiserie%' 


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 '% vin %' AND V_NormTrademark LIKE '%boiserie%' 

-------------------------------------------
------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 '% vin %' AND V_NormTrademark LIKE '%boiserie%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 3.5685808658599854}
----------------------------------------------------------------------------------
LLM CPU Time: 3.5685808658599854