Input path: /home/debian/html/nutritwin/output_llm/66065efa08126/input.json
Output path: /home/debian/html/nutritwin/output_llm/66065efa08126/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": "La Vache qui rit",
    "quantity": "32 portions",
    "weight": "",
    "cooking": "",
    "brand": "La Vache qui rit",
    "company": "Bel",
    "type": "fromage",
    "time": "",
    "event": "declaration"
  }
]
```

Je tiens à préciser que l'image ne fournit pas d'information sur le poids spécifique du fromage, ni sur le moment de la journée (comme le petit-déjeuner ou le dîner), donc ces champs sont laissés vides.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "La Vache qui rit",
    "quantity": "32 portions",
    "weight": "",
    "cooking": "",
    "brand": "La Vache qui rit",
    "company": "Bel",
    "type": "fromage",
    "time": "",
    "event": "declaration"
  }
]
```

Je tiens à préciser que l'image ne fournit pas d'information sur le poids spécifique du fromage, ni sur le moment de la journée (comme le petit-déjeuner ou le dîner), donc ces champs sont laissés vides.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "La Vache qui rit",    "quantity": "32 portions",    "weight": "",    "cooking": "",    "brand": "La Vache qui rit",    "company": "Bel",    "type": "fromage",    "time": "",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'La Vache qui rit', 'quantity': '32 portions', 'weight': '', 'cooking': '', 'brand': 'La Vache qui rit', 'company': 'Bel', 'type': 'fromage', 'time': '', '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 '% la vache qui rit %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


Second 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_NormAggr LIKE '% la vache qui rit %' AND V_NormTrademark LIKE '%%' 

-------------------------------------------
------ERROR--------------------------------
No solution for query: 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_NormAggr LIKE '% la vache qui rit %' AND V_NormTrademark LIKE '%%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 15.704664468765259}
----------------------------------------------------------------------------------
LLM CPU Time: 15.704664468765259