Input path: /home/debian/html/nutritwin/output_llm/674c374975df6/input.json
Output path: /home/debian/html/nutritwin/output_llm/674c374975df6/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": "fromage fouetté",
    "quantity": "1 pot",
    "weight": "150g",
    "cooking": "",
    "brand": "Madame Loïk",
    "company": "Paysan Breton",
    "type": "produit laitier",
    "time": "",
    "event": "declaration"
  }
]
```

Le "time" n'est pas défini dans l'image, donc je n'ai pas rempli cette partie.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "fromage fouetté",
    "quantity": "1 pot",
    "weight": "150g",
    "cooking": "",
    "brand": "Madame Loïk",
    "company": "Paysan Breton",
    "type": "produit laitier",
    "time": "",
    "event": "declaration"
  }
]
```

Le "time" n'est pas défini dans l'image, donc je n'ai pas rempli cette partie.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "fromage fouetté",    "quantity": "1 pot",    "weight": "150g",    "cooking": "",    "brand": "Madame Loïk",    "company": "Paysan Breton",    "type": "produit laitier",    "time": "",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'fromage fouetté', 'quantity': '1 pot', 'weight': '150g', 'cooking': '', 'brand': 'Madame Loïk', 'company': 'Paysan Breton', 'type': 'produit laitier', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'fromage fouetté', 'quantity': '1 pot', 'weight': '150g', 'cooking': '', 'brand': 'Madame Loïk', 'company': 'Paysan Breton', 'type': 'produit laitier', 'time': '', '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 '% fromage fouette %' AND V_NormTrademark LIKE '%madame loik%' 


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 '% fromage fouette %' AND V_NormTrademark LIKE '%madame loik%' 


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 '% fromage fouette %' AND V_NormAggr LIKE '% madame loik %' AND V_NormAggr LIKE '% paysan breton %' 

-------------------------------------------
------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 '% fromage fouette %' AND V_NormAggr LIKE '% madame loik %' AND V_NormAggr LIKE '% paysan breton %' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 4.65848445892334}
----------------------------------------------------------------------------------
LLM CPU Time: 4.65848445892334