Input path: /home/debian/html/nutritwin/output_llm/674b62a227f44/input.json
Output path: /home/debian/html/nutritwin/output_llm/674b62a227f44/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": "rum baba",
    "quantity": "2 unités",
    "weight": "",
    "cooking": "",
    "brand": "Salon de Thé de Joël Robuchon",
    "company": "Joël Robuchon",
    "type": "pâtisserie",
    "time": "grignotage",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
  {
    "name": "rum baba",
    "quantity": "2 unités",
    "weight": "",
    "cooking": "",
    "brand": "Salon de Thé de Joël Robuchon",
    "company": "Joël Robuchon",
    "type": "pâtisserie",
    "time": "grignotage",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "rum baba",    "quantity": "2 unités",    "weight": "",    "cooking": "",    "brand": "Salon de Thé de Joël Robuchon",    "company": "Joël Robuchon",    "type": "pâtisserie",    "time": "grignotage",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'rum baba', 'quantity': '2 unités', 'weight': '', 'cooking': '', 'brand': 'Salon de Thé de Joël Robuchon', 'company': 'Joël Robuchon', 'type': 'pâtisserie', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'rum baba', 'quantity': '2 unités', 'weight': '', 'cooking': '', 'brand': 'Salon de Thé de Joël Robuchon', 'company': 'Joël Robuchon', 'type': 'pâtisserie', '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 '% rum baba %' AND V_NormTrademark LIKE '%salon de the de joel robuchon%' 


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 '% rum baba %' AND V_NormTrademark LIKE '%salon de the de joel robuchon%' 


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 '% rum baba %' AND V_NormAggr LIKE '% salon de the de joel robuchon %' AND V_NormAggr LIKE '% joel robuchon %' 

-------------------------------------------
------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 '% rum baba %' AND V_NormAggr LIKE '% salon de the de joel robuchon %' AND V_NormAggr LIKE '% joel robuchon %' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 2.985257625579834}
----------------------------------------------------------------------------------
LLM CPU Time: 2.985257625579834