Input path: /home/debian/html/nutritwin/output_llm/67333d85f2046/input.json
Output path: /home/debian/html/nutritwin/output_llm/67333d85f2046/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 -----------------------------
Je suis navré, mais l'image que vous avez partagée semble comprendre uniquement une salade, et il n'est pas possible de différencier avec précision les types de feuilles ou autres ingrédients spécifiques juste en regardant la photo. Cependant, je peux vous donner un exemple général de ce que pourrait être la composition de cette salade:

[
  {"name": "épinard", "quantity": "portion", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "légume vert", "time": "", "event": "déclaration"},
  {"name": "betterave", "quantity": "portion", "weight": "", "cooking": "cuite", "brand": "", "company": "", "type": "légume racine", "time": "", "event": "déclaration"},
  {"name": "fromage de chèvre", "quantity": "portion", "weight": "", "cooking": "frais", "brand": "", "company": "", "type": "produit laitier", "time": "", "event": "déclaration"},
  {"name": "vinaigrette", "quantity": "", "weight": "", "cooking": "", "brand": "", "company": "", "type": "sauce", "time": "", "event": "dé
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Je suis navré, mais l'image que vous avez partagée semble comprendre uniquement une salade, et il n'est pas possible de différencier avec précision les types de feuilles ou autres ingrédients spécifiques juste en regardant la photo. Cependant, je peux vous donner un exemple général de ce que pourrait être la composition de cette salade:

[
  {"name": "épinard", "quantity": "portion", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "légume vert", "time": "", "event": "déclaration"},
  {"name": "betterave", "quantity": "portion", "weight": "", "cooking": "cuite", "brand": "", "company": "", "type": "légume racine", "time": "", "event": "déclaration"},
  {"name": "fromage de chèvre", "quantity": "portion", "weight": "", "cooking": "frais", "brand": "", "company": "", "type": "produit laitier", "time": "", "event": "déclaration"},
  {"name": "vinaigrette", "quantity": "", "weight": "", "cooking": "", "brand": "", "company": "", "type": "sauce", "time": "", "event": "dé
------------------------------------------------------

------------------------ After simplification ------------------------
{"name": "épinard", "quantity": "portion", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "légume vert", "time": "", "event": "déclaration"}
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': {'name': 'épinard', 'quantity': 'portion', 'weight': '', 'cooking': 'cru', 'brand': '', 'company': '', 'type': 'légume vert', 'time': '', 'event': 'déclaration'}, 'cost': 0.0}
--------------------------------------------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 7.56281304359436}
----------------------------------------------------------------------------------
LLM CPU Time: 7.56281304359436