Input path: /home/debian/html/nutritwin/output_llm/66ed96bd74caa/input.json
Output path: /home/debian/html/nutritwin/output_llm/66ed96bd74caa/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 -----------------------------
The image provided does not contain any visible food or beverages, so I cannot provide any information as requested. If you have an image that does include food and drinks, please share it, and I'll be happy to help with the identification.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
The image provided does not contain any visible food or beverages, so I cannot provide any information as requested. If you have an image that does include food and drinks, please share it, and I'll be happy to help with the identification.
------------------------------------------------------

------------------------ After simplification ------------------------

----------------------------------------------------------------------

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
ERROR: impossible to parse [II]:
The image provided does not contain any visible food or beverages, so I cannot provide any information as requested. If you have an image that does include food and drinks, please share it, and I'll be happy to help with the identification.

The extracted string is 
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

--------------------------------- LLM result -----------------------------------
{'response': {}, 'cost': 0.0}
--------------------------------------------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 3.256495714187622}
----------------------------------------------------------------------------------
LLM CPU Time: 3.256495714187622