Input path: /home/debian/html/nutritwin/output_llm/6611362c1e1fb/input.json
Output path: /home/debian/html/nutritwin/output_llm/6611362c1e1fb/output.json
Input text: Lieu noir
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: Lieu noir
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Lieu noir###. 
 Format the result in JSON format: {intents: []}.
=========================================================================================


------------------------------ LLM Raw response -----------------------------
{
"intents": ["Other intent"]
}
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
{
"intents": ["Other intent"]
}
------------------------------------------------------

------------------------ After simplification ------------------------
{"intents": ["Other intent"]}
----------------------------------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Lieu noir', 'intents': ['Other intent'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 0.41951966285705566}
----------------------------------------------------------------------------------
LLM CPU Time: 0.41951966285705566