Input path: /home/debian/html/nutritwin/output_llm/6718db98ac3ac/input.json
Output path: /home/debian/html/nutritwin/output_llm/6718db98ac3ac/output.json
Input text: Trois.
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: Trois.
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Identify food consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Trois.###. 
 Format the result in JSON format: {intents: []}.
=========================================================================================


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

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

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

--------------------------------- final result -----------------------------------
{'prompt': 'Trois.', 'intents': ['Other intent'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 0.4581155776977539}
----------------------------------------------------------------------------------
LLM CPU Time: 0.4581155776977539