Input path: /home/debian/html/nutritwin/output_llm/6705406f8a5ff/input.json
Output path: /home/debian/html/nutritwin/output_llm/6705406f8a5ff/output.json
Input text: Une tranche.
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: Une tranche.
==================================================================================================================================

==================================== 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: ###Une tranche.###. 
 Format the result in JSON format: {intents: []}.
=========================================================================================


------------------------------ LLM Raw response -----------------------------
```json
{
  "intents": ["Identify food consumption or declaration"]
}
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
{
  "intents": ["Identify food consumption or declaration"]
}
```
------------------------------------------------------

------------------------ After simplification ------------------------
{  "intents": ["Identify food consumption or declaration"]}
----------------------------------------------------------------------

==================================== Prompt =============================================

Convert this natural language query : """Une tranche.""" into an array in JSON of consumed foods and beverages.

Provide a solution without explanation.

Use only the ontology described in this Turtle/RDF model:

"""
@prefix food: <http://www.3dminded.com/RDF/ontology/food#> .

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . 
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix prov: <http://www.w3.org/ns/prov#> .

food: a owl:Ontology ;
    rdfs:comment "Definition of the food archetype"@en .

food:name a owl:DatatypeProperty;
    rdfs:label "name"@en;
    rdfs:comment "Food or beverage identifier, the name should not contain information related to quantity or container (like glass...)."@en;
    rdfs:comment "Ignore food or beverage when it is not consumed in the past, now or in the future."@en;
    rdfs:comment "The cooking mode is not in the name. Keep the same language"@en;
    rdfs:range xsd:string.

food:quantity a owl:DatatypeProperty ;
    rdfs:label "quantity"@en;
    rdfs:comment "The quantity of food or drink that is or was consumed. Quantity examples in french: 'un quignon', 'un cornet', 'un verre', 'une tranche', 'une boule', 'un', 'deux', 'trois',... Keep the same language."@en;
    rdfs:range xsd:string.

food:cookingMethod a owl:DatatypeProperty ;
    rdfs:label "cooking method"@en;
    rdfs:comment "The cooking method of food. Keep the same language"@en;
    rdfs:range xsd:string.

food:type a owl:DatatypeProperty ;
    rdfs:label "type of food"@en;
    rdfs:comment "Identify the type of food."@en;
    rdfs:range xsd:string.

food:food a food:type ;
    rdfs:label "food" .
food:beverage a food:type ;
    rdfs:label "beverage" .
	
food:timeOfTheDay a owl:DatatypeProperty ;
    rdfs:label "time of the day"@en;
    rdfs:comment "Time of the day when food or drink was consumed."@en;
    rdfs:range xsd:string.

food:breakfast a food:timeOfTheDay ;
    rdfs:label "breakfast" .
food:lunch a food:timeOfTheDay ;
    rdfs:label "lunch" .
food:snacking a food:timeOfTheDay ;
    rdfs:label "snacking" .
food:dinner a food:timeOfTheDay ;
    rdfs:label "dinner" .

food:brand a owl:DatatypeProperty ;
    rdfs:label "Brand"@en;
    rdfs:comment "Food or beverage brand. The restaurants are not brand."@en;
    rdfs:comment "When the name is very known (ex: Activia, Coca) and the brand is not mentioned, guess the brand."@en;
    rdfs:range xsd:string.

food:company a owl:DatatypeProperty ;
    rdfs:label "Company"@en;
    rdfs:comment "Product company."@en;
    rdfs:range xsd:string.

food:enumEvent a rdfs:Class .

food:event a owl:DatatypeProperty ;
    rdfs:label "event"@en;
    rdfs:comment "Event of eating or drinking. Each must have an event"@en;
    rdfs:range food:enumEvent.

food:intent a food:enumEvent ;
    rdfs:label "intent" .
    rdfs:comment "When the event should happen"@en.
food:declaration a food:enumEvent ;
    rdfs:label "declaration" .
    rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:enumEvent ;
    rdfs:label "unknown" ;
    rdfs:comment "When the event is unknown in the day"@en.

"""
=========================================================================================


------------------------------ LLM Raw response -----------------------------
```json
[
    {
        "quantity": "une tranche",
        "event": "declaration"
    }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
```json
[
    {
        "quantity": "une tranche",
        "event": "declaration"
    }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[    {        "quantity": "une tranche",        "event": "declaration"    }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'quantity': 'une tranche', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'quantity': 'une tranche', 'event': 'declaration'}
Traceback (most recent call last):
  File "/home/debian/html/nutritwin/resources/KCALLMMainService.py", line 70, in <module>
    omess = KCALLMMain.runEvent(event)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/debian/html/nutritwin/resources/KCALLMMain.py", line 134, in runEvent
    resp = KCALLMMainSpeechToData.execute(speech, image64, comment, appId, device, version, age, gender, longitude, latitude, test, llmid)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/debian/html/nutritwin/resources/KCALLMMainSpeechToData.py", line 39, in execute
    omess = executeLLMSingle(text, image64, comment, model)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/debian/html/nutritwin/resources/KCALLMMainSpeechToData.py", line 189, in executeLLMSingle
    sols = KCALLMNutritionUtilities.getBestSolutions(jresult["response"], dbPath, jVoca)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/debian/html/nutritwin/resources/KCALLMNutritionUtilities.py", line 278, in getBestSolutions
    name = f["name"]
           ~^^^^^^^^
KeyError: 'name'