Input path: /home/debian/html/nutritwin/output_llm/68a721ebd19a2/input.json
Output path: /home/debian/html/nutritwin/output_llm/68a721ebd19a2/output.json
Input text: Compote bonne-maman.
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: Compote bonne-maman.
==================================================================================================================================

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


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

----------------- Make it compliant ------------------
{"intents": ["Identify food and beverage consumption or declaration"]}
------------------------------------------------------
ERROR: wrong object representation:
{'intents': ['Identify food and beverage consumption or declaration']}

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

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

Convert this natural language query : """Compote bonne-maman.""" into an array of JSON.
Ignore what it is not connected to nutrition, beverage or food. 

Provide a solution without explanation.

Use the following ontology and only this 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...).
Ignore food or beverage when it is not consumed in the past, now or in the future.
The cooking mode is not in the name. The name is only in french."""@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 is only in french. Here are examples: '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. The cooking method is in french."@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.
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.

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.

    
"""

Here is an example of result:
[
    {
        "name": "blanquette de veau",
        "quantity": "un plat",
        "cookingMethod": "mijot\u00e9",
        "timeOfTheDay": "lunch",
        "company": "Leclerc",
        "type": "food",
        "event": "declaration"
    },
    {
        "name": "eau",
        "brand": "Evian",
        "company": "Danone",
        "timeOfTheDay": "breakfast",
        "quantity": "un verre",
        "type": "beverage",
        "event": "intent"
    }
]
=========================================================================================


------------------------------ LLM Raw response -----------------------------
[
    {
        "name": "compote",
        "brand": "Bonne-maman",
        "type": "food",
        "event": "unknown"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "compote",
        "brand": "Bonne-maman",
        "type": "food",
        "event": "unknown"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "compote",
        "brand": "Bonne-maman",
        "type": "food",
        "event": "unknown"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'compote', 'brand': 'Bonne-maman', 'type': 'food', 'event': 'unknown'}], 'cost': 0.09636}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'compote', 'brand': 'Bonne-maman', 'type': 'food', 'event': 'unknown'}

First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% compote %' AND V_NormTrademark LIKE '%bonne maman%' 


------------- Found solution (max 20) --------------
Compote -  compote  -  - Bonne Maman - 0 - 3045320006919 - 3045320006919 - OFF#04b7951217cfb84d97b3d59a28797b4e
Compote -  compote  -  - Bonne Maman - 0 - 3608580931607 - 3045320006919 - OFF#eb6e65503f2942eeca52c81c782e4b5f
Compote Rhubarbe -  compote rhubarbe  -  - Bonne Maman - 0 - 0088702077261 - 0088702077261 - OFF#c929775657de0190241245d305c69407
Compote de Peche -  compote de peche  -  - Bonne Maman - 0 - 3045320449419 - 3045320449419 - OFF#7087108dea90ac8ea8ca212d77fb7b1f
Peach Compote -  peach compote  -  - Bonne Maman - 0 - 3045320073591 - 3045320073591 - OFF#40281f7e76b3d8f26e6abf673f54d49b
Pommes à la Vanille en Compote -  pomme vanille en compote  -  - Bonne Maman - 0 - 3608580847830 - 3608580847830 - OFF#7721d1878d355acacda616fc6e25c0a4
----------------------------------------------------

ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': 'Compote bonne-maman.', 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Compote', 'normName': ' compote ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#04b7951217cfb84d97b3d59a28797b4e', 'quantity': '', 'quantityLem': '', 'pack': ['YA1.w125'], 'type': 'food', 'gtin': '3045320006919', 'gtinRef': '3045320006919', 'brand': 'Bonne Maman', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.6598873138427734}
----------------------------------------------------------------------------------
LLM CPU Time: 1.6598873138427734