Input path: /home/debian/html/nutritwin/output_llm/67fcbaaf48eb2/input.json
Output path: /home/debian/html/nutritwin/output_llm/67fcbaaf48eb2/output.json
Input text: Ce matin j'ai mangé une tranche de jambon cru.
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: Ce matin j'ai mangé une tranche de jambon cru.
==================================================================================================================================

==================================== 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: ###Ce matin j'ai mangé une tranche de jambon cru.###. 
 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 : """Ce matin j'ai mangé une tranche de jambon cru.""" 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": "jambon cru",
        "quantity": "une tranche",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "jambon cru",
        "quantity": "une tranche",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "jambon cru",
        "quantity": "une tranche",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'jambon cru', 'quantity': 'une tranche', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}], 'cost': 0.09869999999999998}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'jambon cru', 'quantity': 'une tranche', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}

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 '% jambon cru %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Jambon Cru -  jambon cru  -  -  - 9885 -  -  - CIQ#64b8482a5f9494f91650a6dfbb0cd41e
Jambon Cru -  jambon cru  - fumé -  - 268 -  -  - CIQ#5f3f73264b7c8e8500821bffaac09aee
Jambon Cru -  jambon cru  - fumé, allégé en matière grasse -  - 0 -  -  - CIQ#f647a53f900ffb0f8b6bcc1b9daac3fd
Pizza au Speck ou Jambon Cru -  pizza speck ou jambon cru  -  -  - 0 -  -  - CIQ#3f16647ebd7a191191b99b195cd9379f
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé une tranche de jambon cru.", 'model': 'mistral-large-2411', 'imagePath': '', 'intents': ['Identify food and beverage consumption or declaration'], 'solutions': {'nutrition': [{'name': 'Jambon Cru', 'normName': ' jambon cru ', 'comment': '', 'normComment': '', 'rank': 9885, 'id': 'CIQ#64b8482a5f9494f91650a6dfbb0cd41e', 'quantity': 'une tranche', 'quantityLem': '1 tranche', 'pack': ['TR3.w25'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'breakfast', 'event': 'declaration', 'serving': 'TR3-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.4312374591827393}
----------------------------------------------------------------------------------
LLM CPU Time: 2.4312374591827393