Input path: /home/debian/html/nutritwin/output_llm/68e39db40a1e0/input.json
Output path: /home/debian/html/nutritwin/output_llm/68e39db40a1e0/output.json
Input text: 
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: 
==================================================================================================================================
Image to be analyzed: /home/debian/html/nutritwin/output_llm/68e39db40a1e0/capture.jpg
##############################################################################################
#   For image extraction, pixtral-large-2411 is used    #
##############################################################################################

==================================== Prompt =============================================
In the image, identify all the foods and beverages, convert them into an array of JSON with consumed foods.
Ignore what it is not connected to nutrition, beverage or food. 
When a food or a beverage has several instances unify them on a single food or beverage and add the quantities of each.
The attribute name must remain in English but the result, so the attribute value, must be in french, and only in french.

Provide a solution without explanation.

Use only the food & beverage 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": "chocolat aromatisé noisette",
        "quantity": "un",
        "type": "food",
        "brand": "Danette",
        "company": "Danone",
        "event": "declaration"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "chocolat aromatisé noisette",
        "quantity": "un",
        "type": "food",
        "brand": "Danette",
        "company": "Danone",
        "event": "declaration"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "chocolat aromatis\u00e9 noisette",
        "quantity": "un",
        "type": "food",
        "brand": "Danette",
        "company": "Danone",
        "event": "declaration"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'chocolat aromatisé noisette', 'quantity': 'un', 'type': 'food', 'brand': 'Danette', 'company': 'Danone', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'chocolat aromatisé noisette', 'quantity': 'un', 'type': 'food', 'brand': 'Danette', 'company': 'Danone', '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 '% chocolat aromatise noisette %' AND V_NormTrademark LIKE '%danette%' 

--> CPU time in DB: 0.1187 seconds
Word: Chocolat au Lait Noisette  -  dist: 0.5327698588371277  -  row: 25294
Word: Chocolat au Lait Noisettes  -  dist: 0.5428718328475952  -  row: 5592
Word: Chocolat Noisette  -  dist: 0.5438801050186157  -  row: 51842
Word: Chocolat aux Noisettes  -  dist: 0.5485817790031433  -  row: 23698
Word: Chocolat Noir aux Noisettes  -  dist: 0.5537505149841309  -  row: 7531
Found embedding word: Chocolat au Lait Noisette

Second try (embedded):
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_Name = 'Chocolat au Lait Noisette'


------------- Found solution (max 20) --------------
Chocolat au Lait Noisette -  chocolat lait noisette  -  - Carrefour - 0 - 3245390122100 - 3245390122100 - OFF#c40b1ed9d16aa48ba31e29391b69b039
Chocolat au Lait Noisette -  chocolat lait noisette  -  - Mondelez International - 0 - 7622210926005 - 5410081203965 - OFF#4f7fe3ac44f97b8e97720ca5f7c3d861
----------------------------------------------------

ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/68e39db40a1e0/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Chocolat au Lait Noisette', 'normName': ' chocolat lait noisette ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#c40b1ed9d16aa48ba31e29391b69b039', 'quantity': 'un', 'quantityLem': '1', 'pack': ['CHO.w8'], 'type': 'food', 'gtin': '3245390122100', 'gtinRef': '3245390122100', 'brand': 'Carrefour', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 4.388517379760742}
----------------------------------------------------------------------------------
LLM CPU Time: 4.388517379760742