Input path: /home/debian/html/nutritwin/output_llm/68b5ef2c11f16/input.json
Output path: /home/debian/html/nutritwin/output_llm/68b5ef2c11f16/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/68b5ef2c11f16/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": "miettes de thon à la tomate",
        "quantity": "une boîte",
        "type": "food",
        "event": "unknownEvent"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "miettes de thon à la tomate",
        "quantity": "une boîte",
        "type": "food",
        "event": "unknownEvent"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "miettes de thon \u00e0 la tomate",
        "quantity": "une bo\u00eete",
        "type": "food",
        "event": "unknownEvent"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'miettes de thon à la tomate', 'quantity': 'une boîte', 'type': 'food', 'event': 'unknownEvent'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'miettes de thon à la tomate', 'quantity': 'une boîte', 'type': 'food', 'event': 'unknownEvent'}

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 '% miette de thon tomate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)

--> CPU time in DB: 0.1239 seconds
Word: Miettes de Thon à la Tomate  -  dist: 0.3438268303871155  -  row: 5370
Word: Miettes de Thon A la Tomate  -  dist: 0.3646572232246399  -  row: 19687
Word: Miettes de Thon à la Sauce Tomate  -  dist: 0.3879515528678894  -  row: 14836
Word: Miettes de Thon Tomate Piment  -  dist: 0.44066089391708374  -  row: 19714
Word: Miettes de Thon  -  dist: 0.4451616704463959  -  row: 5372
Found embedding word: Miettes de Thon à la Tomate

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 = 'Miettes de Thon à la Tomate'


------------- Found solution (max 20) --------------
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Lidl - 0 - 20240776 - 20240776 - OFF#5f7e3d88aa663cff0f57803da8f531f7
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Petit Navire - 0 - 3019081236168 - 3019081236168 - OFF#5de7ab59849a1c6d5b5869cd3be2544c
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Casino - 0 - 3222471053355 - 3222471053355 - OFF#09711ea43376b1427bdab0b82b5a6e39
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Monoprix - 0 - 3248650024800 - 3248650024800 - OFF#771e1c11233f26b36e038df8560b2486
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Les Mousquetaires - 0 - 3250391598793 - 3250391598793 - OFF#0e7fcb004dab3ddd1c69510f8c4eaf40
Miettes de Thon à la Tomate -  miette de thon tomate  -  - U - 0 - 3256220358583 - 3256220358583 - OFF#40da2480d7934106af330ded30312571
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Belle France - 0 - 3258561221328 - 3258561221328 - OFF#28908e1285e915661c89b0125cd37502
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Leader Price - 0 - 3263851280312 - 3263851280312 - OFF#2e60172bc9b04f0a5a9a18dd87a27d31
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Carrefour - 0 - 3560071096038 - 3560071096038 - OFF#79dfc0ec87699513792e1dac7a581e6f
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Auchan - 0 - 3596710396184 - 3596710396184 - OFF#a0831f1c22b150b4eee84e4516c78b2f
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Petit Navire - 0 - 3019081236175 - 3019081236168 - OFF#94d12a9b861491e3e4a0d12e42fc83f4
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Petit Navire - 0 - 3019081236199 - 3019081236168 - OFF#f7e5fc8421278eff57b1adfdf4a654e0
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Petit Navire - 0 - 3019081236205 - 3019081236168 - OFF#56d6acf3fccba6a007b3b21e3a0a6ca7
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Casino - 0 - 3222471123966 - 3222471053355 - OFF#750cfb1b465d9dec6c9ecc1b429f43e4
Miettes de Thon à la Tomate -  miette de thon tomate  -  - Monoprix - 0 - 3248650123206 - 3248650024800 - OFF#6445d77445c9785b67711bd256d3807b
Miettes de Thon à la Tomate -  miette de thon tomate  -  - U - 0 - 3256222240459 - 3256220358583 - OFF#46191382b86c3835a7c4f35194742eda
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/68b5ef2c11f16/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Miettes de Thon à la Tomate', 'normName': ' miette de thon tomate ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#5f7e3d88aa663cff0f57803da8f531f7', 'quantity': 'une boîte', 'quantityLem': '1 boite', 'pack': ['TH1.w112'], 'type': 'food', 'gtin': '20240776', 'gtinRef': '20240776', 'brand': 'Lidl', 'time': '', 'event': 'unknownEvent', 'serving': 'TH1-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.7528579235076904}
----------------------------------------------------------------------------------
LLM CPU Time: 3.7528579235076904