Input path: /home/debian/html/nutritwin/output_llm/678d445beded8/input.json
Output path: /home/debian/html/nutritwin/output_llm/678d445beded8/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/678d445beded8/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": "velouté de courges",
        "quantity": "un",
        "cookingMethod": "potiron, butternut & potimarron",
        "type": "food",
        "brand": "Green Shoot",
        "event": "unknownEvent"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "velouté de courges",
        "quantity": "un",
        "cookingMethod": "potiron, butternut & potimarron",
        "type": "food",
        "brand": "Green Shoot",
        "event": "unknownEvent"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "velout\u00e9 de courges",
        "quantity": "un",
        "cookingMethod": "potiron, butternut & potimarron",
        "type": "food",
        "brand": "Green Shoot",
        "event": "unknownEvent"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'velouté de courges', 'quantity': 'un', 'cookingMethod': 'potiron, butternut & potimarron', 'type': 'food', 'brand': 'Green Shoot', 'event': 'unknownEvent'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'velouté de courges', 'quantity': 'un', 'cookingMethod': 'potiron, butternut & potimarron', 'type': 'food', 'brand': 'Green Shoot', '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 '% veloute de courge %' AND V_NormTrademark LIKE '%green shoot%' 

--> CPU time in DB: 0.1181 seconds
Word: Velouté de Courgette et Basilic  -  dist: 0.48470696806907654  -  row: 32275
Word: Velouté de Potiron Graines de Courge  -  dist: 0.5028239488601685  -  row: 38567
Word: Velouté Courgettes Basilic  -  dist: 0.5054444074630737  -  row: 49588
Word: Velouté Potiron et Graines de Courge  -  dist: 0.5057839751243591  -  row: 57210
Word: Velouté aux Courgettes et Pointe d'Épices  -  dist: 0.5071565508842468  -  row: 44758
Found embedding word: Velouté de Courgette et Basilic

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 = 'Velouté de Courgette et Basilic'


------------- Found solution (max 20) --------------
Velouté de Courgette et Basilic -  veloute de courgette basilic  -  - Belle France - 0 - 3258561130460 - 3258561130460 - OFF#492825dfe993e494c3a762f8ff60f11b
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/678d445beded8/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Velouté de Courgette et Basilic', 'normName': ' veloute de courgette basilic ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#492825dfe993e494c3a762f8ff60f11b', 'quantity': 'un', 'quantityLem': '1', 'pack': ['ACR.w300', 'BOL', 'MUG'], 'type': 'food', 'gtin': '3258561130460', 'gtinRef': '3258561130460', 'brand': 'Belle France', 'time': '', 'event': 'unknownEvent', 'serving': 'BOL-100', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 4.071078300476074}
----------------------------------------------------------------------------------
LLM CPU Time: 4.071078300476074