Input path: /home/debian/html/nutritwin/output_llm/67c07ce577f94/input.json
Output path: /home/debian/html/nutritwin/output_llm/67c07ce577f94/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/67c07ce577f94/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": "œufs brouillés",
        "quantity": "une portion",
        "cookingMethod": "brouillé",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    },
    {
        "name": "bacon",
        "quantity": "trois tranches",
        "cookingMethod": "grillé",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
[
    {
        "name": "œufs brouillés",
        "quantity": "une portion",
        "cookingMethod": "brouillé",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    },
    {
        "name": "bacon",
        "quantity": "trois tranches",
        "cookingMethod": "grillé",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
------------------------------------------------------

------------------------ After simplification ------------------------
[
    {
        "name": "\u0153ufs brouill\u00e9s",
        "quantity": "une portion",
        "cookingMethod": "brouill\u00e9",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    },
    {
        "name": "bacon",
        "quantity": "trois tranches",
        "cookingMethod": "grill\u00e9",
        "timeOfTheDay": "breakfast",
        "type": "food",
        "event": "declaration"
    }
]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'œufs brouillés', 'quantity': 'une portion', 'cookingMethod': 'brouillé', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}, {'name': 'bacon', 'quantity': 'trois tranches', 'cookingMethod': 'grillé', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------

----------- result to be analyzed -----------
{'name': 'œufs brouillés', 'quantity': 'une portion', 'cookingMethod': 'brouillé', '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 '% oeuf brouille %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Oeufs Brouillés -  oeuf brouille  -  -  - 5704 -  -  - KCA#e7821dbd6e4eed7e40207749566215c6
Oeufs Brouillés au Parme -  oeuf brouille parme  -  -  - 21 -  -  - KCA#46f4f89df78ef4f353027ad54bc58fb2
Oeufs Brouillés au Piment -  oeuf brouille piment  -  -  - 13 -  -  - KCA#206dad6382c9ccfc755dff2b1ea88b47
Oeufs Brouillés à la Bressane -  oeuf brouille bressane  -  -  - 7 -  -  - KCA#3977cd58d40def04a5618de6e2cfc76a
Croque Monsieur aux Oeufs Brouillés -  croque monsieur au oeuf brouille  -  -  - 3 -  -  - KCA#0d6d602aab5ed85a471d72a5fba11639
----------------------------------------------------

ERROR: no solution for picto in the first solution

----------- result to be analyzed -----------
{'name': 'bacon', 'quantity': 'trois tranches', 'cookingMethod': 'grillé', '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 '% bacon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Bacon -  bacon  -  -  - 1249 -  -  - KCA#0482061fb705e4c4fcc5927e178c582b
Bacon Fumé -  bacon fume  -  -  - 1278 -  -  - KCA#d2c907a6b10b4bf58eeff287e3a5bd48
Filet de Bacon -  filet de bacon  -  -  - 0 -  -  - CIQ#a6bdf7b42fb52b081cf21cf9a5d77664
Filet de Bacon, Cuit -  filet de bacon cuit  -  -  - 468 -  -  - KCA#9e5f54ed32945591e73a66b3ada89b73
Noix de Veau au Bacon -  noix de veau bacon  -  -  - 3 -  -  - KCA#8fd91c269e5276f02a8363ef6803c0ab
Huîtres à la Tomate et au Bacon -  huitre tomate bacon  -  -  - 1 -  -  - KCA#525ca26224d0e0b8b24bac6cb44ba9aa
Haricots Borlotti au Sirop d'Érable et au Bacon -  haricot borlotti sirop erable bacon  -  -  - 1 -  -  - KCA#88835bb81177ef9e0a375a4fa749c0ca
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/67c07ce577f94/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Oeufs Brouillés', 'normName': ' oeuf brouille ', 'comment': '', 'normComment': '', 'rank': 5704, 'id': 'KCA#e7821dbd6e4eed7e40207749566215c6', 'quantity': 'une portion', 'quantityLem': '1 portion', 'pack': ['OE2.w60', 'OEU.w60'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'breakfast', 'event': 'declaration', 'serving': '', 'posiNormName': 0}, {'name': 'Bacon', 'normName': ' bacon ', 'comment': '', 'normComment': '', 'rank': 1249, 'id': 'KCA#0482061fb705e4c4fcc5927e178c582b', 'quantity': 'trois tranches', 'quantityLem': '3 tranche', 'pack': ['BAC.w20', 'TR2.w50'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'breakfast', 'event': 'declaration', 'serving': 'BAC-300', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 5.098180294036865}
----------------------------------------------------------------------------------
LLM CPU Time: 5.098180294036865