Input path: /home/debian/html/nutritwin/output_llm/670ab825ba84b/input.json
Output path: /home/debian/html/nutritwin/output_llm/670ab825ba84b/output.json
Input text: 100 g de hareng fumé nature.
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: 100 g de hareng fumé nature.
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Identify food consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###100 g de hareng fumé nature.###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
{
"intents": ["Identify food consumption or declaration"]
}
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
{
"intents": ["Identify food consumption or declaration"]
}
```
------------------------------------------------------
------------------------ After simplification ------------------------
{ "intents": ["Identify food consumption or declaration"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """100 g de hareng fumé nature.""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this Turtle/RDF model:
"""
@prefix food: .
@prefix rdfs: .
@prefix xsd: .
@prefix owl: .
@prefix 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...)."@en;
rdfs:comment "Ignore food or beverage when it is not consumed in the past, now or in the future."@en;
rdfs:comment "The cooking mode is not in the name. Keep the same language"@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 examples in french: '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. Keep the same language"@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."@en;
rdfs:comment "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.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"name": "hareng",
"quantity": "100 g",
"cooking method": "fumé",
"type of food": "food",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "hareng",
"quantity": "100 g",
"cooking method": "fumé",
"type of food": "food",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "hareng", "quantity": "100 g", "cooking method": "fumé", "type of food": "food", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'hareng', 'quantity': '100 g', 'cooking method': 'fumé', 'type of food': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'hareng', 'quantity': '100 g', 'cooking method': 'fumé', 'type of food': '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 '% hareng %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Hareng - hareng - frit - - 2 - - - CIQ#d2a27231750f63e60632076e2770a386
Hareng - hareng - grillé/poêlé - - 0 - - - CIQ#d26a8a458c2a6eb4e0bafa4640dfa08f
Hareng Frit - hareng frit - - - 0 - - - KCA#37d0241f8bbf75401ef8a5e13bd942db
Hareng Gras - hareng gra - cru - - 0 - - - CIQ#163d20e4299969c6f5b5af848cca7503
Hareng Fumé - hareng fume - à l'huile - - 0 - - - CIQ#4b5a56c9dc1abb7628f04c638e94a6e9
Hareng Fumé - hareng fume - au naturel - - 0 - - - CIQ#51db27e832158a01153de7b87a5a32cf
Hareng Fumé - hareng fume - filet, doux - - 0 - - - CIQ#8680169aba7494aae76dd884c05597eb
Hareng Frais - hareng frai - - - 31 - - - KCA#fea33234d7a64cd0e82ce8454ea27908
Hareng Grillé - hareng grille - - - 8 - - - KCA#c92ebf2bcd63a28671f0459a6120e58e
Hareng Maigre - hareng maigre - cru - - 0 - - - CIQ#c05953202f13d70707b893f959c986d7
Harengs Marinés - hareng marine - - - 168 - - - KCA#2fa4f2823353c66a328481638e51d5da
Harengs Meunière - hareng meuniere - - - 8 - - - KCA#202d14063337e26e153f1fbf94baf5af
Harengs en Croquette - hareng en croquette - - - 1 - - - KCA#d676ed98943ba245efc3586ce1dc889b
Hareng Fumé à l'Huile - hareng fume huile - - - 190 - - - KCA#9d5815dfca1c714dd49353c1cdec3400
Harengs Saurs Grillés - hareng saur grille - - - 2 - - - KCA#8a7b51185b67bd999a9efab3699fcbf7
Harengs Saurs en Salade - hareng saur en salade - - - 56 - - - KCA#4a0cc543a0054a8ce8647ead1ba3015b
Hareng Mariné ou Rollmops - hareng marine ou rollmop - - - 277 - - - KCA#7c1c4eb13d05c4317bc2579a4088e26f
Harengs Farcis aux Herbes - hareng farci au herbe - - - 5 - - - KCA#7d66d0d6fe6e7c7e5bc92cac63af6b0c
Hareng Mariné ou Rollmops - hareng marine ou rollmop - - - 0 - - - CIQ#7c1c4eb13d05c4317bc2579a4088e26f
Harengs à la Moutarde et aux Lentilles - hareng moutarde au lentille - - - 9 - - - KCA#76fbe267126f5c6a222af4f3f2d2e959
----------------------------------------------------
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': '100 g de hareng fumé nature.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Hareng', 'normName': ' hareng ', 'comment': 'frit', 'normComment': ' frit ', 'rank': 2, 'id': 'CIQ#d2a27231750f63e60632076e2770a386', 'quantity': '100 g', 'quantityLem': '100 g', 'pack': ['PO5.w200'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.1604976654052734}
----------------------------------------------------------------------------------
LLM CPU Time: 2.1604976654052734