Input path: /home/debian/html/nutritwin/output_llm/6721dce67d97e/input.json
Output path: /home/debian/html/nutritwin/output_llm/6721dce67d97e/output.json
Input text: Deux cuillères de confiture à la figue.
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: Deux cuillères de confiture à la figue.
==================================================================================================================================
==================================== 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: ###Deux cuillères de confiture à la figue.###.
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 : """Deux cuillères de confiture à la figue.""" 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": "confiture à la figue",
"quantity": "Deux cuillères",
"type": "food",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "confiture à la figue",
"quantity": "Deux cuillères",
"type": "food",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "confiture à la figue", "quantity": "Deux cuillères", "type": "food", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'confiture à la figue', 'quantity': 'Deux cuillères', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'confiture à la figue', 'quantity': 'Deux cuillères', '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 '% confiture figue %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second 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_NormAggr LIKE '% confiture figue %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Confiture Extra Figue - confiture extra figue - - Cora - 0 - 3257983138955 - 3257983138955 - OFF#9e3928d2862d1251e2048a9bfbfd69e2
Confiture Extra de Figues 50% Fruits BIO - confiture extra de figue 50% fruit bio - - Intermarché - 0 - 3250392554682 - 3250392554682 - OFF#f710bb81fbf05f674a30a9b628f93456
Confiture Figues - confiture figue - - Bonne Maman - 0 - 3045320522181 - 3045320522181 - OFF#41f27060249e4580f2b6ed6083a81294
Confiture Figues - confiture figue - - Leclerc - 0 - 3564706684485 - 3564706684485 - OFF#9632aeea2449aa4a33cd9665aeedcb42
Confiture Figues - confiture figue - - Delhaize - 0 - 5400113520948 - 5400113520948 - OFF#2958a32cde573a3e2540648e5439428a
Confiture Figue BIO - confiture figue bio - - Carrefour - 0 - 3560071501723 - 3560071501723 - OFF#a1c5973ebf0d39244b07c65c5dafe7d6
Confiture Figues Extra - confiture figue extra - - Coteaux Nantais - 0 - 3301595000480 - 3301595000480 - OFF#8ca9a90c9bca209e87e8a6fdb7a3af82
Confiture Figues Extra - confiture figue extra - - Coteaux Nantais - 0 - 3760076651939 - 3301595000480 - OFF#ef8939ec582d4fdee74650a3a5a884b5
Confiture Figue Allégée - confiture figue allegee - - U - 0 - 3256220851084 - 3256220851084 - OFF#ab868e9484b30f00d84585acab0c0506
Confiture Figue Blanche - confiture figue blanche - - Les Mousquetaires - 0 - 3330720251060 - 3330720251060 - OFF#32ff1162dd376b5ded3a6c653ce7bf07
Confiture Figue Intense - confiture figue intense - - Bonne Maman - 0 - 3608580823506 - 3608580823506 - OFF#83d94b7cd92aa568a8305cdf9d75d275
Confiture Figue Violette - confiture figue violette - - Jardin Bio - 0 - 3307131305033 - 3307131305033 - OFF#698d281ba18b70066948ab819a8a4be2
Confiture Figue Violette - confiture figue violette - - Auchan - 0 - 3596710418213 - 3596710418213 - OFF#3f0e1ca85b14907904f5a7141930ed23
Confiture Figues Violettes - confiture figue violette - - Bonne Maman - 0 - 3608580793328 - 3608580793328 - OFF#20c76a85ebac7f56d03b94bb9f8b23e9
Confiture la Figue de Provence - confiture figue de provence - - Franprix - 0 - 3263855406015 - 3263855406015 - OFF#1d335aceca83f4da198c011940225291
Confiture Figues Abricots Amandes - confiture figue abricot amande - - Barilla - 0 - 8076809577915 - 8076809577915 - OFF#e3f572774074cf97ce3d6cf79635b4f2
Confiture Figue Intense Moins Sucrée - confiture figue intense moin sucree - - Bonne Maman - 0 - 3608580034407 - 3608580034407 - OFF#50ab2ac7d4452bf2eed5dbe78cba957a
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Deux cuillères de confiture à la figue.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Confiture Extra Figue', 'normName': ' confiture extra figue ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#9e3928d2862d1251e2048a9bfbfd69e2', 'quantity': 'Deux cuillères', 'quantityLem': '2 cuillere', 'pack': ['CCL.w10'], 'type': 'food', 'gtin': '3257983138955', 'gtinRef': '3257983138955', 'brand': 'Cora', 'time': '', 'event': 'declaration', 'serving': 'CCL-200', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 1.2933695316314697}
----------------------------------------------------------------------------------
LLM CPU Time: 1.2933695316314697