Input path: /home/debian/html/nutritwin/output_llm/6728f736b163a/input.json
Output path: /home/debian/html/nutritwin/output_llm/6728f736b163a/output.json
Input text: Ce matin j'ai mangé deux tranches de pain avec deux tranches de jambon.
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: Ce matin j'ai mangé deux tranches de pain avec deux tranches de jambon.
==================================================================================================================================
==================================== 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: ###Ce matin j'ai mangé deux tranches de pain avec deux tranches de jambon.###.
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 : """Ce matin j'ai mangé deux tranches de pain avec deux tranches de jambon.""" 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": "pain",
"quantity": "deux tranches",
"type": "food",
"time of the day": "breakfast",
"event": "declaration"
},
{
"name": "jambon",
"quantity": "deux tranches",
"type": "food",
"time of the day": "breakfast",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "pain",
"quantity": "deux tranches",
"type": "food",
"time of the day": "breakfast",
"event": "declaration"
},
{
"name": "jambon",
"quantity": "deux tranches",
"type": "food",
"time of the day": "breakfast",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pain", "quantity": "deux tranches", "type": "food", "time of the day": "breakfast", "event": "declaration" }, { "name": "jambon", "quantity": "deux tranches", "type": "food", "time of the day": "breakfast", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pain', 'quantity': 'deux tranches', 'type': 'food', 'time of the day': 'breakfast', 'event': 'declaration'}, {'name': 'jambon', 'quantity': 'deux tranches', 'type': 'food', 'time of the day': 'breakfast', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'pain', 'quantity': 'deux tranches', 'type': 'food', 'time of the day': 'breakfast', '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 '% pain %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pain - pain - - - 261532 - - - CIQ#78316c0b820d8f80c640c9d0bc741c50
Pain - pain - sans gluten - - 29 - - - CIQ#9d6a800b4a9dbe9504fb68b26057ad7b
Pain - pain - baguette, courante - - 0 - - - CIQ#c92016dc98d790db0bc7c949d601f5c2
Pain - pain - baguette ou boule, au levain - - 0 - - - CIQ#4b65f0348cbdd1f29daadea789369616
Pain - pain - baguette ou boule, de campagne - - 0 - - - CIQ#665da1982ec8e7e74501d57dc7e111b8
Pain - pain - baguette, de tradition française - - 0 - - - CIQ#e5e8a2a86b1a95d66e26a64c18c0b520
Pain - pain - baguette ou boule, bis, à la farine T80 ou T110 - - 0 - - - CIQ#233b9a74f0cc423be7b3fe6fa040567b
Pain - pain - baguette ou boule, bio, à la farine T55 jusqu'à T110 - - 0 - - - CIQ#91fae3ae1c9b87dd0039d7caa03a7d72
Pain - pain - baguette ou boule, aux céréales et graines, artisanal - - 0 - - - CIQ#5fed24621fe6dde995398f020bf84d7d
Pain Bis - pain bi - - - 77 - - - KCA#0d04d397f5620b8618c8972be2ce29a7
Pain Pita - pain pita - - - 951 - - - KCA#0a6b29619370c1e5c09e5ec16992feed
Pain Azyme - pain azyme - - - 1038 - - - KCA#90d292248257ebd4aba91b7e0f6f67d7
Pain Perdu - pain perdu - - - 783 - - - CIQ#67427fe34e70bfc99fd131b16908c1ee
Pain de Son - pain de son - - - 302 - - - KCA#3ccdb3c87985b4f83e1354ee3a2cebfd
Pain au Son - pain son - - - 0 - - - CIQ#825cc00fe7ac81ed34e142fde0f6ddf4
Pain de Mie - pain de mie - au son - - 0 - - - CIQ#1f8d06921f1e892824b0f8cef870e840
Pain de Mie - pain de mie - complet - - 7211 - - - CIQ#d93405497d2314d29dbd770c5b956eeb
Pain de Mie - pain de mie - courant - - 0 - - - CIQ#667832b5357e637fdb28760b7d6c2d8d
Pain Grillé - pain grille - domestique - - 0 - - - CIQ#f4bc68c618fb825e526db4034e88b66a
Pain de Mie - pain de mie - sans croûte - - 32 - - - CIQ#be3f663945b51703d39413cadc3becab
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'jambon', 'quantity': 'deux tranches', 'type': 'food', 'time of the day': 'breakfast', '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 '% jambon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Jambon Cru - jambon cru - - - 9885 - - - CIQ#64b8482a5f9494f91650a6dfbb0cd41e
Jambon Sec - jambon sec - - - 0 - - - CIQ#96c8fe38103fc721a15cfe55d6e25c6f
Jambon Cru - jambon cru - fumé - - 268 - - - CIQ#5f3f73264b7c8e8500821bffaac09aee
Jambon Sec - jambon sec - découenné, dégraissé - - 293 - - - CIQ#25959c69f01c1f2120ccc677017fa727
Jambon Cru - jambon cru - fumé, allégé en matière grasse - - 0 - - - CIQ#f647a53f900ffb0f8b6bcc1b9daac3fd
Jambon Fumé - jambon fume - - - 1235 - - - KCA#b89a3b14af6277985c3d77e8a43fd3a7
Jambon Cuit - jambon cuit - fumé - - 130 - - - CIQ#17ca7e15b0319f1e287cbd0bcf02e149
Jambon Cuit - jambon cuit - choix - - 0 - - - CIQ#31a3ba17bd765304c35083900245a906
Jambon Cuit - jambon cuit - supérieur - - 879 - - - CIQ#62b09fb38df99e94d05d097272b0f943
Jambon Cuit - jambon cuit - choix, avec couenne - - 0 - - - CIQ#c197beb44fda0f03581cdd01ee751078
Jambon Cuit - jambon cuit - supérieur, découenné - - 0 - - - CIQ#a4feb0298e2ed9bf7086021f843d5542
Jambon Cuit - jambon cuit - supérieur, avec couenne - - 0 - - - CIQ#44f954aa2607fc98de99e42c7a2f34f0
Jambon Cuit - jambon cuit - choix, découenné dégraissé - - 0 - - - CIQ#1bdbfa77737e32f3afd8b85235c13da8
Jambon Cuit - jambon cuit - de Paris, découenné dégraissé - - 0 - - - CIQ#2204461860d60e77475581012d525590
Jambon Cuit - jambon cuit - supérieur, découenné dégraissé - - 0 - - - CIQ#7fe80de772280767444b552c0124ab0f
Jambon Cuit - jambon cuit - supérieur, à teneur réduite en sel - - 0 - - - CIQ#f6e3b7457066170ebc96fe96171fba23
Jambon Blanc - jambon blanc - - - 41088 - - - KCA#a2c3580fad4917288fe40406fb88cadb
Jambon Bayonne - jambon bayonne - - - 2108 - - - KCA#a7501ed926d61fc6282a9dc417593554
Jambon Persillé - jambon persille - - - 315 - - - KCA#a68e12a46f2795c6c267b411dd8111f4
Jambon de Poulet - jambon de poulet - - - 5421 - - - KCA#8a8c7fe60575ff37bd0a2f58c58a75a0
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé deux tranches de pain avec deux tranches de jambon.", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Pain', 'normName': ' pain ', 'comment': '', 'normComment': '', 'rank': 261532, 'id': 'CIQ#78316c0b820d8f80c640c9d0bc741c50', 'quantity': 'deux tranches', 'quantityLem': '2 tranche', 'pack': ['PAI.w60', 'BAG.w60', 'TPA.w30'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'TPA-200', 'posiNormName': 0}, {'name': 'Jambon Cru', 'normName': ' jambon cru ', 'comment': '', 'normComment': '', 'rank': 9885, 'id': 'CIQ#64b8482a5f9494f91650a6dfbb0cd41e', 'quantity': 'deux tranches', 'quantityLem': '2 tranche', 'pack': ['TR3.w25'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'TR3-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.7737648487091064}
----------------------------------------------------------------------------------
LLM CPU Time: 2.7737648487091064