Input path: /home/debian/html/nutritwin/output_llm/66addbf71210c/input.json
Output path: /home/debian/html/nutritwin/output_llm/66addbf71210c/output.json
Input text: J'ai pris un mugs de café au lait avec une tartine de Pata tartiner et une tartine beurre confiture
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: J'ai pris un mugs de café au lait avec une tartine de Pata tartiner et une tartine beurre confiture
==================================================================================================================================
==================================== 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: ###J'ai pris un mugs de café au lait avec une tartine de Pata tartiner et une tartine beurre confiture###.
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 : """J'ai pris un mugs de café au lait avec une tartine de Pata tartiner et une tartine beurre confiture""" 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 drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. 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. When the 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@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": "café au lait",
"quantity": "un mugs",
"type": "beverage",
"event": "declaration"
},
{
"name": "tartine de Pata tartiner",
"quantity": "une",
"type": "food",
"event": "declaration"
},
{
"name": "tartine beurre confiture",
"quantity": "une",
"type": "food",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "café au lait",
"quantity": "un mugs",
"type": "beverage",
"event": "declaration"
},
{
"name": "tartine de Pata tartiner",
"quantity": "une",
"type": "food",
"event": "declaration"
},
{
"name": "tartine beurre confiture",
"quantity": "une",
"type": "food",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "café au lait", "quantity": "un mugs", "type": "beverage", "event": "declaration" }, { "name": "tartine de Pata tartiner", "quantity": "une", "type": "food", "event": "declaration" }, { "name": "tartine beurre confiture", "quantity": "une", "type": "food", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'café au lait', 'quantity': 'un mugs', 'type': 'beverage', 'event': 'declaration'}, {'name': 'tartine de Pata tartiner', 'quantity': 'une', 'type': 'food', 'event': 'declaration'}, {'name': 'tartine beurre confiture', 'quantity': 'une', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'café au lait', 'quantity': 'un mugs', 'type': 'beverage', '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 '% cafe lait %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Café au Lait - cafe lait - entier sucré - - 686 - - - KCA#79a7269ac953a86d5d8964ee0f4152db
Café au Lait - cafe lait - écrémé sucré - - 653 - - - KCA#cea770a189e838bbc39e36cf537abb5a
Café au Lait - cafe lait - 1/2 écrémé sucré - - 15199 - - - KCA#138ec7dba7fa585306b852c3f7e0a463
Café au Lait - cafe lait - écrémé non sucré - - 6369 - - - KCA#eefa4e0f868d9c342316060e62f23159
Café au Lait - cafe lait - entier non sucré - - 1063 - - - KCA#766d75aba9738d735cfb5303e24e0712
Café au Lait - cafe lait - 1/2 écrémé non sucré - - 21616 - - - KCA#e8f1a390014f879ed671041ebfeb6366
Café au Lait - cafe lait - café crème ou cappuccino, instantané ou non, non sucré, prêt à boire - - 0 - - - CIQ#61667259d09a30eac4d1919dafb0f043
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'tartine de Pata tartiner', 'quantity': 'une', '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 '% tartine de pata tartiner %' 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 '% tartine de pata tartiner %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
------ERROR--------------------------------
No solution for query: 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 '% tartine de pata tartiner %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
-------------------------------------------
----------- result to be analyzed -----------
{'name': 'tartine beurre confiture', 'quantity': 'une', '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 '% tartine beurre confiture %' 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 '% tartine beurre confiture %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
------ERROR--------------------------------
No solution for query: 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 '% tartine beurre confiture %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
-------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "J'ai pris un mugs de café au lait avec une tartine de Pata tartiner et une tartine beurre confiture", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Café au Lait', 'normName': ' cafe lait ', 'comment': 'entier sucré', 'normComment': ' entier sucre ', 'rank': 686, 'id': 'KCA#79a7269ac953a86d5d8964ee0f4152db', 'quantity': 'un mugs', 'quantityLem': '1 mug', 'pack': ['TA2', 'TAS', 'TAC', 'TA3', 'MUG', 'BOL'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'MUG-100', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.6745851039886475}
----------------------------------------------------------------------------------
LLM CPU Time: 2.6745851039886475