Input path: /home/debian/html/nutritwin/output_llm/6703cd3bcd4b8/input.json
Output path: /home/debian/html/nutritwin/output_llm/6703cd3bcd4b8/output.json
Input text: Café sucré.
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: Café sucré.
==================================================================================================================================
==================================== 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: ###Café sucré.###.
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 : """Café sucré.""" 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": "Café",
"type": "beverage",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "Café",
"type": "beverage",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Café", "type": "beverage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Café', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Café', '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 %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Café - cafe - instantané, non sucré, prêt à boire - - 0 - - - CIQ#264e95338204dca4258b74b77eb82c9d
Café - cafe - non instantané, non sucré, prêt à boire - - 0 - - - CIQ#3c8ab223f148936c6d387b43adfd13fd
Café Noir - cafe noir - sucré - - 41467 - - - KCA#4340bea443e4a31592a29591931d64f4
Café Noir - cafe noir - non sucré - - 48621 - - - KCA#7783b77c6af961856829a78ae941e4f5
Café Crème - cafe creme - - - 795 - - - KCA#0fb4970e6ac2d812b39e89ee8fd4d737
Café Liégois - cafe liegoi - - - 213 - - - KCA#c4757bb9d7b5ef114a1b9111b15b705d
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 Soluble - cafe soluble - reconstitué non sucré - - 90 - - - KCA#0c31272ac325fe94fd9d5005ecb8ac13
Café au Lait - cafe lait - café crème ou cappuccino, instantané ou non, non sucré, prêt à boire - - 0 - - - CIQ#61667259d09a30eac4d1919dafb0f043
Café Noisette - cafe noisette - - - 971 - - - KCA#0fc9cdc7bb8a494e3e53719b2bee98c8
Café Expresso - cafe expresso - non instantané, non sucré, prêt à boire - - 5358 - - - CIQ#71484d6749acf1476e8d6abb42471db7
Café Décaféiné - cafe decafeine - sucré - - 984 - - - KCA#8a390d02b1d614cdea70649e29d1eb33
Café Décaféiné - cafe decafeine - instantané, non sucré, prêt à boire - - 0 - - - CIQ#74256f0fb8c48036bc45f36ec358fe89
Café Décaféiné - cafe decafeine - non instantané, non sucré, prêt à boire - - 0 - - - CIQ#ee0c2c2c94c61b5486ce3cdc38d75906
Café Poudre Soluble - cafe poudre soluble - - - 735 - - - KCA#4e1ee649d6587af50fb6c6c59ba70334
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': 'Café sucré.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Café', 'normName': ' cafe ', 'comment': 'instantané, non sucré, prêt à boire', 'normComment': ' instantane non sucre pret boire ', 'rank': 0, 'id': 'CIQ#264e95338204dca4258b74b77eb82c9d', 'quantity': '', 'quantityLem': '', 'pack': ['TA2', 'TA3'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.4479947090148926}
----------------------------------------------------------------------------------
LLM CPU Time: 1.4479947090148926