Input path: /home/debian/html/nutritwin/output_llm/670a21eb45ccb/input.json
Output path: /home/debian/html/nutritwin/output_llm/670a21eb45ccb/output.json
Input text: 180 2 g de lait demi écrémé.
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: 180 2 g de lait demi écrémé.
==================================================================================================================================
==================================== 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: ###180 2 g de lait demi écrémé.###.
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 : """180 2 g de lait demi écrémé.""" 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": "lait demi écrémé",
"quantity": "180 2 g",
"type": "beverage",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "lait demi écrémé",
"quantity": "180 2 g",
"type": "beverage",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "lait demi écrémé", "quantity": "180 2 g", "type": "beverage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'lait demi écrémé', 'quantity': '180 2 g', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'lait demi écrémé', 'quantity': '180 2 g', '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 '% lait demi ecreme %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Lait Demi-écrémé - lait demi ecreme - UHT - - 1882 - - - CIQ#a52f3d296711c40b0cf1a9e387dd93c3
Lait Demi-écrémé - lait demi ecreme - pasteurisé - - 0 - - - CIQ#ba021562100b158345cde3da8ea5941f
Lait Demi-écrémé - lait demi ecreme - UHT, enrichi en vitamine D seulement - - 0 - - - CIQ#386868d6d030c7295418701a8d2582af
Lait Demi-écrémé - lait demi ecreme - ou à teneur en matière grasse légèrement inférieure, à teneur réduite en lactose - - 0 - - - CIQ#bcb97383db7bbbc0b61664db635f7d92
Lait Demi-écrémé UHT - lait demi ecreme uht - - - 0 - - - KCA#a4ffea6cb369217cc82c2068b37730db
Lait Demi-écrémé Aromatisé - lait demi ecreme aromatise - - - 38 - - - KCA#5f315c448ad7d9f47c565c26bc467d76
Lait Demi-écrémé en Poudre - lait demi ecreme en poudre - - - 13 - - - KCA#142dc57784314bb5d9232ef1c9c8c155
Lait Demi-écrémé Pasteurisé - lait demi ecreme pasteurise - - - 62 - - - KCA#3b75e189515de06da4c3d229418838e1
----------------------------------------------------
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': '180 2 g de lait demi écrémé.', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Lait Demi-écrémé', 'normName': ' lait demi ecreme ', 'comment': 'UHT', 'normComment': ' uht ', 'rank': 1882, 'id': 'CIQ#a52f3d296711c40b0cf1a9e387dd93c3', 'quantity': '180 2 g', 'quantityLem': '180 2 g', 'pack': ['VX1', 'VA2', 'VA3', 'BI4', 'VA4'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.254779815673828}
----------------------------------------------------------------------------------
LLM CPU Time: 2.254779815673828