Input path: /home/debian/html/nutritwin/output_llm/664af125a2bdc/input.json
Output path: /home/debian/html/nutritwin/output_llm/664af125a2bdc/output.json
Input text: Ce matin j'ai mangé un Danone
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 un Danone
==================================================================================================================================
==================================== 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 un Danone###.
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 un Danone""" 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": "Danone",
"quantity": "",
"cooking method": "",
"type of food": "food",
"time of the day": "breakfast",
"brand": "Danone",
"company": "",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "Danone",
"quantity": "",
"cooking method": "",
"type of food": "food",
"time of the day": "breakfast",
"brand": "Danone",
"company": "",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Danone", "quantity": "", "cooking method": "", "type of food": "food", "time of the day": "breakfast", "brand": "Danone", "company": "", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Danone', 'quantity': '', 'cooking method': '', 'type of food': 'food', 'time of the day': 'breakfast', 'brand': 'Danone', 'company': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Danone', 'quantity': '', 'cooking method': '', 'type of food': 'food', 'time of the day': 'breakfast', 'brand': 'Danone', 'company': '', '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 '% danone %' AND V_NormTrademark LIKE '%danone%'
------------- Found solution (max 20) --------------
Danone - danone - - Danone - 0 - 3411500000075 - 3411500000075 - OFF#27d14e892fc873326f564b968a8338df
Danone - danone - - Danone - 0 - 5410146416897 - 3411500000075 - OFF#80cd44e0ca70a594c1e0923904995885
Danone - danone - - Danone - 0 - 5601050033168 - 3411500000075 - OFF#ca024f4855d6dc294ab03a96e2ddf905
Danone - danone - - Danone - 0 - 5601050033182 - 3411500000075 - OFF#7d73f9aa5909a1476dfc50276a28ea6f
Danone - danone - - Danone - 0 - 8410500018764 - 3411500000075 - OFF#c2886f624e7fb39eb32b4307f5f917bf
Danone Fruit - danone fruit - - Danone - 0 - 5410146416293 - 5410146416293 - OFF#af27b2324883b82587b8cd7240ebef13
Danone Fruix - danone frui - - Danone - 0 - 5410146417115 - 5410146417115 - OFF#10e332fec164717f10f0e4064250be64
Danone Oikos - danone oiko - - Danone - 0 - 8410500022433 - 8410500022433 - OFF#d514b93bd787c04712c355ab4e5ea948
Danone Fruit - danone fruit - - Danone - 0 - 5410146418501 - 5410146416293 - OFF#11c684d26f5e484acc4743a2254eacf1
Danone Fruit - danone fruit - - Danone - 0 - 5410146418471 - 5410146416293 - OFF#6697be9a8a5418984d0842fda2905a8d
Danone Fruit - danone fruit - - Danone - 0 - 5410146420610 - 5410146416293 - OFF#5db3655e177704e67dba62605767f0d6
Danone Fruit - danone fruit - - Danone - 0 - 5410146418495 - 5410146416293 - OFF#d9104c25650f1f18a708e887311dfc9c
Danone Nature - danone nature - - Danone - 0 - 3033490004521 - 3033490004521 - OFF#a7864f0e343d12d8fa2465f15f9c1927
Danone Nature - danone nature - - Danone - 0 - 3330261010119 - 3033490004521 - OFF#60e764eff5a5ea0268ee47d069357c92
Danone Nature - danone nature - - Danone - 0 - 3033491955396 - 3033490004521 - OFF#b308cd49bc976bb029c867e9262675ea
Danone Nature - danone nature - - Danone - 0 - 5410146417146 - 3033490004521 - OFF#52f1f6fa622cbec8da560421d156f78b
Danone Yoothie - danone yoothie - - Danone - 0 - 3033491316692 - 3033491316692 - OFF#e7ca16c29efef8114675f925751349ab
Danone Vanille - danone vanille - - Danone - 0 - 3033491557798 - 3033491557798 - OFF#6660ea570b4d90abc30c47749cfa6816
Danone BIO Vanille - danone bio vanille - - Danone - 0 - 5410146419416 - 3033491339134 - OFF#fdfdfae6920db3f4f48b2052cfc89796
Danone Edition 1919 - danone edition 1919 - - Danone - 0 - 3033491557811 - 3033491557811 - OFF#02d6db071ed9403c701ff569efe0285f
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
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': "Ce matin j'ai mangé un Danone", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Danone', 'normName': ' danone ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#27d14e892fc873326f564b968a8338df', 'quantity': '', 'quantityLem': '', 'pack': ['YA2.w125', 'YA9.w125'], 'type': '', 'gtin': '3411500000075', 'gtinRef': '3411500000075', 'brand': 'Danone', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.3090178966522217}
----------------------------------------------------------------------------------
LLM CPU Time: 2.3090178966522217