Input path: /home/debian/html/nutritwin/output_llm/673f6b5a25c1b/input.json
Output path: /home/debian/html/nutritwin/output_llm/673f6b5a25c1b/output.json
Input text: Ce matin j'ai mangé deux bananes.
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 bananes.
==================================================================================================================================
==================================== 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 bananes.###.
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 bananes.""" 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": "banane",
"quantity": "deux",
"type": "food",
"event": "declaration",
"time of the day": "breakfast"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "banane",
"quantity": "deux",
"type": "food",
"event": "declaration",
"time of the day": "breakfast"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "banane", "quantity": "deux", "type": "food", "event": "declaration", "time of the day": "breakfast" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'banane', 'quantity': 'deux', 'type': 'food', 'event': 'declaration', 'time of the day': 'breakfast'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'banane', 'quantity': 'deux', 'type': 'food', 'event': 'declaration', 'time of the day': 'breakfast'}
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 '% banane %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Banane - banane - pulpe, crue - - 57967 - - - CIQ#6066b5bb884711efc0e44c9446b96aa3
Banane Sèche - banane seche - - - 346 - - - KCA#2e3e40d3b1ae9f793251e9948142d784
Bananes en Robe - banane en robe - - - 14 - - - KCA#b274666ef64f762c58695191d4286b85
Banane Plantain - banane plantain - - - 2 - - - CIQ#1055a76a23712202f3c842fba09fa691
Bananes Barbecue - banane barbecue - - - 33 - - - KCA#1d31fb8efe54f0bc7765a60cc9f8c324
Bananes au Jambon - banane jambon - - - 4 - - - KCA#e21d980b838ba89f4e9ba1d85f593c95
Smoothie Banane et Lait de Soja - smoothie banane lait de soja - de soja - - 0 - - - KCA#dc0b16a02e5290892f9adee7419ec0e7
Crème Glacée Banane, Pomme et Noix de Macadamia - creme glacee banane pomme noix de macadamia - - - 34 - - - KCA#3233d39965b7baa31d10a301ac541ffa
Bruschette à la Fraise, à la Banane et à la Ricotta - bruschette fraise banane ricotta - - - 2 - - - KCA#fd9db147f698ab1c84b0905704258a5f
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé deux bananes.", 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Banane', 'normName': ' banane ', 'comment': 'pulpe, crue', 'normComment': ' pulpe crue ', 'rank': 57967, 'id': 'CIQ#6066b5bb884711efc0e44c9446b96aa3', 'quantity': 'deux', 'quantityLem': '2', 'pack': ['BAN.w100'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'BAN-200', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.799032211303711}
----------------------------------------------------------------------------------
LLM CPU Time: 1.799032211303711