Input path: /home/debian/html/nutritwin/output_llm/667b2debc67ff/input.json
Output path: /home/debian/html/nutritwin/output_llm/667b2debc67ff/output.json
Input text: Et de la pizza
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: Et de la pizza
==================================================================================================================================
==================================== 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: ###Et de la pizza###.
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 : """Et de la pizza""" 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": "pizza",
"type": "food",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "pizza",
"type": "food",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pizza", "type": "food", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pizza', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'pizza', '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 '% pizza %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pizza - pizza - - - 10599 - - - CIQ#733e507c20c6036da06902e2929056db
Pizza - pizza - - - 0 - - - KCA#733e507c20c6036da06902e2929056db
Pizza - pizza - sauce garniture pour - - 0 - - - CIQ#a275181c086396e0bec873fcc94008cb
Pizza Kebab - pizza kebab - - - 6 - - - CIQ#6bbe41be8630f033bfe294b94bbf8d0c
Pizza Moyenne - pizza moyenne - - - 38 - - - KCA#9bdfcad1de65c2ebcc7384d0aa3fa55f
Pizza au Thon - pizza thon - - - 16 - - - CIQ#5f444b59309014aeab27095b6eb2d95b
Pizza Fromage - pizza fromage - - - 0 - - - KCA#5175d910a3bb5ffe553ada3ee1d50309
Pizza au Poulet - pizza poulet - - - 0 - - - CIQ#33e0a5ea4366eeb0aad919629cf8f008
Pizza au Saumon - pizza saumon - - - 0 - - - CIQ#531c0deee226a1ed25c6ad7e9344ecef
Pizza 4 Fromages - pizza fromage - - - 2361 - - - CIQ#5175d910a3bb5ffe553ada3ee1d50309
Pizza 'Spéciale' - pizza speciale - - - 146 - - - KCA#a6f6dd5434366be39fec21c560e1457e
Pizza à la Poêle - pizza poele - - - 64 - - - KCA#2cd730363965f0d5363b216aaaa75f26
Pizza Boulangerie - pizza boulangerie - - - 318 - - - KCA#291611656924ce924ca7d5200705c55e
Pizza à la Viande - pizza viande - type bolognaise - - 0 - - - CIQ#b17f77e6924678e84c353cde4ec8bdc4
Pizza aux Lardons - pizza au lardon - oignons et fromage - - 0 - - - CIQ#2ff2fb0af20f513208206f7883b4b537
Pizzas Végétariennes - pizza vegetarienne - - - 566 - - - KCA#9f884aabd1a0211b685859e3d93bb8c8
Pizza Jambon Fromage - pizza jambon fromage - - - 405 - - - CIQ#a5c5fe6f659b72fa37b3770428f770e9
Pizza Jambon Fromage - pizza jambon fromage - - - 0 - - - KCA#a5c5fe6f659b72fa37b3770428f770e9
Pizza Tomate et Fromage - pizza tomate fromage - - - 111 - - - KCA#0962f5517452bf8b32ecb09f5f3166da
Pizza aux Fruits de Mer - pizza au fruit de mer - - - 0 - - - CIQ#d9baa96a97f96e931fba42a44879122e
----------------------------------------------------
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': 'Et de la pizza', 'intents': ['Identify food consumption or declaration'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Pizza', 'normName': ' pizza ', 'comment': '', 'normComment': '', 'rank': 10599, 'id': 'CIQ#733e507c20c6036da06902e2929056db', 'quantity': '', 'quantityLem': '', 'pack': ['PIZ.w200.p2'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 1.9253029823303223}
----------------------------------------------------------------------------------
LLM CPU Time: 1.9253029823303223