Input path: /home/debian/html/nutritwin/output_llm/663a57913aa48/input.json
Output path: /home/debian/html/nutritwin/output_llm/663a57913aa48/output.json
Input text: Combien de calories dans une cousous de chez Picard ?
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: Combien de calories dans une cousous de chez Picard ?
==================================================================================================================================
==================================== 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: ###Combien de calories dans une cousous de chez Picard ?###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
{
"intents": ["Answer a nutrition question"]
}
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
{
"intents": ["Answer a nutrition question"]
}
```
------------------------------------------------------
------------------------ After simplification ------------------------
{ "intents": ["Answer a nutrition question"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """Combien de calories dans une cousous de chez Picard ?""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this 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. 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:event a owl:DatatypeProperty ;
rdfs:label "event"@en;
rdfs:comment "Event of eating or drinking. Each must have an event"@en;
rdfs:range xsd:string.
food:intent a food:event ;
rdfs:label "intent" .
rdfs:comment "When the event should happen"@en.
food:declaration a food:event ;
rdfs:label "declaration" .
rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:event ;
rdfs:label "unknown" ;
rdfs:comment "When the event is unknown in the day"@en.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"food:name": "couscous",
"food:quantity": "une",
"food:brand": "Picard",
"food:event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"food:name": "couscous",
"food:quantity": "une",
"food:brand": "Picard",
"food:event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "food:name": "couscous", "food:quantity": "une", "food:brand": "Picard", "food:event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'food:name': 'couscous', 'food:quantity': 'une', 'food:brand': 'Picard', 'food:event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'couscous', 'quantity': 'une', 'brand': 'Picard', '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 '% couscou %' AND V_NormTrademark LIKE '%picard%'
------------- Found solution (max 20) --------------
Couscous - couscou - - Picard - 0 - 3270160860753 - 3270160860753 - OFF#8b7e171b8124e7f860189f765a6b4ea2
Couscous Royal - couscou royal - - Picard - 0 - 3270160864393 - 3270160864393 - OFF#27c2e7fb882dff234b3b95a4df5e4636
Couscous Royal - couscou royal - - Picard - 0 - 3270160864591 - 3270160864393 - OFF#eb37f0ae8370565f701088c26a87cfe0
Couscous Royal - couscou royal - - Picard - 0 - 3270160864409 - 3270160864393 - OFF#47310ccedfd03dd45940970f64377555
Couscous Surgelé - couscou surgele - - Picard - 0 - 3270160381630 - 3270160381630 - OFF#bb4cdf161b37537f4370e1d8b798e05c
Couscous Tout Bon Tout Veggie - couscou tout bon tout veggie - - Picard - 0 - 3270160861842 - 3270160861842 - OFF#e3316e7124e19b00de23a55912bd6af8
Couscous Royal au Poulet Merguez et Agneau - couscou royal poulet merguez agneau - - Picard - 0 - 3270160290239 - 3270160290239 - OFF#db76e32a04cc89f481754119e22ad67f
Couscous Royal au Poulet Agneau et Merguez - couscou royal poulet agneau merguez - - Picard - 0 - 3270160399802 - 3270160399802 - OFF#30d4db3972a6cf4aaa2e732453d5a098
Graine de Couscous Cuisinée - graine de couscou cuisinee - - Picard - 0 - 3270160751921 - 3270160751921 - OFF#fa5a9984365b1589ac7bac1fdd3c02d2
Légumes pour Couscous - legume pour couscou - - Picard - 0 - 3270160116355 - 3270160116355 - OFF#bffa8f91b6286fd3010ae4793e051cc1
----------------------------------------------------
BA1.w400
==================================== Prompt =============================================
Here is all known information:
For "Couscous", here are the nutrition values:
name: Couscous
GTIN: 3270160860753
brand: Picard
calorie: 122.0Kcal per 100g
reference weight for a unity: 400g
salt: 0.71g per 100g
sugar: 0.0g per 100g
NutriScore: A
EcoScore: B
allergens: en:celery,en:gluten,en:milk
allergen traces: none
data source: Open Food Facts
Answer in less than 50 words to this question with a short explanation if needed: "Combien de calories dans une cousous de chez Picard ?"
" + "Mention the data source in the response if it exists. The answer must be in the same language than the question
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Il y a 122.0Kcal pour 100g de couscous de chez Picard. Donc, pour une unité de 400g, cela fait 488Kcal. Source: Open Food Facts.
-----------------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': 'Il y a 122.0Kcal pour 100g de couscous de chez Picard. Donc, pour une unité de 400g, cela fait 488Kcal. Source: Open Food Facts.', 'cost': 0.0}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Combien de calories dans une cousous de chez Picard ?', 'intents': ['Answer a nutrition question'], 'model': 'gpt-4-0125-preview', 'solutions': {'nutrition': [{'name': 'Couscous', 'normName': ' couscou ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#8b7e171b8124e7f860189f765a6b4ea2', 'quantity': 'une', 'quantityLem': '1', 'pack': ['BA1.w400'], 'type': '', 'gtin': '3270160860753', 'gtinRef': '3270160860753', 'brand': 'Picard', 'time': '', 'event': 'declaration', 'serving': 'BA1-100', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': 'Il y a 122.0Kcal pour 100g de couscous de chez Picard. Donc, pour une unité de 400g, cela fait 488Kcal. Source: Open Food Facts.'}}, 'cputime': 17.39006757736206}
----------------------------------------------------------------------------------
LLM CPU Time: 17.39006757736206