Input path: /home/debian/html/nutritwin/output_llm/66cc8bf43d413/input.json
Output path: /home/debian/html/nutritwin/output_llm/66cc8bf43d413/output.json
Input text: How much calories in 3 bananas ?
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: How much calories in 3 bananas ?
==================================================================================================================================
==================================== 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: ###How much calories in 3 bananas ?###.
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 : """How much calories in 3 bananas ?""" 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": "banana",
"quantity": "3",
"cooking method": "",
"type of food": "food",
"time of the day": "",
"brand": "",
"company": "",
"event": "unknown"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "banana",
"quantity": "3",
"cooking method": "",
"type of food": "food",
"time of the day": "",
"brand": "",
"company": "",
"event": "unknown"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "banana", "quantity": "3", "cooking method": "", "type of food": "food", "time of the day": "", "brand": "", "company": "", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'banana', 'quantity': '3', 'cooking method': '', 'type of food': 'food', 'time of the day': '', 'brand': '', 'company': '', 'event': 'unknown'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'banana', 'quantity': '3', 'cooking method': '', 'type of food': 'food', 'time of the day': '', 'brand': '', 'company': '', 'event': 'unknown'}
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 '% banana %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Banana Split - banana split - - - 78 - - - KCA#40ca76eb0daa7cd59e4a57177e4db0b4
----------------------------------------------------
AAD.w150
==================================== Prompt =============================================
Here is all known information:
For "Banana Split", here are the nutrition values:
name: Banana Split
GTIN: none
brand: none
calorie: 275.0Kcal per 100g
reference weight for a unity: 150g
salt: 0.13g per 100g
sugar: -1.0g per 100g
NutriScore: none
EcoScore: none
allergens: none
allergen traces: none
data source: KcalMe
Answer in less than 50 words to this question with a short explanation if needed: "How much calories in 3 bananas ?"
" + "Mention the data source in the response if it exists. The answer must be in the same language than the question
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Les informations fournies concernent un Banana Split, pas des bananes. Source: KcalMe.
-----------------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': 'Les informations fournies concernent un Banana Split, pas des bananes. Source: KcalMe.', 'cost': 0.0}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'How much calories in 3 bananas ?', 'intents': ['Answer a nutrition question'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Banana Split', 'normName': ' banana split ', 'comment': '', 'normComment': '', 'rank': 78, 'id': 'KCA#40ca76eb0daa7cd59e4a57177e4db0b4', 'quantity': '3', 'quantityLem': '3', 'pack': ['AAD.w150'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': 'AAD-300', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': 'Les informations fournies concernent un Banana Split, pas des bananes. Source: KcalMe.'}}, 'cputime': 2.8509323596954346}
----------------------------------------------------------------------------------
LLM CPU Time: 2.8509323596954346