Input path: /home/debian/html/nutritwin/output_llm/662a9a01128bc/input.json
Output path: /home/debian/html/nutritwin/output_llm/662a9a01128bc/output.json
Input text: Pâtes au beurre
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: Pâtes au beurre
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Pâtes au beurre###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
{
"intents": ["Answer a nutrition question"]
}
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
{
"intents": ["Answer a nutrition question"]
}
------------------------------------------------------
------------------------ After simplification ------------------------
{"intents": ["Answer a nutrition question"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "Pâtes au beurre".
I want to identify for the food or beverage: the name, the type, the quantity for each ingredient and, if it exists, identify the brand, the cooking mode and the company name.
Containers, like "canette" or "verre", are quantities and not ingredients or food product.
"Portions", like "tranche", are quantities.
"Quantity" is in french.
"Company" is the company of the brand.
"Quignon" is a quantity.
Ignore what it is not connected to nutrition, beverage or food.
Music and is not nutrition.
Extract how the product is consumed.
The level of cooking mode is not in the name.
There is no quantity in the name, ex: the name for "une pomme" is "pomme".
When brand is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "".
Ignore the actions.
The restaurants are not brand.
Identify what type of food.
Ignore food with a negative verb, ex "Je n'ai pas pris de viande".
Do not extract ingredients for product with a brand.
If the food or beverage consumption is in the past, the event is a "declaration", for example: "J'ai mangé du pain", the event is a declaration.
If the food or beverage consumption will be in the future or even soon, the event is an "intent", for example: "Je vais manger du pain", the event is an intent.
Otherwise the event is unknown.
Map the event of eating on ["intent", "declaration", "unknown"].
Identify the time of day when the foods were eaten and map it on "petit-déjeuner", "déjeuner", "grignotage" or "dîner".
Format the result in french in JSON in an array of tuples {"name":, "quantity":, "cooking":, "brand":, "company":, "type":, "time":, "event":}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Based on your instructions, here's the analysis of the sentence "Pâtes au beurre".
The sentence translates to "Butter pasta" in English.
Here's the formatted result:
```json
[
{
"name": "pâtes",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "unknown",
"event": "unknown"
},
{
"name": "beurre",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "unknown",
"event": "unknown"
}
]
```
Explanation:
- The name of the foods are "pâtes" (pasta) and "beurre" (butter).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand and the company are not specified in the sentence.
- The type of both foods is "food".
- The time of day when the foods were eaten is not specified in the sentence.
- The event (intent, declaration, unknown) is not specified in the sentence.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Pâtes au beurre".
The sentence translates to "Butter pasta" in English.
Here's the formatted result:
```json
[
{
"name": "pâtes",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "unknown",
"event": "unknown"
},
{
"name": "beurre",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "unknown",
"event": "unknown"
}
]
```
Explanation:
- The name of the foods are "pâtes" (pasta) and "beurre" (butter).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand and the company are not specified in the sentence.
- The type of both foods is "food".
- The time of day when the foods were eaten is not specified in the sentence.
- The event (intent, declaration, unknown) is not specified in the sentence.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pâtes", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "food", "time": "unknown", "event": "unknown" }, { "name": "beurre", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "food", "time": "unknown", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pâtes', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'food', 'time': 'unknown', 'event': 'unknown'}, {'name': 'beurre', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'food', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.06828}
--------------------------------------------------------------------------------
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 '% pate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pâté - pate - - - 35 - - - CIQ#afa9f7f047da1f15de2883f037186a92
Pâtes - pate - sans gluten, à base de riz et maïs, à l'eau, non salées - - 0 - - - CIQ#fbb4c57fdca55e795247628ccb5aecdd
Pâtes - pate - sans gluten, à base de lentilles corail, à l'eau, non salées - - 0 - - - CIQ#b616881505a8dc3bb22f36ba73c591e5
Pâté Breton - pate breton - - - 0 - - - CIQ#7bf7cf124b0a4bd2e2ef3a9a0a499589
Pâtes Cuites - pate cuite - - - 40303 - - - KCA#5f79f58611165eed8a9639bfa123a9ca
Pâté de Foie - pate de foie - - - 754 - - - KCA#a5e2912dd9f9cde202e6768375fa2481
Pâté de Tête - pate de tete - - - 191 - - - KCA#f90aa2ff530cc5bc04459e1ca2ba4490
Pâtes Sèches - pate seche - aux oeufs, crues - - 0 - - - CIQ#52cf76f71ceae840a6e8cfb7bb87401e
Pâtes Sèches - pate seche - sans gluten, crues - - 0 - - - CIQ#a6df809c43c5e8ea99c2290e16e50a23
Pâtes Sèches - pate seche - au blé complet, crues - - 0 - - - CIQ#2cd29b7b7d0a8beffb2a20bdcd5b67d9
Pâtes Sèches - pate seche - aux oeufs, non salées - - 0 - - - CIQ#475f5a3e0ebed8ce058915c8c0e2488a
Pâtes Sèches - pate seche - sans gluten, non salées - - 0 - - - CIQ#a83a046d5cb792a1634de34a8b103f8c
Pâtes Sèches - pate seche - au blé complet, non salées - - 0 - - - CIQ#086a2b5c3417a99bed48fb94c6f8e347
Pâte d'Amande - pate amande - - - 753 - - - CIQ#7c0811ad432704e3560ead7d11dcc54b
Pâté de Lapin - pate de lapin - - - 228 - - - CIQ#cd9ac9416e8376ef0d33dc474b22d8d1
Pâte de Fruits - pate de fruit - - - 904 - - - CIQ#ddc417db85ad45f7b63c72987afd1efd
Pâté en Croûte - pate en croute - - - 69 - - - CIQ#e2118c3e025007fd1644c613af45b0cf
Pâté de Gibier - pate de gibier - - - 62 - - - CIQ#68811d74011dd1931c6725029c3ec0d8
Pâté Ardennais - pate ardennai - - - 33 - - - KCA#1c1510a6deb74a99fe2687d0ba87d678
Pâtes Fraîches - pate fraiche - aux oeufs, crues - - 0 - - - CIQ#9afbc65919a12bd31e467b9e01a43777
----------------------------------------------------
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
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 '% beurre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Beurre Doux - beurre dou - - - 78338 - - - KCA#70e9c7551c333018468c7d83aa296ffb
Beurre Allégé - beurre allege - - - 951 - - - KCA#e5fa3181212a47a965532c52b5386037
Beurre Demi-sel - beurre demi sel - - - 4320 - - - KCA#35fac54cc4123c12fb12b350abb9b457
Beurre à 80% MG - beurre 80% mg - salé - - 0 - - - CIQ#d5e469e83eef82ff717fa1e221cde650
Beurre à 82% MG - beurre 82% mg - doux - - 0 - - - CIQ#3a7a92623060e0f381d677274ed57211
Beurre à 80% MG - beurre 80% mg - demi-sel - - 0 - - - CIQ#35e38c233cccb53f6cefdd1ec56c4b5c
Beurre à 82% MG - beurre 82% mg - doux, tendre - - 0 - - - CIQ#786b1aab29e436814daeb984092dd932
Beurre à 39-41% MG - beurre 39 41% mg - léger, doux - - 0 - - - CIQ#ff256be25c78202b0129214d377078fb
Beurre à 60-62% MG - beurre 60 62% mg - à teneur réduite en matière grasse, doux - - 0 - - - CIQ#73ce45af94ffc409f4861c7199a2763b
Beurre à 60-62% MG - beurre 60 62% mg - à teneur réduite en matière grasse, demi-sel - - 0 - - - CIQ#66500be73ce80bda379e9a7f7301e55e
Beurre ou Assimilé Allégé - beurre ou assimile allege - léger ou à teneur reduite en matière grasse, doux, aliment moyen - - 0 - - - CIQ#39265899332bfa2f5a199208dbbcc2e4
Beurre Léger 39-41% MG Doux - beurre leger 39 41% mg dou - - - 148 - - - KCA#3c9a43dcdff911e58d4868ccce7295f5
Beurre Léger 39-41% MG Demi-sel - beurre leger 39 41% mg demi sel - - - 652 - - - KCA#1379376ecc87973b7f43402c587fe353
Beurre à Teneur en Matière Grasse Inconnue - beurre teneur en matiere grasse inconnue - allégé ou non, demi-sel, aliment moyen - - 0 - - - CIQ#b13b44b9cead6168da8f188d7acc178b
Beurre ou Assimilé à Teneur en Matière Grasse Inconnue - beurre ou assimile teneur en matiere grasse inconnue - doux, aliment moyen - - 0 - - - CIQ#4b726ca38cd9d14e2956083e7e442827
Raie au Beurre Noir - raie beurre noir - - - 78 - - - KCA#89fb07f55a25aa8cbb884e6ce5baa12c
Crème au Beurre - creme beurre - - - 43 - - - KCA#8239042903c3d4e3b577b2c276b39ee8
Crêpe Beurre Sucre - crepe beurre sucre - - - 1261 - - - KCA#adf06f3567a044e35baea32b960ad4dc
Petit Beurre Industriel - petit beurre industriel - - - 41 - - - KCA#99d8a990698f19e6a4feca4736bb7a6c
Haricot Beurre - haricot beurre - bouilli/cuit à l'eau - - 0 - - - CIQ#114be26033d424582a109fd9eea38878
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
TR5.w150
NOI.w10,BEU.w15
==================================== Prompt =============================================
Here is all known information:
For "Pâté", here are the nutrition values:
name: Pâté
GTIN: none
brand: none
calorie: 325.0Kcal per 100g
reference weight for a unity: 150g
salt: 1.58g per 100g
sugar: 1.23g per 100g
NutriScore: none
EcoScore: none
allergens: none
allergen traces: none
data source: Ciqual
For "Beurre Doux", here are the nutrition values:
name: Beurre Doux
GTIN: none
brand: none
calorie: 760.0Kcal per 100g
reference weight for a unity: 15g
salt: 0.02794g 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: "Pâtes au beurre"
" + "Mention the data source in the response if it exists. The answer must be in the same language than the question
=========================================================================================
------------------------------ LLM Raw response -----------------------------
"Pâtes au beurre" is not a specific food item provided in the data. However, it's typically made with pasta and butter. The nutrition values would vary based on the specific types and amounts of pasta and butter used. The data sources for similar items are Ciqual and KcalMe.
-----------------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': '"Pâtes au beurre" is not a specific food item provided in the data. However, it\'s typically made with pasta and butter. The nutrition values would vary based on the specific types and amounts of pasta and butter used. The data sources for similar items are Ciqual and KcalMe.', 'cost': 0.02712}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Pâtes au beurre', 'intents': ['Answer a nutrition question'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Pâté', 'normName': ' pate ', 'comment': '', 'normComment': '', 'rank': 35, 'id': 'CIQ#afa9f7f047da1f15de2883f037186a92', 'quantity': '', 'quantityLem': '', 'pack': ['TR5.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}, {'name': 'Beurre Doux', 'normName': ' beurre dou ', 'comment': '', 'normComment': '', 'rank': 78338, 'id': 'KCA#70e9c7551c333018468c7d83aa296ffb', 'quantity': '', 'quantityLem': '', 'pack': ['NOI.w10', 'BEU.w15'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {'type': 'text', 'data': '"Pâtes au beurre" is not a specific food item provided in the data. However, it\'s typically made with pasta and butter. The nutrition values would vary based on the specific types and amounts of pasta and butter used. The data sources for similar items are Ciqual and KcalMe.'}}, 'cputime': 14.902840375900269}
----------------------------------------------------------------------------------
LLM CPU Time: 14.902840375900269