Input path: /home/debian/html/nutritwin/output_llm/660edb1b7d6b2/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edb1b7d6b2/output.json
Input text: Deux oeufs
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: Deux oeufs
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Deux oeufs###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
{
"intents": ["Capture the user food consumption"]
}
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
{
"intents": ["Capture the user food consumption"]
}
------------------------------------------------------
------------------------ After simplification ------------------------
{"intents": ["Capture the user food consumption"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "Deux oeufs".
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.
In the name, ignore the level of cooking mode.
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 how I would analyze the sentence "Deux oeufs":
The sentence is simple and refers to a quantity of eggs. There's no information about the brand, the company, the cooking mode, or the time of day when the eggs were or will be eaten. The event (intent, declaration, or unknown) is also not specified in the sentence.
Here's the JSON formatted result:
```json
[
{
"name": "oeufs",
"quantity": "deux",
"cooking": "",
"brand": "",
"company": "",
"type": "aliment",
"time": "",
"event": "inconnu"
}
]
```
In this case, the type is "aliment" (food) because eggs are a type of food. The event is "inconnu" (unknown) because the sentence does not specify whether the eggs were eaten (declaration) or will be eaten (intent). The time is also not specified in the sentence, so it's left blank.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's how I would analyze the sentence "Deux oeufs":
The sentence is simple and refers to a quantity of eggs. There's no information about the brand, the company, the cooking mode, or the time of day when the eggs were or will be eaten. The event (intent, declaration, or unknown) is also not specified in the sentence.
Here's the JSON formatted result:
```json
[
{
"name": "oeufs",
"quantity": "deux",
"cooking": "",
"brand": "",
"company": "",
"type": "aliment",
"time": "",
"event": "inconnu"
}
]
```
In this case, the type is "aliment" (food) because eggs are a type of food. The event is "inconnu" (unknown) because the sentence does not specify whether the eggs were eaten (declaration) or will be eaten (intent). The time is also not specified in the sentence, so it's left blank.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "oeufs", "quantity": "deux", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "", "event": "inconnu" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'oeufs', 'quantity': 'deux', 'cooking': '', 'brand': '', 'company': '', 'type': 'aliment', 'time': '', 'event': 'inconnu'}], 'cost': 0.059699999999999996}
--------------------------------------------------------------------------------
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 '% oeuf %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Oeuf - oeuf - cru - - 177 - - - CIQ#89c78a1c04879b2ae973694f50092c79
Oeuf - oeuf - dur - - 0 - - - CIQ#fda269f79263c80adf5b9b2c3c29c1d7
Oeuf - oeuf - poché - - 0 - - - CIQ#8d04a52d9c575bdba000c6f1cf343ab0
Oeuf - oeuf - en poudre - - 0 - - - CIQ#f4b4ef030ae3fcf5bbfea0a792a9ab66
Oeuf - oeuf - à la coque - - 3414 - - - CIQ#37567ba433b1d5278fcb1a7813128c96
Oeuf - oeuf - blanc, blanc d'oeuf - - 0 - - - CIQ#f8541a0a53cfc718c4be702af74b13a6
Oeuf - oeuf - jaune, jaune d'oeuf - - 0 - - - CIQ#caff0c1a1a02e4d086dd987b784e898a
Oeuf - oeuf - au plat, frit, salé - - 0 - - - CIQ#f9852838d9a21ae4940ea5102b58e8d1
Oeuf - oeuf - blanc, blanc d'oeuf, cru - - 0 - - - CIQ#91658f86dcc6220b09b2ffc7d5e4d309
Oeuf - oeuf - jaune, jaune d'oeuf, cru - - 0 - - - CIQ#cab44469339c33f14bf4c536019e8f57
Oeuf - oeuf - au plat, sans matière grasse - - 0 - - - CIQ#36e518c64c0e0c5a908f4674e1587a9c
Oeuf - oeuf - brouillé, avec matière grasse - - 0 - - - CIQ#89ffd23269a5b9a6910f6a7bb1a17945
Oeuf - oeuf - blanc, blanc d'oeuf, en poudre - - 0 - - - CIQ#6dc23efe8a247a89ac865e3539278bb1
Oeuf - oeuf - jaune, jaune d'oeuf, en poudre - - 0 - - - CIQ#20ab10b969e15e835fce7d54c1815eeb
Oeuf Dur - oeuf dur - - - 34213 - - - KCA#0c9196f2d28e211ac0aeb81d4c9361a9
Oeuf Poché - oeuf poche - - - 645 - - - KCA#1759edc574d011bf3a8af743ed941e6e
Oeuf d'Oie - oeuf oie - cru - - 0 - - - CIQ#d5a0273c1cb314a819952b4272379b24
Oeuf Miroir - oeuf miroir - - - 11945 - - - KCA#5cf9cdac852c9777e406442fcfd51315
Oeufs Panés - oeuf pane - - - 15 - - - KCA#476eea019750878ec03fbc60bcfa9020
Oeufs Frits - oeuf frit - aux Tomates Provençales - - 13 - - - KCA#1939ad70c8a272d6285ac0509778f087
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Deux oeufs', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Oeuf', 'normName': ' oeuf ', 'comment': 'cru', 'normComment': ' cru ', 'rank': 177, 'id': 'CIQ#89c78a1c04879b2ae973694f50092c79', 'quantity': 'deux', 'quantityLem': '2', 'pack': ['OEU.w60'], 'type': 'aliment', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'inconnu', 'serving': 'OEU-200', 'posiNormName': 0}], 'activity': []}, 'cputime': 7.1264636516571045}
----------------------------------------------------------------------------------
LLM CPU Time: 7.1264636516571045