Input path: /home/debian/html/nutritwin/output_llm/661bf02dbe29e/input.json
Output path: /home/debian/html/nutritwin/output_llm/661bf02dbe29e/output.json
Input text: Ce matin j'ai bu un grand verre de lait
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: Ce matin j'ai bu un grand verre de lait
==================================================================================================================================
==================================== 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: ###Ce matin j'ai bu un grand verre de lait###.
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: "Ce matin j'ai bu un grand verre de lait".
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 the analysis of the sentence "Ce matin j'ai bu un grand verre de lait".
The food item is "lait" (milk). The quantity is "un grand verre" (a large glass). The brand and company are not specified. The type of food is a beverage. The time of day is "ce matin" (this morning), which corresponds to "petit-déjeuner" (breakfast). The event is a "declaration" because the consumption is in the past.
The result in JSON format would look like this:
```json
[
{
"name": "lait",
"quantity": "un grand verre",
"cooking": "",
"brand": "",
"company": "",
"type": "boisson",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai bu un grand verre de lait".
The food item is "lait" (milk). The quantity is "un grand verre" (a large glass). The brand and company are not specified. The type of food is a beverage. The time of day is "ce matin" (this morning), which corresponds to "petit-déjeuner" (breakfast). The event is a "declaration" because the consumption is in the past.
The result in JSON format would look like this:
```json
[
{
"name": "lait",
"quantity": "un grand verre",
"cooking": "",
"brand": "",
"company": "",
"type": "boisson",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "lait", "quantity": "un grand verre", "cooking": "", "brand": "", "company": "", "type": "boisson", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'lait', 'quantity': 'un grand verre', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.05711999999999999}
--------------------------------------------------------------------------------
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 '% lait %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Lait - lait - teneur en matière grasse inconnue, UHT, aliment moyen - - 0 - - - CIQ#ebdfafe0fce6b513193ae9c0855b4094
Lait à 1 - lait - 2% de matière grasse, UHT, enrichi en plusieurs vitamines - - 0 - - - CIQ#825f8bcb068ecde315938147ed819623
Lait Entier - lait entier - - - 1435 - - - KCA#c131edf4d3c1e17da0b0a54b5ed8bbb6
Lait Écrémé - lait ecreme - UHT - - 9353 - - - CIQ#27de8d007093ae392f4b782851e7fd9c
Lait Entier - lait entier - UHT - - 0 - - - CIQ#5118aac9b89cceae9a62423175de70eb
Lait Écrémé - lait ecreme - pasteurisé - - 0 - - - CIQ#1622e54576ffea9bca81697cacb48d94
Lait Entier - lait entier - pasteurisé - - 0 - - - CIQ#d5881852b522b09ee02aa0fe46885b00
Lait de Soja - lait de soja - - - 3001 - - - KCA#7484ab8a01f886bca7607cf06a579a2c
Lait d'Avoine - lait avoine - - - 837 - - - KCA#54605e0becbb04ace3db6bf78748c15f
Lait de Poule - lait de poule - sans alcool - - 0 - - - CIQ#f6756ecdc46ec65e5972c6aaf481f4a2
Lait en Poudre - lait en poudre - écrémé - - 117 - - - CIQ#1d9ba583216533c41321ffd9ea51b327
Lait en Poudre - lait en poudre - entier - - 25 - - - CIQ#be7d16f0a05422e5eb1d5ff077dee20c
Lait de Brebis - lait de brebi - entier - - 0 - - - CIQ#b54f3b8a48f8d3e0ba7a0228c8adca4f
Lait de Jument - lait de jument - entier - - 0 - - - CIQ#05ea74b811b1a15ad91876c22391f13a
Lait en Poudre - lait en poudre - demi-écrémé - - 0 - - - CIQ#ee03115de1c18f635dbb62d80d6f9715
Lait de Chèvre - lait de chevre - entier, cru - - 0 - - - CIQ#8fb6afe4302a0073de91d274e3722c3e
Lait de Chèvre - lait de chevre - entier, UHT - - 0 - - - CIQ#9d462cfc80afac9cf259f0f2f305db74
Lait de Chèvre - lait de chevre - demi-écrémé, UHT - - 0 - - - CIQ#a497c21ecfbd7c2930cb99326897a779
Lait 1/2 Écrémé - lait 1/2 ecreme - - - 23220 - - - KCA#d5b12fbedab6d0f0a741feeaa8e92b35
Lait Entier UHT - lait entier uht - - - 25 - - - KCA#aeb66cc691b5e08f15b01dc094a51d18
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai bu un grand verre de lait", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Lait', 'normName': ' lait ', 'comment': 'teneur en matière grasse inconnue, UHT, aliment moyen', 'normComment': ' teneur en matiere grasse inconnue uht aliment moyen ', 'rank': 0, 'id': 'CIQ#ebdfafe0fce6b513193ae9c0855b4094', 'quantity': 'un grand verre', 'quantityLem': '1 grand verre', 'pack': ['VX1', 'VA2', 'VA3', 'BI4', 'VA4'], 'type': 'boisson', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': 'VX1-100', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 7.960331201553345}
----------------------------------------------------------------------------------
LLM CPU Time: 7.960331201553345