Input path: /home/debian/html/nutritwin/output_llm/660edca650799/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edca650799/output.json
Input text: Une tranche de pain
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: Une tranche de pain
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Une tranche de pain###.
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: "Une tranche de pain".
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 "Une tranche de pain".
The sentence mentions a food item, "pain" (bread), with a quantity of "une tranche" (one slice). There's no information about the brand, the company, the cooking mode, or the time of day when the bread is eaten. The event is unknown because the sentence does not indicate whether the eating of the bread is in the past, future, or present.
Here's the result formatted in JSON:
```json
[
{
"name": "pain",
"quantity": "une tranche",
"cooking": "",
"brand": "",
"company": "",
"type": "nourriture",
"time": "",
"event": "inconnu"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Une tranche de pain".
The sentence mentions a food item, "pain" (bread), with a quantity of "une tranche" (one slice). There's no information about the brand, the company, the cooking mode, or the time of day when the bread is eaten. The event is unknown because the sentence does not indicate whether the eating of the bread is in the past, future, or present.
Here's the result formatted in JSON:
```json
[
{
"name": "pain",
"quantity": "une tranche",
"cooking": "",
"brand": "",
"company": "",
"type": "nourriture",
"time": "",
"event": "inconnu"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pain", "quantity": "une tranche", "cooking": "", "brand": "", "company": "", "type": "nourriture", "time": "", "event": "inconnu" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pain', 'quantity': 'une tranche', 'cooking': '', 'brand': '', 'company': '', 'type': 'nourriture', 'time': '', 'event': 'inconnu'}], 'cost': 0.053399999999999996}
--------------------------------------------------------------------------------
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 '% pain %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pain - pain - - - 261532 - - - CIQ#78316c0b820d8f80c640c9d0bc741c50
Pain - pain - sans gluten - - 29 - - - CIQ#9d6a800b4a9dbe9504fb68b26057ad7b
Pain - pain - baguette, courante - - 0 - - - CIQ#c92016dc98d790db0bc7c949d601f5c2
Pain - pain - baguette ou boule, au levain - - 0 - - - CIQ#4b65f0348cbdd1f29daadea789369616
Pain - pain - baguette ou boule, de campagne - - 0 - - - CIQ#665da1982ec8e7e74501d57dc7e111b8
Pain - pain - baguette, de tradition française - - 0 - - - CIQ#e5e8a2a86b1a95d66e26a64c18c0b520
Pain - pain - baguette ou boule, bis, à la farine T80 ou T110 - - 0 - - - CIQ#233b9a74f0cc423be7b3fe6fa040567b
Pain - pain - baguette ou boule, bio, à la farine T55 jusqu'à T110 - - 0 - - - CIQ#91fae3ae1c9b87dd0039d7caa03a7d72
Pain - pain - baguette ou boule, aux céréales et graines, artisanal - - 0 - - - CIQ#5fed24621fe6dde995398f020bf84d7d
Pain Bis - pain bi - - - 77 - - - KCA#0d04d397f5620b8618c8972be2ce29a7
Pain Pita - pain pita - - - 951 - - - KCA#0a6b29619370c1e5c09e5ec16992feed
Pain Azyme - pain azyme - - - 1038 - - - KCA#90d292248257ebd4aba91b7e0f6f67d7
Pain Perdu - pain perdu - - - 783 - - - CIQ#67427fe34e70bfc99fd131b16908c1ee
Pain de Son - pain de son - - - 302 - - - KCA#3ccdb3c87985b4f83e1354ee3a2cebfd
Pain au Son - pain son - - - 0 - - - CIQ#825cc00fe7ac81ed34e142fde0f6ddf4
Pain de Mie - pain de mie - au son - - 0 - - - CIQ#1f8d06921f1e892824b0f8cef870e840
Pain de Mie - pain de mie - complet - - 7211 - - - CIQ#d93405497d2314d29dbd770c5b956eeb
Pain de Mie - pain de mie - courant - - 0 - - - CIQ#667832b5357e637fdb28760b7d6c2d8d
Pain Grillé - pain grille - domestique - - 0 - - - CIQ#f4bc68c618fb825e526db4034e88b66a
Pain de Mie - pain de mie - sans croûte - - 32 - - - CIQ#be3f663945b51703d39413cadc3becab
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Une tranche de pain', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Pain', 'normName': ' pain ', 'comment': '', 'normComment': '', 'rank': 261532, 'id': 'CIQ#78316c0b820d8f80c640c9d0bc741c50', 'quantity': 'une tranche', 'quantityLem': '1 tranche', 'pack': ['PAI.w60', 'BAG.w60', 'TPA.w30'], 'type': 'nourriture', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'inconnu', 'serving': 'TPA-100', 'posiNormName': 0}], 'activity': []}, 'cputime': 5.302788734436035}
----------------------------------------------------------------------------------
LLM CPU Time: 5.302788734436035