Input path: /home/debian/html/nutritwin/output_llm/660edc95e3eed/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edc95e3eed/output.json
Input text: Un morceau de fromage
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: Un morceau de fromage
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Un morceau de fromage###.
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: "Un morceau de fromage".
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 "Un morceau de fromage".
The sentence mentions a food item, "fromage" (cheese), with a quantity "un morceau" (a piece). There's no information about the brand, the company, the cooking mode, or the time of day when the cheese is eaten. The event is unknown because the sentence does not indicate whether the eating of the cheese is in the past, future, or present.
Here's the information formatted in JSON:
```json
[
{
"name": "fromage",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "laitier",
"time": "",
"event": "unknown"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's how I would analyze the sentence "Un morceau de fromage".
The sentence mentions a food item, "fromage" (cheese), with a quantity "un morceau" (a piece). There's no information about the brand, the company, the cooking mode, or the time of day when the cheese is eaten. The event is unknown because the sentence does not indicate whether the eating of the cheese is in the past, future, or present.
Here's the information formatted in JSON:
```json
[
{
"name": "fromage",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "laitier",
"time": "",
"event": "unknown"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "fromage", "quantity": "un morceau", "cooking": "", "brand": "", "company": "", "type": "laitier", "time": "", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'fromage', 'quantity': 'un morceau', 'cooking': '', 'brand': '', 'company': '', 'type': 'laitier', 'time': '', 'event': 'unknown'}], 'cost': 0.053759999999999995}
--------------------------------------------------------------------------------
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 '% fromage %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Fromage - fromage - - - 23096 - - - KCA#e2646df35885ba5fc75c406a551c9fbc
Fromage 45% MG - fromage 45% mg - - - 6874 - - - KCA#14ed2b0745972d44df97c5d52a44ac69
Fromage 20% MG - fromage 20% mg - - - 1124 - - - KCA#e32d6c98bf1d5f0a3c853a8f6bb7c3b3
Fromage 70% MG - fromage 70% mg - - - 494 - - - KCA#351b50fec02ae7c43d964985ac9086c6
Fromage de Tête - fromage de tete - - - 258 - - - CIQ#a80997979cdf84066ed5ed98f0291aef
Fromage de Chèvre - fromage de chevre - - - 4537 - - - KCA#87a40b8f006dcb11aafd4e97014ed3f4
Fromage de Brebis - fromage de brebi - pâte pressée - - 0 - - - KCA#58787aec327646598cc7785b49eea77a
Fromage de Brebis - fromage de brebi - pâte molle à croûte fleurie - - 0 - - - KCA#a463c1fc485a4f9d296ce6817ce2c361
Fromage de Chèvre - fromage de chevre - lactique affiné, au lait cru type Crottin - - 250 - - - KCA#2c01ba493c1fac82cabb393f8f3648a7
Fromage Frais 0% MG - fromage frai 0% mg - - - 519 - - - KCA#88f1992eded597fa4d19465f74683774
Fromage Fondu 25% MG - fromage fondu 25% mg - - - 3246 - - - KCA#d149670a9548a1b193a2c41eca41b75f
Fromage Frais 30% MG - fromage frai 30% mg - - - 145 - - - KCA#7925728898a08e85f13745b60bc71320
Fromage Fondu 45% MG - fromage fondu 45% mg - - - 95 - - - KCA#6d8e1e183c61d211654c306cf3835256
Fromage Frais 20% MG - fromage frai 20% mg - - - 81 - - - KCA#0c277d2e26315ef0b610a1ac6f0b2c8f
Fromage Fondu 70% MG - fromage fondu 70% mg - - - 55 - - - KCA#4310db392dfdcff70718326fee922034
Fromage Fondu 65% MG - fromage fondu 65% mg - - - 52 - - - KCA#df8a055eb661bce01be58e63581e3ace
Fromage Blanc Nature - fromage blanc nature - 0% MG - - 24178 - - - CIQ#36c17f9437be97fba469ea7cd5441d75
Fromage Blanc Nature - fromage blanc nature - 3% MG environ - - 10606 - - - CIQ#4a1c07f162d63ff83801c1fb767aafcf
Fromage Blanc Nature - fromage blanc nature - gourmand, 8% MG environ - - 0 - - - CIQ#4ec95c0d5d5444677063a6486af1e1c9
Fromage Fondu aux Noix - fromage fondu au noix - - - 23 - - - KCA#849bff96c14abb755613ff11508fe7c9
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'Un morceau de fromage', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Fromage', 'normName': ' fromage ', 'comment': '', 'normComment': '', 'rank': 23096, 'id': 'KCA#e2646df35885ba5fc75c406a551c9fbc', 'quantity': 'un morceau', 'quantityLem': '1 morceau', 'pack': ['CAM.w20', 'GRU.w20', 'MIM.w20', 'ROC.w20', 'CH2.w20'], 'type': 'laitier', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': 'CAM-100', 'posiNormName': 0}], 'activity': []}, 'cputime': 5.505255937576294}
----------------------------------------------------------------------------------
LLM CPU Time: 5.505255937576294