Input path: /home/debian/html/nutritwin/output_llm/6612c81720b0f/input.json
Output path: /home/debian/html/nutritwin/output_llm/6612c81720b0f/output.json
Input text: 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: Beurre
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Beurre###. 
 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: "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. 
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, I've analyzed the sentence "Beurre". However, the sentence is quite simple and does not provide much context. Here's the information I could extract:

```json
[
  {
    "name": "Beurre",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "Laitier",
    "time": "unknown",
    "event": "unknown"
  }
]
```

The word "Beurre" translates to "Butter" in English. It's a dairy product, hence the type "Laitier". The quantity, cooking method, brand, company, time of consumption, and event (declaration or intent) are not specified in the sentence. Therefore, these fields are left blank or set to "unknown".
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, I've analyzed the sentence "Beurre". However, the sentence is quite simple and does not provide much context. Here's the information I could extract:

```json
[
  {
    "name": "Beurre",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "Laitier",
    "time": "unknown",
    "event": "unknown"
  }
]
```

The word "Beurre" translates to "Butter" in English. It's a dairy product, hence the type "Laitier". The quantity, cooking method, brand, company, time of consumption, and event (declaration or intent) are not specified in the sentence. Therefore, these fields are left blank or set to "unknown".
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "Beurre",    "quantity": "",    "cooking": "",    "brand": "",    "company": "",    "type": "Laitier",    "time": "unknown",    "event": "unknown"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Beurre', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'Laitier', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.052919999999999995}
--------------------------------------------------------------------------------

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

--------------------------------- final result -----------------------------------
{'prompt': 'Beurre', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Beurre Doux', 'normName': ' beurre dou ', 'comment': '', 'normComment': '', 'rank': 78338, 'id': 'KCA#70e9c7551c333018468c7d83aa296ffb', 'quantity': '', 'quantityLem': '', 'pack': ['NOI.w10', 'BEU.w15'], 'type': 'Laitier', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 6.20914363861084}
----------------------------------------------------------------------------------
LLM CPU Time: 6.20914363861084