Input path: /home/debian/html/nutritwin/output_llm/662a19ef239de/input.json
Output path: /home/debian/html/nutritwin/output_llm/662a19ef239de/output.json
Input text: Et trois petits 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: Et trois petits beurre
==================================================================================================================================

==================================== 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: ###Et trois petits 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: "Et trois petits 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. 
The level of cooking mode is not in the name.
There is no quantity in the name, ex: the name for "une pomme" is "pomme".
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 "Et trois petits beurre".

The sentence mentions a food item "petits beurre", which is a type of biscuit. The quantity is "trois" (three). There's no information about the brand, company, cooking mode, or the time of day when it was consumed. The event is unknown since the sentence does not indicate whether the consumption is in the past, future, or present.

Here's the formatted result in French:

```json
[
  {
    "name": "petits beurre",
    "quantity": "trois",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "biscuit",
    "time": "",
    "event": "inconnu"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Et trois petits beurre".

The sentence mentions a food item "petits beurre", which is a type of biscuit. The quantity is "trois" (three). There's no information about the brand, company, cooking mode, or the time of day when it was consumed. The event is unknown since the sentence does not indicate whether the consumption is in the past, future, or present.

Here's the formatted result in French:

```json
[
  {
    "name": "petits beurre",
    "quantity": "trois",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "biscuit",
    "time": "",
    "event": "inconnu"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "petits beurre",    "quantity": "trois",    "cooking": "",    "brand": "",    "company": "",    "type": "biscuit",    "time": "",    "event": "inconnu"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'petits beurre', 'quantity': 'trois', 'cooking': '', 'brand': '', 'company': '', 'type': 'biscuit', 'time': '', 'event': 'inconnu'}], 'cost': 0.055619999999999996}
--------------------------------------------------------------------------------

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 '% petit beurre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Petit Beurre Industriel -  petit beurre industriel  -  -  - 41 -  -  - KCA#99d8a990698f19e6a4feca4736bb7a6c
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': 'Et trois petits beurre', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Petit Beurre Industriel', 'normName': ' petit beurre industriel ', 'comment': '', 'normComment': '', 'rank': 41, 'id': 'KCA#99d8a990698f19e6a4feca4736bb7a6c', 'quantity': 'trois', 'quantityLem': '3', 'pack': ['GA3.w7'], 'type': 'biscuit', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'inconnu', 'serving': 'GA3-300', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 9.088480949401855}
----------------------------------------------------------------------------------
LLM CPU Time: 9.088480949401855