Input path: /home/debian/html/nutritwin/output_llm/66161b849a014/input.json
Output path: /home/debian/html/nutritwin/output_llm/66161b849a014/output.json
Input text: Ce matin j'ai bu du champagne dans un verre de dégustation
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 du champagne dans un verre de dégustation
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Ce matin j'ai bu du champagne dans un verre de dégustation###. 
 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 du champagne dans un verre de dégustation".

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 du champagne dans un verre de dégustation".

The food/beverage identified is "champagne". The quantity is not specified in the sentence, but it is implied to be a glass ("un verre"). The brand and company are not mentioned. The type is "alcoholic beverage". The time of day is "petit-déjeuner" (breakfast) as indicated by "Ce matin" (This morning). The event is a "declaration" as the consumption is in the past.

Here's the information formatted in JSON:

```json
[
  {
    "name": "champagne",
    "quantity": "un verre",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson alcoolisée",
    "time": "petit-déjeuner",
    "event": "déclaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai bu du champagne dans un verre de dégustation".

The food/beverage identified is "champagne". The quantity is not specified in the sentence, but it is implied to be a glass ("un verre"). The brand and company are not mentioned. The type is "alcoholic beverage". The time of day is "petit-déjeuner" (breakfast) as indicated by "Ce matin" (This morning). The event is a "declaration" as the consumption is in the past.

Here's the information formatted in JSON:

```json
[
  {
    "name": "champagne",
    "quantity": "un verre",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "boisson alcoolisée",
    "time": "petit-déjeuner",
    "event": "déclaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "champagne",    "quantity": "un verre",    "cooking": "",    "brand": "",    "company": "",    "type": "boisson alcoolisée",    "time": "petit-déjeuner",    "event": "déclaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'champagne', 'quantity': 'un verre', 'cooking': '', 'brand': '', 'company': '', 'type': 'boisson alcoolisée', 'time': 'petit-déjeuner', 'event': 'déclaration'}], 'cost': 0.05993999999999999}
--------------------------------------------------------------------------------

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 '% champagne %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Champagne -  champagne  -  -  - 12344 -  -  - KCA#9594d59f67802537367ff9f03d6efb38
Escargots au Champagne -  escargot champagne  -  -  - 9 -  -  - KCA#3a0938b543fd1edc84436a70228d099a
Bécasses à la Fine Champagne -  becasse fine champagne  -  -  - 2 -  -  - KCA#1f07b7e17ab70fdaceb1874b30d0fa99
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai bu du champagne dans un verre de dégustation", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Champagne', 'normName': ' champagne ', 'comment': '', 'normComment': '', 'rank': 12344, 'id': 'KCA#9594d59f67802537367ff9f03d6efb38', 'quantity': 'un verre', 'quantityLem': '1 verre', 'pack': ['FAC', 'VAD', 'COU'], 'type': 'boisson alcoolisée', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'déclaration', 'serving': 'VAD-100', 'posiNormName': 0}], 'activity': []}, 'cputime': 6.22220516204834}
----------------------------------------------------------------------------------
LLM CPU Time: 6.22220516204834