Input path: /home/debian/html/nutritwin/output_llm/66083f728e0c9/input.json
Output path: /home/debian/html/nutritwin/output_llm/66083f728e0c9/output.json
Input text: J'ai mangé ce matin une brochette de poulet
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: J'ai mangé ce matin une brochette de poulet
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###J'ai mangé ce matin une brochette de poulet###. 
 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"]}
----------------------------------------------------------------------
=====> KO

==================================== Prompt =============================================
I need to identify food information from sentences.

Analyze the following french sentence: "J'ai mangé ce matin une brochette de poulet".

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 "J'ai mangé ce matin une brochette de poulet".

The sentence indicates that the person ate a chicken skewer in the morning. The food item is "brochette de poulet" (chicken skewer). The quantity is "une" (one). The time of eating is "ce matin" (in the morning). The event is a "declaration" as the eating is in the past.

The type of food is "viande" (meat) as chicken is a type of meat. The brand, cooking mode, and company are not specified in the sentence.

Here's the formatted result in French:

```json
[
  {
    "name": "brochette de poulet",
    "quantity": "une",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "viande",
    "time": "petit-déjeuner",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "J'ai mangé ce matin une brochette de poulet".

The sentence indicates that the person ate a chicken skewer in the morning. The food item is "brochette de poulet" (chicken skewer). The quantity is "une" (one). The time of eating is "ce matin" (in the morning). The event is a "declaration" as the eating is in the past.

The type of food is "viande" (meat) as chicken is a type of meat. The brand, cooking mode, and company are not specified in the sentence.

Here's the formatted result in French:

```json
[
  {
    "name": "brochette de poulet",
    "quantity": "une",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "viande",
    "time": "petit-déjeuner",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "brochette de poulet",    "quantity": "une",    "cooking": "",    "brand": "",    "company": "",    "type": "viande",    "time": "petit-déjeuner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'brochette de poulet', 'quantity': 'une', 'cooking': '', 'brand': '', 'company': '', 'type': 'viande', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.05993999999999999}
--------------------------------------------------------------------------------

First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% brochette de poulet %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 10) --------------
Brochette de Poulet -  -  - 1781 -  - KCA#9d104200200016e6017329c07f19c4ba
----------------------------------------------------

ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': "J'ai mangé ce matin une brochette de poulet", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Brochette de Poulet', 'normName': ' brochette de poulet ', 'comment': '', 'normComment': '', 'rank': 1781, 'id': 'KCA#9d104200200016e6017329c07f19c4ba', 'quantity': 'une', 'quantityLem': '1', 'pack': ['BRO.w200'], 'type': 'viande', 'gtin': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 6.4367711544036865}
----------------------------------------------------------------------------------
LLM CPU Time: 6.4367711544036865