Input path: /home/debian/html/nutritwin/output_llm/66093bc248781/input.json
Output path: /home/debian/html/nutritwin/output_llm/66093bc248781/output.json
Input text: Je vais manger deux portions de vache qui rit
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: Je vais manger deux portions de vache qui rit
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Je vais manger deux portions de vache qui rit###. 
 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: "Je vais manger deux portions de vache qui rit".

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 "Je vais manger deux portions de vache qui rit".

The food product mentioned is "vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" (two portions). The event is an "intent" as the consumption is planned for the future. The time of day is not specified in the sentence.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vache qui rit",
    "quantity": "deux portions",
    "cooking": "",
    "brand": "vache qui rit",
    "company": "Bel Group",
    "type": "fromage",
    "time": "unknown",
    "event": "intent"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Je vais manger deux portions de vache qui rit".

The food product mentioned is "vache qui rit", which is a well-known brand of cheese. The quantity is "deux portions" (two portions). The event is an "intent" as the consumption is planned for the future. The time of day is not specified in the sentence.

Here's the information formatted in JSON:

```json
[
  {
    "name": "vache qui rit",
    "quantity": "deux portions",
    "cooking": "",
    "brand": "vache qui rit",
    "company": "Bel Group",
    "type": "fromage",
    "time": "unknown",
    "event": "intent"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "vache qui rit",    "quantity": "deux portions",    "cooking": "",    "brand": "vache qui rit",    "company": "Bel Group",    "type": "fromage",    "time": "unknown",    "event": "intent"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'vache qui rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'vache qui rit', 'company': 'Bel Group', 'type': 'fromage', 'time': 'unknown', 'event': 'intent'}], 'cost': 0.053099999999999994}
--------------------------------------------------------------------------------

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 '% vache qui rit %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


Second 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_NormAggr LIKE '% vache qui rit %' AND V_NormTrademark LIKE '%%' 


------------- Found solution (max 10) --------------
Vache Qui Rit -  - group Bel - 0 - 3073781149937 - OFF#238401604d1d422256e49b0f1d3275eb
Vache Qui Rit -  - group Bel - 0 - 3073781149586 - OFF#2e3e77bbf5eaf2494f777fd3cd8d4850
Vache Qui Rit -  - group Bel - 0 - 3073781071559 - OFF#d9e86ed900dd50bcf8a531fe23ed3d81
Vache Qui Rit -  - Danone - 0 - 3073781158533 - OFF#bd66328f891f776430f621daeda198ff
La Vache Qui Rit -  - Danone - 0 - 3073781192186 - OFF#ff6846fe40706a9ac66456b7536de7b9
La Vache Qui Rit -  - Danone - 0 - 3073781160420 - OFF#bd3414c2bb0fa45a7826718fe2de4e94
La Vache Qui Rit -  - Danone - 0 - 3073781158847 - OFF#b55cd4e5a220d345d25485e76d0810eb
La Vache Qui Rit -  - Danone - 0 - 3073781158854 - OFF#b49a5811950933218d222b303a3b3b01
La Vache Qui Rit -  - Danone - 0 - 3073780574242 - OFF#d1fefed61a433800b0ecb0aa9493a669
La Vache Qui Rit -  - Danone - 0 - 3073780886840 - OFF#6734da8b6a45c6182103aeb5c21691d4
----------------------------------------------------

ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': 'Je vais manger deux portions de vache qui rit', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Vache Qui Rit', 'normName': ' vache qui rit ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#238401604d1d422256e49b0f1d3275eb', 'quantity': 'deux portions', 'quantityLem': '2 portion', 'pack': ['UN2.w20'], 'type': 'fromage', 'gtin': '3073781149937', 'brand': 'group Bel', 'time': 'unknown', 'event': 'intent', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 6.9071009159088135}
----------------------------------------------------------------------------------
LLM CPU Time: 6.9071009159088135