Input path: /home/debian/html/nutritwin/output_llm/660edb8a9adc1/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edb8a9adc1/output.json
Input text: Une petite cuillère de parmesan
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: Une petite cuillère de parmesan
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Une petite cuillère de parmesan###. 
 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: "Une petite cuillère de parmesan".

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 "Une petite cuillère de parmesan".

The food item mentioned is "parmesan". The quantity is "une petite cuillère" (a small spoon). The type of food is "cheese". The brand, company, cooking mode, and time of day are not specified in the sentence. The event is "unknown" because the sentence does not indicate whether the consumption of parmesan is in the past, future, or present.

Here's the information formatted in JSON:

```json
[
  {
    "name": "parmesan",
    "quantity": "une petite cuillère",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "fromage",
    "time": "",
    "event": "inconnu"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Une petite cuillère de parmesan".

The food item mentioned is "parmesan". The quantity is "une petite cuillère" (a small spoon). The type of food is "cheese". The brand, company, cooking mode, and time of day are not specified in the sentence. The event is "unknown" because the sentence does not indicate whether the consumption of parmesan is in the past, future, or present.

Here's the information formatted in JSON:

```json
[
  {
    "name": "parmesan",
    "quantity": "une petite cuillère",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "fromage",
    "time": "",
    "event": "inconnu"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "parmesan",    "quantity": "une petite cuillère",    "cooking": "",    "brand": "",    "company": "",    "type": "fromage",    "time": "",    "event": "inconnu"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'parmesan', 'quantity': 'une petite cuillère', 'cooking': '', 'brand': '', 'company': '', 'type': 'fromage', 'time': '', 'event': 'inconnu'}], 'cost': 0.054779999999999995}
--------------------------------------------------------------------------------

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


------------- Found solution (max 20) --------------
Parmesan -  parmesan  -  -  - 0 -  -  - KCA#fae0e5fbea5d01848c4a1a151cfeffc3
Soupe au Parmesan -  soupe parmesan  - au parmesan -  - 0 -  -  - KCA#e1ecae3e9155e0db4a179db8f1e6e457
Asperges au Parmesan -  asperge parmesan  -  -  - 67 -  -  - KCA#a127db60c903b37982601b9e36b76706
Torsades au Parmesan -  torsade parmesan  - et aux graines de Pavot -  - 15 -  -  - KCA#3e830ae45227519c360729235abd088a
Croquettes de Pomme de Terre au Parmesan -  croquette de pomme de terre parmesan  -  -  - 16 -  -  - KCA#1a8ca7a877d4a38b488d84f4422b572d
----------------------------------------------------

ERROR: Wrong quantity: '1 petit cuillere'
ERROR: Wrong quantity: '2 petit cuillere'
ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': 'Une petite cuillère de parmesan', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Parmesan', 'normName': ' parmesan ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'KCA#fae0e5fbea5d01848c4a1a151cfeffc3', 'quantity': 'une petite cuillère', 'quantityLem': '1 petit cuillere', 'pack': ['CSS.w20', 'GRU.w20'], 'type': 'fromage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'inconnu', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 7.255524396896362}
----------------------------------------------------------------------------------
LLM CPU Time: 7.255524396896362