Input path: /home/debian/html/nutritwin/output_llm/66294be700b94/input.json
Output path: /home/debian/html/nutritwin/output_llm/66294be700b94/output.json
Input text: Ce soir j'ai mangé un hamburger
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 soir j'ai mangé un hamburger
==================================================================================================================================

==================================== 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: ###Ce soir j'ai mangé un hamburger###. 
 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 soir j'ai mangé un hamburger".

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 "Ce soir j'ai mangé un hamburger".

The food item mentioned is "hamburger". There's no specific brand, cooking mode, or company name mentioned. The quantity is "un" (one), and the type is "fast food". The time of eating is "dîner" (dinner) as "Ce soir" means "this evening". The event is a "declaration" as the eating is in the past.

Here's the formatted result in French:

```json
[
  {
    "name": "hamburger",
    "quantity": "un",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "fast food",
    "time": "dîner",
    "event": "declaration"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce soir j'ai mangé un hamburger".

The food item mentioned is "hamburger". There's no specific brand, cooking mode, or company name mentioned. The quantity is "un" (one), and the type is "fast food". The time of eating is "dîner" (dinner) as "Ce soir" means "this evening". The event is a "declaration" as the eating is in the past.

Here's the formatted result in French:

```json
[
  {
    "name": "hamburger",
    "quantity": "un",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "fast food",
    "time": "dîner",
    "event": "declaration"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "hamburger",    "quantity": "un",    "cooking": "",    "brand": "",    "company": "",    "type": "fast food",    "time": "dîner",    "event": "declaration"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'hamburger', 'quantity': 'un', 'cooking': '', 'brand': '', 'company': '', 'type': 'fast food', 'time': 'dîner', 'event': 'declaration'}], 'cost': 0.05724}
--------------------------------------------------------------------------------

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


------------- Found solution (max 20) --------------
Hamburger -  hamburger  - provenant de fast food -  - 0 -  -  - CIQ#c10a5ce03cc9db08facab760bd900ddd
Hamburger au Bleu -  hamburger bleu  -  -  - 235 -  -  - KCA#b588d98a9225d4f0c4c4edbd4f6dfb60
Hamburger au Fromage -  hamburger fromage  -  -  - 486 -  -  - KCA#c23ba0be0ce05ecf80edcc7c9c3c6652
Pain pour Hamburger -  pain pour hamburger  -  -  - 386 -  -  - KCA#54ed6c7f151f2acb0f95aefbae574187
----------------------------------------------------


--------------------------------- final result -----------------------------------
{'prompt': "Ce soir j'ai mangé un hamburger", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Hamburger', 'normName': ' hamburger ', 'comment': 'provenant de fast food', 'normComment': ' provenant de fast food ', 'rank': 0, 'id': 'CIQ#c10a5ce03cc9db08facab760bd900ddd', 'quantity': 'un', 'quantityLem': '1', 'pack': ['HAM.w250'], 'type': 'fast food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'dîner', 'event': 'declaration', 'serving': 'HAM-100', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 12.13750672340393}
----------------------------------------------------------------------------------
LLM CPU Time: 12.13750672340393