Input path: /home/debian/html/nutritwin/output_llm/66119181cdcf7/input.json
Output path: /home/debian/html/nutritwin/output_llm/66119181cdcf7/output.json
Input text: Fèves
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: Fèves
==================================================================================================================================

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

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 the instructions provided, I will analyze the sentence "Fèves".

Since "Fèves" is a single word and does not provide much context, I will make some assumptions. "Fèves" is the French word for "broad beans" or "fava beans". Since there is no additional information about the quantity, brand, company, cooking mode, or time of consumption, I will leave those fields blank. The type of food is a vegetable, and the event is unknown since there is no indication of past or future consumption.

Here is the JSON formatted result:

\[
{"name": "Fèves",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume",
"time": "",
"event": "inconnu"}
\]
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on the instructions provided, I will analyze the sentence "Fèves".

Since "Fèves" is a single word and does not provide much context, I will make some assumptions. "Fèves" is the French word for "broad beans" or "fava beans". Since there is no additional information about the quantity, brand, company, cooking mode, or time of consumption, I will leave those fields blank. The type of food is a vegetable, and the event is unknown since there is no indication of past or future consumption.

Here is the JSON formatted result:

\[
{"name": "Fèves",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume",
"time": "",
"event": "inconnu"}
\]
------------------------------------------------------

------------------------ After simplification ------------------------
[{"name": "Fèves","quantity": "","cooking": "","brand": "","company": "","type": "légume","time": "","event": "inconnu"}\]
----------------------------------------------------------------------

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
ERROR: impossible to parse [II]:
Based on the instructions provided, I will analyze the sentence "Fèves".

Since "Fèves" is a single word and does not provide much context, I will make some assumptions. "Fèves" is the French word for "broad beans" or "fava beans". Since there is no additional information about the quantity, brand, company, cooking mode, or time of consumption, I will leave those fields blank. The type of food is a vegetable, and the event is unknown since there is no indication of past or future consumption.

Here is the JSON formatted result:

\[
{"name": "Fèves",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume",
"time": "",
"event": "inconnu"}
\]

The extracted string is [{"name": "Fèves","quantity": "","cooking": "","brand": "","company": "","type": "légume","time": "","event": "inconnu"}\]
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

--------------------------------- LLM result -----------------------------------
{'response': {}, 'cost': 0.05148}
--------------------------------------------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Fèves', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 4.9149558544158936}
----------------------------------------------------------------------------------
LLM CPU Time: 4.9149558544158936