Input path: /home/debian/html/nutritwin/output_llm/661018dc6671e/input.json
Output path: /home/debian/html/nutritwin/output_llm/661018dc6671e/output.json
Input text: Salade de frais
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: Salade de frais
==================================================================================================================================

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

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, I have analyzed the sentence "Salade de frais". Here is the information extracted:

[
  {
    "name": "Salade de fraises",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "food",
    "time": "unknown",
    "event": "unknown"
  }
]

Explanation:
- The name of the food is "Salade de fraises" (Strawberry salad).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand is not specified and "Salade de fraises" is not a well-known branded product, so the brand is set to "".
- The company is not specified in the sentence.
- The type of food is "food".
- The time of day when the food is eaten is not specified in the sentence, so it is set to "unknown".
- The event (intent, declaration, unknown) is not specified in the sentence, so it is set to "unknown".
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, I have analyzed the sentence "Salade de frais". Here is the information extracted:

[
  {
    "name": "Salade de fraises",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "food",
    "time": "unknown",
    "event": "unknown"
  }
]

Explanation:
- The name of the food is "Salade de fraises" (Strawberry salad).
- The quantity is not specified in the sentence.
- The cooking mode is not specified in the sentence.
- The brand is not specified and "Salade de fraises" is not a well-known branded product, so the brand is set to "".
- The company is not specified in the sentence.
- The type of food is "food".
- The time of day when the food is eaten is not specified in the sentence, so it is set to "unknown".
- The event (intent, declaration, unknown) is not specified in the sentence, so it is set to "unknown".
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "Salade de fraises",    "quantity": "",    "cooking": "",    "brand": "",    "company": "",    "type": "food",    "time": "unknown",    "event": "unknown"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Salade de fraises', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'food', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.059759999999999994}
--------------------------------------------------------------------------------

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 '% salade de fraise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


Second 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_NormAggr LIKE '% salade de fraise %' AND V_NormTrademark LIKE '%%' 

-------------------------------------------
------ERROR--------------------------------
No solution for query: 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_NormAggr LIKE '% salade de fraise %' AND V_NormTrademark LIKE '%%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Salade de frais', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 7.378093957901001}
----------------------------------------------------------------------------------
LLM CPU Time: 7.378093957901001