Input path: /home/debian/html/nutritwin/output_llm/6610185e2b42b/input.json
Output path: /home/debian/html/nutritwin/output_llm/6610185e2b42b/output.json
Input text: Escalope de veau à la normande
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: Escalope de veau à la normande
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Escalope de veau à la normande###. 
 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: "Escalope de veau à la normande".

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 "Escalope de veau à la normande".

The sentence refers to a dish called "Escalope de veau à la normande", which is a type of food. The name of the dish is "Escalope de veau à la normande". The type of food is "meat", as "veau" means "veal" in English.

The quantity is not specified in the sentence. The cooking mode can be inferred from "à la normande", which is a traditional French cooking method, but the specific details are not provided.

The brand and the company are not mentioned in the sentence. The time of day when the food is eaten is not specified. The event (intent, declaration, unknown) cannot be determined from the sentence as it does not indicate whether the food is being eaten, will be eaten, or has been eaten.

Here's the result formatted in JSON:

```json
[
  {
    "name": "Escalope de veau à la normande",
    "quantity": "",
    "cooking": "à la normande",
    "brand": "",
    "company": "",
    "type": "viande",
    "time": "",
    "event": "inconnu"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Escalope de veau à la normande".

The sentence refers to a dish called "Escalope de veau à la normande", which is a type of food. The name of the dish is "Escalope de veau à la normande". The type of food is "meat", as "veau" means "veal" in English.

The quantity is not specified in the sentence. The cooking mode can be inferred from "à la normande", which is a traditional French cooking method, but the specific details are not provided.

The brand and the company are not mentioned in the sentence. The time of day when the food is eaten is not specified. The event (intent, declaration, unknown) cannot be determined from the sentence as it does not indicate whether the food is being eaten, will be eaten, or has been eaten.

Here's the result formatted in JSON:

```json
[
  {
    "name": "Escalope de veau à la normande",
    "quantity": "",
    "cooking": "à la normande",
    "brand": "",
    "company": "",
    "type": "viande",
    "time": "",
    "event": "inconnu"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "Escalope de veau à la normande",    "quantity": "",    "cooking": "à la normande",    "brand": "",    "company": "",    "type": "viande",    "time": "",    "event": "inconnu"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Escalope de veau à la normande', 'quantity': '', 'cooking': 'à la normande', 'brand': '', 'company': '', 'type': 'viande', 'time': '', 'event': 'inconnu'}], 'cost': 0.06581999999999999}
--------------------------------------------------------------------------------

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 '% escalope de veau normande %' 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 '% escalope de veau normande %' 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 '% escalope de veau normande %' AND V_NormTrademark LIKE '%%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Escalope de veau à la normande', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 8.120289325714111}
----------------------------------------------------------------------------------
LLM CPU Time: 8.120289325714111