Input path: /home/debian/html/nutritwin/output_llm/6610186fd2415/input.json
Output path: /home/debian/html/nutritwin/output_llm/6610186fd2415/output.json
Input text: Escalopes de veau à la crème
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: Escalopes de veau à la crème
==================================================================================================================================

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

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 "Escalopes de veau à la crème".

The sentence does not provide information about the quantity, brand, company, time of eating, or whether it's an intent, declaration, or unknown event. However, we can identify the name, type, and cooking mode.

The name of the dish is "Escalopes de veau à la crème". The type of food is "viande" (meat) as "veau" (veal) is a type of meat. The cooking mode is "à la crème", which suggests the veal escalopes are cooked with cream.

Here's the formatted result:

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

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

The sentence does not provide information about the quantity, brand, company, time of eating, or whether it's an intent, declaration, or unknown event. However, we can identify the name, type, and cooking mode.

The name of the dish is "Escalopes de veau à la crème". The type of food is "viande" (meat) as "veau" (veal) is a type of meat. The cooking mode is "à la crème", which suggests the veal escalopes are cooked with cream.

Here's the formatted result:

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

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

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Escalopes de veau à la crème', 'quantity': '', 'cooking': 'à la crème', 'brand': '', 'company': '', 'type': 'viande', 'time': '', 'event': 'unknown'}], 'cost': 0.05861999999999999}
--------------------------------------------------------------------------------

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 creme %' 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 creme %' 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 creme %' AND V_NormTrademark LIKE '%%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Escalopes de veau à la crème', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 6.091075658798218}
----------------------------------------------------------------------------------
LLM CPU Time: 6.091075658798218