Input path: /home/debian/html/nutritwin/output_llm/66142a0a1e8e5/input.json
Output path: /home/debian/html/nutritwin/output_llm/66142a0a1e8e5/output.json
Input text: Tarte poire chocolat
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: Tarte poire chocolat
==================================================================================================================================

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

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 how I would analyze the sentence "Tarte poire chocolat":

The sentence mentions a food item, "Tarte poire chocolat", which is a type of tart made with pears and chocolate. However, the sentence does not provide information about the quantity, brand, company, cooking mode, or time of day. Additionally, the sentence does not indicate whether the food consumption is in the past, future, or present, so the event is unknown.

Here's how I would format the result in JSON:

[
{
"name": "Tarte poire chocolat",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "dessert",
"time": "",
"event": "unknown"
}
]

The type of food is classified as a dessert because tarts are typically sweet dishes served at the end of a meal. Since the sentence does not provide any information about the time of day, I left the "time" field blank. Similarly, since the event is unknown, I set the "event" field to "unknown".
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's how I would analyze the sentence "Tarte poire chocolat":

The sentence mentions a food item, "Tarte poire chocolat", which is a type of tart made with pears and chocolate. However, the sentence does not provide information about the quantity, brand, company, cooking mode, or time of day. Additionally, the sentence does not indicate whether the food consumption is in the past, future, or present, so the event is unknown.

Here's how I would format the result in JSON:

[
{
"name": "Tarte poire chocolat",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "dessert",
"time": "",
"event": "unknown"
}
]

The type of food is classified as a dessert because tarts are typically sweet dishes served at the end of a meal. Since the sentence does not provide any information about the time of day, I left the "time" field blank. Similarly, since the event is unknown, I set the "event" field to "unknown".
------------------------------------------------------

------------------------ After simplification ------------------------
[{"name": "Tarte poire chocolat","quantity": "","cooking": "","brand": "","company": "","type": "dessert","time": "","event": "unknown"}]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Tarte poire chocolat', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'dessert', 'time': '', 'event': 'unknown'}], 'cost': 0.06}
--------------------------------------------------------------------------------

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 '% tarte poire chocolat %' 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 '% tarte poire chocolat %' 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 '% tarte poire chocolat %' AND V_NormTrademark LIKE '%%' 
-------------------------------------------
-------------------------------------------

--------------------------------- final result -----------------------------------
{'prompt': 'Tarte poire chocolat', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': []}, 'cputime': 6.5477824211120605}
----------------------------------------------------------------------------------
LLM CPU Time: 6.5477824211120605