Input path: /home/debian/html/nutritwin/output_llm/6620c9298f798/input.json
Output path: /home/debian/html/nutritwin/output_llm/6620c9298f798/output.json
Input text: Au petit déjeuner
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: Au petit déjeuner
==================================================================================================================================

==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Au petit déjeuner###. 
 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: "Au petit déjeuner".

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. 
The level of cooking mode is not in the name.
There is no quantity in the name, ex: the name for "une pomme" is "pomme".
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 "Au petit déjeuner".

The sentence only indicates the time of day when food is typically consumed, but it does not provide specific information about the food or beverage itself. Therefore, the details like name, quantity, cooking mode, brand, company, and type cannot be extracted from this sentence.

However, we can identify the time of day and the event. The time of day is "petit-déjeuner" (breakfast) and the event is "unknown" because the sentence does not specify what was or will be eaten.

Here's the result in the requested JSON format:

```json
[
  {
    "name": "",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "",
    "time": "petit-déjeuner",
    "event": "unknown"
  }
]
```
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Au petit déjeuner".

The sentence only indicates the time of day when food is typically consumed, but it does not provide specific information about the food or beverage itself. Therefore, the details like name, quantity, cooking mode, brand, company, and type cannot be extracted from this sentence.

However, we can identify the time of day and the event. The time of day is "petit-déjeuner" (breakfast) and the event is "unknown" because the sentence does not specify what was or will be eaten.

Here's the result in the requested JSON format:

```json
[
  {
    "name": "",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "",
    "time": "petit-déjeuner",
    "event": "unknown"
  }
]
```
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "",    "quantity": "",    "cooking": "",    "brand": "",    "company": "",    "type": "",    "time": "petit-déjeuner",    "event": "unknown"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': '', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': '', 'time': 'petit-déjeuner', 'event': 'unknown'}], 'cost': 0.05796}
--------------------------------------------------------------------------------

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 '%%' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)


------------- Found solution (max 20) --------------
Eau -  eau  -  -  - 10064 -  -  - KCA#08cfe774cbf7476b1e582734c7082ecd
Gin -  gin  -  -  - 305 -  -  - CIQ#94521206bf4c275eb265a86ea6036082
Ipa -  ipa  -  -  - 0 -  -  - KCA#ebd50d5b6b6cb2ec8bfa3e6a8bdeba4d
Ail -  ail  - cru -  - 0 -  -  - CIQ#5b43141446e8d43dd8a8533ef7df0a78
Ris -  ri  - agneau -  - 0 -  -  - CIQ#1c34fab892076c406949cd124c6de767
Mil -  mil  - non salé -  - 0 -  -  - CIQ#423cec13a11d0bc53abfde877718ee1d
Ail -  ail  - rôti/cuit au four -  - 0 -  -  - CIQ#fc72e8a70a284f71c96dc85627daf0c0
Ris -  ri  - veau, braisé ou sauté/poêlé -  - 0 -  -  - CIQ#e3826facfe30a4f4b86d24f31899fe03
Oie -  oie  - viande, rôtie/cuite au four -  - 0 -  -  - CIQ#6e7b58f3808a5c893520bfef929da2e5
Ail -  ail  - sauté/poêlé, sans matière grasse -  - 0 -  -  - CIQ#dfc4bebee78432f8420f5de7a7e744b2
Dés -  de  - allumettes, râpé ou haché de jambon -  - 0 -  -  - CIQ#02dc641768c9e88837b342007b780d77
Oie -  oie  - viande et peau, rôtie/cuite au four -  - 0 -  -  - CIQ#d5e2c38b4d8c1dfeebdea850c2770bd5
Dés -  de  - allumettes, râpé ou haché de jambon de volaille -  - 0 -  -  - CIQ#f292e1e4204c44fc64784fd3360ff924
Riz -  riz  - mélange de variétés, blanc, complet, rouge, sauvage,., cru -  - 0 -  -  - CIQ#a07819c9749e64bb8fdf05c82933f975
Pain -  pain  -  -  - 261532 -  -  - CIQ#78316c0b820d8f80c640c9d0bc741c50
Noix -  noix  -  -  - 9716 -  -  - KCA#c906c6893ddeb4160c6962e435a64070
Feta -  feta  -  -  - 4690 -  -  - KCA#836376d4d946da7f776d2aecb0221de2
Chou -  chou  -  -  - 1970 -  -  - KCA#03da66f29a5aea409ea105a6a4386e78
Rhum -  rhum  -  -  - 1579 -  -  - CIQ#9888ebb293c4f80dfa7fe483c8c794ed
Bleu -  bleu  -  -  - 1163 -  -  - KCA#b198b5549d924f8c47b17936f7f6b0cd
----------------------------------------------------

ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution

--------------------------------- final result -----------------------------------
{'prompt': 'Au petit déjeuner', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Eau', 'normName': ' eau ', 'comment': '', 'normComment': '', 'rank': 10064, 'id': 'KCA#08cfe774cbf7476b1e582734c7082ecd', 'quantity': '', 'quantityLem': '', 'pack': ['VAE', 'VX1', 'VA2', 'GOB', 'VA4', 'VA4', 'VA3'], 'type': '', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 12.85564374923706}
----------------------------------------------------------------------------------
LLM CPU Time: 12.85564374923706