Input path: /home/debian/html/nutritwin/output_llm/661018980bebe/input.json
Output path: /home/debian/html/nutritwin/output_llm/661018980bebe/output.json
Input text: Crème fraîche
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: Crème fraîche
==================================================================================================================================

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

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 "Crème fraîche". However, the sentence is quite simple and does not provide a lot of context. Here is the information I was able to extract:

```json
[
  {
    "name": "Crème fraîche",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "Dairy product",
    "time": "unknown",
    "event": "unknown"
  }
]
```

In this case, the type of food is a dairy product. The name of the food is "Crème fraîche". The quantity, cooking mode, brand, company, time of consumption, and event (declaration or intent) are not specified in the sentence.
-----------------------------------------------------------------------------

----------------- Make it compliant ------------------
Based on your instructions, I have analyzed the sentence "Crème fraîche". However, the sentence is quite simple and does not provide a lot of context. Here is the information I was able to extract:

```json
[
  {
    "name": "Crème fraîche",
    "quantity": "",
    "cooking": "",
    "brand": "",
    "company": "",
    "type": "Dairy product",
    "time": "unknown",
    "event": "unknown"
  }
]
```

In this case, the type of food is a dairy product. The name of the food is "Crème fraîche". The quantity, cooking mode, brand, company, time of consumption, and event (declaration or intent) are not specified in the sentence.
------------------------------------------------------

------------------------ After simplification ------------------------
[  {    "name": "Crème fraîche",    "quantity": "",    "cooking": "",    "brand": "",    "company": "",    "type": "Dairy product",    "time": "unknown",    "event": "unknown"  }]
----------------------------------------------------------------------

--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Crème fraîche', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'Dairy product', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.05148}
--------------------------------------------------------------------------------

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


------------- Found solution (max 20) --------------
Crème Fraîche Crue -  creme fraiche crue  -  -  - 4124 -  -  - KCA#c1bd7174d9cfe733aef11a5c1c750cfd
Crème Fraîche Légère -  creme fraiche legere  -  -  - 2847 -  -  - KCA#00ee81ddac4e9e1709495cffd6a80e69
Crème Fraîche Épaisse -  creme fraiche epaisse  -  -  - 2380 -  -  - KCA#e95e7170ee1a91ad9675e5d205619112
Crème Fraîche Pasteurisée -  creme fraiche pasteurisee  -  -  - 59 -  -  - KCA#022e9fa3538a3c0e019a200a820a0eae
Crème Fraîche Stérilisée Liquide -  creme fraiche sterilisee liquide  -  -  - 605 -  -  - KCA#befeb4e23c95478ba2fa89d344d4e10b
Crème Fraîche Extra Légère 5 % MG -  creme fraiche extra legere mg  -  -  - 186 -  -  - KCA#a4a35d52f74bfe8d3f5562ba2b3de2ee
Crème Fraîche Légère & Épaisse 15 % MG -  creme fraiche legere epaisse 15 mg  -  -  - 2092 -  -  - KCA#69e6bc10cb8cf8e49afcfcfc6ddb4b3a
----------------------------------------------------

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

--------------------------------- final result -----------------------------------
{'prompt': 'Crème fraîche', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Crème Fraîche Crue', 'normName': ' creme fraiche crue ', 'comment': '', 'normComment': '', 'rank': 4124, 'id': 'KCA#c1bd7174d9cfe733aef11a5c1c750cfd', 'quantity': '', 'quantityLem': '', 'pack': ['CSS.w30'], 'type': 'Dairy product', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 5.230360746383667}
----------------------------------------------------------------------------------
LLM CPU Time: 5.230360746383667