Input path: /home/debian/html/nutritwin/output_llm/661613d9405ad/input.json
Output path: /home/debian/html/nutritwin/output_llm/661613d9405ad/output.json
Input text: Ce matin j'ai bu une coupe de champagne
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: Ce matin j'ai bu une coupe de champagne
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Ce matin j'ai bu une coupe de champagne###.
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: "Ce matin j'ai bu une coupe de champagne".
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 "Ce matin j'ai bu une coupe de champagne".
The food/beverage identified is "champagne". The type of food is "beverage". The quantity is "une coupe" (a glass). The brand is not specified in the sentence, and since champagne is a well-known product but not a specific brand like "Coca-Cola", the brand will be set to "". The company is also not specified. The time of day when the beverage was consumed is "ce matin" (this morning), which maps to "petit-déjeuner" (breakfast). The event is a "declaration" since the consumption is in the past.
Here's the result formatted in JSON:
```json
[
{
"name": "champagne",
"quantity": "une coupe",
"cooking": "",
"brand": "",
"company": "",
"type": "beverage",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai bu une coupe de champagne".
The food/beverage identified is "champagne". The type of food is "beverage". The quantity is "une coupe" (a glass). The brand is not specified in the sentence, and since champagne is a well-known product but not a specific brand like "Coca-Cola", the brand will be set to "". The company is also not specified. The time of day when the beverage was consumed is "ce matin" (this morning), which maps to "petit-déjeuner" (breakfast). The event is a "declaration" since the consumption is in the past.
Here's the result formatted in JSON:
```json
[
{
"name": "champagne",
"quantity": "une coupe",
"cooking": "",
"brand": "",
"company": "",
"type": "beverage",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "champagne", "quantity": "une coupe", "cooking": "", "brand": "", "company": "", "type": "beverage", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'champagne', 'quantity': 'une coupe', 'cooking': '', 'brand': '', 'company': '', 'type': 'beverage', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.062099999999999995}
--------------------------------------------------------------------------------
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 '% champagne %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Champagne - champagne - - - 12344 - - - KCA#9594d59f67802537367ff9f03d6efb38
Escargots au Champagne - escargot champagne - - - 9 - - - KCA#3a0938b543fd1edc84436a70228d099a
Bécasses à la Fine Champagne - becasse fine champagne - - - 2 - - - KCA#1f07b7e17ab70fdaceb1874b30d0fa99
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai bu une coupe de champagne", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Champagne', 'normName': ' champagne ', 'comment': '', 'normComment': '', 'rank': 12344, 'id': 'KCA#9594d59f67802537367ff9f03d6efb38', 'quantity': 'une coupe', 'quantityLem': '1 coupe', 'pack': ['FAC', 'COU'], 'type': 'beverage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': 'COU-100', 'posiNormName': 0}], 'activity': []}, 'cputime': 6.965898752212524}
----------------------------------------------------------------------------------
LLM CPU Time: 6.965898752212524