Input path: /home/debian/html/nutritwin/output_llm/660edbcf6f789/input.json
Output path: /home/debian/html/nutritwin/output_llm/660edbcf6f789/output.json
Input text: 8 g de sucre sucre en poudre
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: 8 g de sucre sucre en poudre
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###8 g de sucre sucre en poudre###.
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: "8 g de sucre sucre en poudre".
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 "8 g de sucre sucre en poudre". However, the sentence does not provide enough information to fill all the fields you requested. Here is the information I was able to extract:
```json
[
{
"name": "sucre en poudre",
"quantity": "8 g",
"cooking": "",
"brand": "",
"company": "",
"type": "ingredient",
"time": "",
"event": "unknown"
}
]
```
The sentence does not mention a brand, a company, a cooking method, a time of day, or whether the consumption of the sugar is a declaration or an intent. The type of food is an ingredient, as "sucre en poudre" is typically used in cooking or baking. The quantity is "8 g". The name of the food is "sucre en poudre", which translates to "powdered sugar" in English.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, I have analyzed the sentence "8 g de sucre sucre en poudre". However, the sentence does not provide enough information to fill all the fields you requested. Here is the information I was able to extract:
```json
[
{
"name": "sucre en poudre",
"quantity": "8 g",
"cooking": "",
"brand": "",
"company": "",
"type": "ingredient",
"time": "",
"event": "unknown"
}
]
```
The sentence does not mention a brand, a company, a cooking method, a time of day, or whether the consumption of the sugar is a declaration or an intent. The type of food is an ingredient, as "sucre en poudre" is typically used in cooking or baking. The quantity is "8 g". The name of the food is "sucre en poudre", which translates to "powdered sugar" in English.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "sucre en poudre", "quantity": "8 g", "cooking": "", "brand": "", "company": "", "type": "ingredient", "time": "", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'sucre en poudre', 'quantity': '8 g', 'cooking': '', 'brand': '', 'company': '', 'type': 'ingredient', 'time': '', 'event': 'unknown'}], 'cost': 0.05747999999999999}
--------------------------------------------------------------------------------
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 '% sucre en poudre %' 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 '% sucre en poudre %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Sucre - sucre - en poudre - - 13047 - - - KCA#a61491f490cb44bcf59ef948a0097b8f
Sucre Blanc en Poudre - sucre blanc en poudre - - Auchan - 0 - 3596710432554 - 3596710432554 - OFF#a66086f773d66a884af2ebaaec624ada
Sucre Blanc en Poudre - sucre blanc en poudre - - Auchan - 0 - 3596710473557 - 3596710432554 - OFF#85e4d5021ed121a52c9724ce550f8bcc
Sucre en Poudre - sucre en poudre - - Monoprix - 0 - 3350033528215 - 3350033528215 - OFF#d3709fb8a546e942565f10c190c23798
Sucre en Poudre - sucre en poudre - - Carrefour - 0 - 3560071410964 - 3560071410964 - OFF#c665aef27ec4e398de83fbff2ef7ebee
Auchan Sucre en Poudre Bec Verseur - auchan sucre en poudre bec verseur - - Auchan - 0 - 3596710466597 - 3596710466597 - OFF#fa252dc8c0177a0b4368f8ed6eaabe98
----------------------------------------------------
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': '8 g de sucre sucre en poudre', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Sucre', 'normName': ' sucre ', 'comment': 'en poudre', 'normComment': ' en poudre ', 'rank': 13047, 'id': 'KCA#a61491f490cb44bcf59ef948a0097b8f', 'quantity': '8 g', 'quantityLem': '8 g', 'pack': ['CSS.w5'], 'type': 'ingredient', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': -1}], 'activity': []}, 'cputime': 6.287847518920898}
----------------------------------------------------------------------------------
LLM CPU Time: 6.287847518920898