Input path: /home/debian/html/nutritwin/output_llm/66267933b700d/input.json
Output path: /home/debian/html/nutritwin/output_llm/66267933b700d/output.json
Input text: j'ai bu un verre de coca
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: j'ai bu un verre de coca
==================================================================================================================================
==================================== 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: ###j'ai bu un verre de coca###.
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: "j'ai bu un verre de coca".
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 "j'ai bu un verre de coca".
The food item is a beverage, specifically "Coca". The quantity is "un verre" (one glass). The brand is "Coca-Cola", as "Coca" is commonly understood to refer to this well-known brand. The event is a "declaration" as the consumption is in the past. The time of day is not specified in the sentence.
Here's the information formatted in JSON:
```json
[
{
"name": "Coca",
"quantity": "un verre",
"cooking": "",
"brand": "Coca-Cola",
"company": "",
"type": "boisson",
"time": "",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "j'ai bu un verre de coca".
The food item is a beverage, specifically "Coca". The quantity is "un verre" (one glass). The brand is "Coca-Cola", as "Coca" is commonly understood to refer to this well-known brand. The event is a "declaration" as the consumption is in the past. The time of day is not specified in the sentence.
Here's the information formatted in JSON:
```json
[
{
"name": "Coca",
"quantity": "un verre",
"cooking": "",
"brand": "Coca-Cola",
"company": "",
"type": "boisson",
"time": "",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Coca", "quantity": "un verre", "cooking": "", "brand": "Coca-Cola", "company": "", "type": "boisson", "time": "", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Coca', 'quantity': 'un verre', 'cooking': '', 'brand': 'Coca-Cola', 'company': '', 'type': 'boisson', 'time': '', 'event': 'declaration'}], 'cost': 0.05585999999999999}
--------------------------------------------------------------------------------
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 '% coca %' AND V_NormTrademark LIKE '%coca cola%'
------------- Found solution (max 20) --------------
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 0049000004632 - 0049000004632 - OFF#40538ecf4c32705b3b695d7a2d2b3611
Coca Zero - coca zero - - The Coca-Cola Company - 0 - 3348630001101 - 3348630001101 - OFF#440095f3b491e7e31cd0d061fc51039a
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5449000028921 - 0049000004632 - OFF#20f61f62bf57f7016ce7534ad4a33ade
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5449000155986 - 0049000004632 - OFF#9ebd6cc3a118cd0be4c1bdf4c247d6cf
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112634716 - 0049000004632 - OFF#88d61dff0933f8e590144113cc5fbe1d
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112634655 - 0049000004632 - OFF#3406355b3bacc2c71d87ae5715b5090a
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112632767 - 0049000004632 - OFF#07b9b58aabe14458999e1bfb98e87471
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112631104 - 0049000004632 - OFF#30539faf7d5765f120a708d779bbff5c
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112631029 - 0049000004632 - OFF#9b12e89e2a7c51a6d2314130eede254c
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112630794 - 0049000004632 - OFF#c802b479d07373b20244ae19f9866d1b
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112624663 - 0049000004632 - OFF#781452f961918695196b160a130f121e
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112624656 - 0049000004632 - OFF#1aa11fccd73b2506297d35c05e25668c
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112620047 - 0049000004632 - OFF#5fa47b84809957c26b74ef2cf2ca3881
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112619997 - 0049000004632 - OFF#9c8fd611edcd6f445818d612e940ba65
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112619959 - 0049000004632 - OFF#044755b9424ce5ab97492747546a7707
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112634723 - 0049000004632 - OFF#4d069c23c7aaf392d5331bb6df53c608
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112619386 - 0049000004632 - OFF#9a6fb1fc814d8435a8012f445c5422a3
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112611878 - 0049000004632 - OFF#6cf84d5d85e9ef0f5c5592e371cf72a8
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112611748 - 0049000004632 - OFF#3da24a84935e71bf071d57cfe1811b6d
Coca Cola - coca cola - - The Coca-Cola Company - 0 - 5000112609691 - 0049000004632 - OFF#f8f46179d904948cee801fc72dce6f34
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "j'ai bu un verre de coca", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Coca Cola', 'normName': ' coca cola ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#40538ecf4c32705b3b695d7a2d2b3611', 'quantity': 'un verre', 'quantityLem': '1 verre', 'pack': ['C50', 'C3B', 'C33', 'VX1', 'BI4', 'VA2', 'VA3', 'GOB'], 'type': 'boisson', 'gtin': '0049000004632', 'gtinRef': '0049000004632', 'brand': 'The Coca-Cola Company', 'time': '', 'event': 'declaration', 'serving': 'BI4-100', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 11.667573928833008}
----------------------------------------------------------------------------------
LLM CPU Time: 11.667573928833008