Input path: /home/debian/html/nutritwin/output_llm/6623c22a7fb54/input.json
Output path: /home/debian/html/nutritwin/output_llm/6623c22a7fb54/output.json
Input text: Ce midi j'ai mangé une pizza
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 midi j'ai mangé une pizza
==================================================================================================================================
==================================== 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: ###Ce midi j'ai mangé une pizza###.
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 midi j'ai mangé une pizza".
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 "Ce midi j'ai mangé une pizza".
The sentence indicates that the person ate a pizza for lunch. The time of day "Ce midi" corresponds to "déjeuner" (lunch). The event is a "declaration" because the action of eating pizza is in the past.
The food item is "pizza". Since no brand is specified and pizza is not a well-known brand itself, the brand is set to "". The type of food is "plat principal" (main dish) as pizza is typically considered a main dish.
The quantity is "une" (one), but it's not specified for each ingredient as the sentence doesn't provide this information. The cooking mode is not mentioned in the sentence. The company name is not provided and cannot be inferred from the information given.
Here's the result formatted in JSON:
```json
[
{
"name": "pizza",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "plat principal",
"time": "déjeuner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce midi j'ai mangé une pizza".
The sentence indicates that the person ate a pizza for lunch. The time of day "Ce midi" corresponds to "déjeuner" (lunch). The event is a "declaration" because the action of eating pizza is in the past.
The food item is "pizza". Since no brand is specified and pizza is not a well-known brand itself, the brand is set to "". The type of food is "plat principal" (main dish) as pizza is typically considered a main dish.
The quantity is "une" (one), but it's not specified for each ingredient as the sentence doesn't provide this information. The cooking mode is not mentioned in the sentence. The company name is not provided and cannot be inferred from the information given.
Here's the result formatted in JSON:
```json
[
{
"name": "pizza",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "plat principal",
"time": "déjeuner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pizza", "quantity": "une", "cooking": "", "brand": "", "company": "", "type": "plat principal", "time": "déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pizza', 'quantity': 'une', 'cooking': '', 'brand': '', 'company': '', 'type': 'plat principal', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.06666}
--------------------------------------------------------------------------------
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 '% pizza %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Pizza - pizza - - - 10599 - - - CIQ#733e507c20c6036da06902e2929056db
Pizza - pizza - - - 0 - - - KCA#733e507c20c6036da06902e2929056db
Pizza - pizza - sauce garniture pour - - 0 - - - CIQ#a275181c086396e0bec873fcc94008cb
Pizza Kebab - pizza kebab - - - 6 - - - CIQ#6bbe41be8630f033bfe294b94bbf8d0c
Pizza Moyenne - pizza moyenne - - - 38 - - - KCA#9bdfcad1de65c2ebcc7384d0aa3fa55f
Pizza au Thon - pizza thon - - - 16 - - - CIQ#5f444b59309014aeab27095b6eb2d95b
Pizza Fromage - pizza fromage - - - 0 - - - KCA#5175d910a3bb5ffe553ada3ee1d50309
Pizza au Poulet - pizza poulet - - - 0 - - - CIQ#33e0a5ea4366eeb0aad919629cf8f008
Pizza au Saumon - pizza saumon - - - 0 - - - CIQ#531c0deee226a1ed25c6ad7e9344ecef
Pizza 4 Fromages - pizza fromage - - - 2361 - - - CIQ#5175d910a3bb5ffe553ada3ee1d50309
Pizza 'Spéciale' - pizza speciale - - - 146 - - - KCA#a6f6dd5434366be39fec21c560e1457e
Pizza à la Poêle - pizza poele - - - 64 - - - KCA#2cd730363965f0d5363b216aaaa75f26
Pizza Boulangerie - pizza boulangerie - - - 318 - - - KCA#291611656924ce924ca7d5200705c55e
Pizza à la Viande - pizza viande - type bolognaise - - 0 - - - CIQ#b17f77e6924678e84c353cde4ec8bdc4
Pizza aux Lardons - pizza au lardon - oignons et fromage - - 0 - - - CIQ#2ff2fb0af20f513208206f7883b4b537
Pizzas Végétariennes - pizza vegetarienne - - - 566 - - - KCA#9f884aabd1a0211b685859e3d93bb8c8
Pizza Jambon Fromage - pizza jambon fromage - - - 405 - - - CIQ#a5c5fe6f659b72fa37b3770428f770e9
Pizza Jambon Fromage - pizza jambon fromage - - - 0 - - - KCA#a5c5fe6f659b72fa37b3770428f770e9
Pizza Tomate et Fromage - pizza tomate fromage - - - 111 - - - KCA#0962f5517452bf8b32ecb09f5f3166da
Pizza aux Fruits de Mer - pizza au fruit de mer - - - 0 - - - CIQ#d9baa96a97f96e931fba42a44879122e
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce midi j'ai mangé une pizza", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Pizza', 'normName': ' pizza ', 'comment': '', 'normComment': '', 'rank': 10599, 'id': 'CIQ#733e507c20c6036da06902e2929056db', 'quantity': 'une', 'quantityLem': '1', 'pack': ['PIZ.w200.p2'], 'type': 'plat principal', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': 'PIZ-100', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 12.148146867752075}
----------------------------------------------------------------------------------
LLM CPU Time: 12.148146867752075