Input path: /home/debian/html/nutritwin/output_llm/660c520f39a29/input.json
Output path: /home/debian/html/nutritwin/output_llm/660c520f39a29/output.json
Input text: À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc
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: À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc###.
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: "À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc".
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 "À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc".
```json
[
{
"name": "pain hyper protéiné multi céréales",
"quantity": "quatre tranches",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "jambon blanc",
"quantity": "quatre tranches",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "déjeuner",
"event": "declaration"
}
]
```
In this case, the event is a "declaration" because the sentence is stating a past action. The time of day is "déjeuner" because "à midi" refers to lunchtime in French. The brands and companies are not specified in the sentence, and the foods are not associated with a specific cooking method. The types of food are "food" because they are solid, non-beverage items. The quantities are "quatre tranches" for both items. The names of the foods are "pain hyper protéiné multi céréales" and "jambon blanc".
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc".
```json
[
{
"name": "pain hyper protéiné multi céréales",
"quantity": "quatre tranches",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "jambon blanc",
"quantity": "quatre tranches",
"cooking": "",
"brand": "",
"company": "",
"type": "food",
"time": "déjeuner",
"event": "declaration"
}
]
```
In this case, the event is a "declaration" because the sentence is stating a past action. The time of day is "déjeuner" because "à midi" refers to lunchtime in French. The brands and companies are not specified in the sentence, and the foods are not associated with a specific cooking method. The types of food are "food" because they are solid, non-beverage items. The quantities are "quatre tranches" for both items. The names of the foods are "pain hyper protéiné multi céréales" and "jambon blanc".
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "pain hyper protéiné multi céréales", "quantity": "quatre tranches", "cooking": "", "brand": "", "company": "", "type": "food", "time": "déjeuner", "event": "declaration" }, { "name": "jambon blanc", "quantity": "quatre tranches", "cooking": "", "brand": "", "company": "", "type": "food", "time": "déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'pain hyper protéiné multi céréales', 'quantity': 'quatre tranches', 'cooking': '', 'brand': '', 'company': '', 'type': 'food', 'time': 'déjeuner', 'event': 'declaration'}, {'name': 'jambon blanc', 'quantity': 'quatre tranches', 'cooking': '', 'brand': '', 'company': '', 'type': 'food', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.07301999999999999}
--------------------------------------------------------------------------------
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 '% pain hyper proteine multi cereale %' 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 '% pain hyper proteine multi cereale %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
------ERROR--------------------------------
No solution for query: 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 '% pain hyper proteine multi cereale %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
-------------------------------------------
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 '% jambon blanc %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Jambon Blanc - jambon blanc - - - 41088 - - - KCA#a2c3580fad4917288fe40406fb88cadb
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "À midi j'ai mangé quatre tranches de pain hyper protéiné multi céréales et quatre tranches de jambon blanc", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Jambon Blanc', 'normName': ' jambon blanc ', 'comment': '', 'normComment': '', 'rank': 41088, 'id': 'KCA#a2c3580fad4917288fe40406fb88cadb', 'quantity': 'quatre tranches', 'quantityLem': '4 tranche', 'pack': ['TR3.w50'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': 'TR3-400', 'posiNormName': 0}], 'activity': []}, 'cputime': 9.101646900177002}
----------------------------------------------------------------------------------
LLM CPU Time: 9.101646900177002