Input path: /home/debian/html/nutritwin/output_llm/661bfd854dff2/input.json
Output path: /home/debian/html/nutritwin/output_llm/661bfd854dff2/output.json
Input text: Ce matin j'ai mangé une orange
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 mangé une orange
==================================================================================================================================
==================================== 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 matin j'ai mangé une orange###.
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 mangé une orange".
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 matin j'ai mangé une orange".
The food item mentioned is "une orange" which translates to "an orange" in English. The type of food is a fruit. The quantity is "une" which means "one". There is no brand, cooking mode, or company name mentioned in the sentence. The time of day when the food was eaten is "Ce matin" which translates to "This morning", so it's "petit-déjeuner" (breakfast). The event is a "declaration" because the food consumption is in the past.
Here's the result formatted in JSON:
```json
[
{
"name": "orange",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai mangé une orange".
The food item mentioned is "une orange" which translates to "an orange" in English. The type of food is a fruit. The quantity is "une" which means "one". There is no brand, cooking mode, or company name mentioned in the sentence. The time of day when the food was eaten is "Ce matin" which translates to "This morning", so it's "petit-déjeuner" (breakfast). The event is a "declaration" because the food consumption is in the past.
Here's the result formatted in JSON:
```json
[
{
"name": "orange",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "orange", "quantity": "une", "cooking": "", "brand": "", "company": "", "type": "fruit", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'orange', 'quantity': 'une', 'cooking': '', 'brand': '', 'company': '', 'type': 'fruit', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.06054}
--------------------------------------------------------------------------------
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 '% orange %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Orange - orange - pulpe, crue - - 19789 - - - CIQ#a84ec3c1b9bc46c10b639fa15eeef5f4
Orange Givrée - orange givree - - - 31 - - - KCA#78bd77a68826904b6b891043ddcc9d5a
Orange Pressée - orange pressee - - - 3137 - - - KCA#d951c9057cfe647b69b7f30181322ad1
Jus d'Orange - ju orange - - - 52983 - - - KCA#da7a1f81a8cd82dbbbbbedf56a167258
Jus d'Orange - ju orange - pur jus - - 0 - - - CIQ#a4328be11b7e0fb0c4474532724cf38f
Jus d'Orange - ju orange - à base de concentré - - 0 - - - CIQ#72928c242781a6ee15266175037b3fb8
Jus d'Orange Pasteurisé - ju orange pasteurise - - - 44 - - - KCA#8dc9e7ac955777e77122f7bd97350613
Jus d'Orange et Gingembre - ju orange gingembre - - - 31 - - - KCA#ac517779183d5fdeff117cfe8eb4be98
Jus d'Orange, Mangue et Fraise - ju orange mangue fraise - - - 60 - - - KCA#12cc18043b0813e5110bb808101edc8e
Jus Orange Pamplemousse Pressés - ju orange pamplemousse presse - - - 517 - - - KCA#e606e760b12355e0cc070fbf069b4261
Jus d'Orange, Carotte et Céleri - ju orange carotte celeri - - - 117 - - - KCA#ba4cb33c47a671db82eeaad9ddd5c63e
Jus d'Orange, Gingembre et Ananas - ju orange gingembre anana - - - 6 - - - KCA#e2edd8bdeebd69177ece6caee7f071d8
Jus d'Orange, Carotte et Gingembre - ju orange carotte gingembre - - - 73 - - - KCA#0c209cbc5beac761ddcf7ea316e5b29e
Jus d'Orange, Ananas et Glace au Melon - ju orange anana glace melon - - - 21 - - - KCA#3e4e71456576da23059304f3eba50c9c
Gin Orange - gin orange - - - 11 - - - KCA#69422eafcd803a4841e22ba7a24dbeaf
Vodka Orange - vodka orange - - - 303 - - - KCA#afee6734db3389d9a53ba62d8e345e8e
Tarte à l'Orange - tarte orange - à l'orange - - 0 - - - KCA#8cf553da1e0c3135218833739419ea98
Salade d'Oranges - salade orange - - - 71 - - - KCA#a7fe61d6cb0d6c12eba4ca95e0f74781
Dorade à l'Orange - dorade orange - - - 76 - - - KCA#f37ceb94004879aa0221259fea9ea8bd
Canard à l'Orange - canard orange - - - 33 - - - KCA#42651abfdc29355ec0cf7e410b802f1a
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé une orange", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Orange', 'normName': ' orange ', 'comment': 'pulpe, crue', 'normComment': ' pulpe crue ', 'rank': 19789, 'id': 'CIQ#a84ec3c1b9bc46c10b639fa15eeef5f4', 'quantity': 'une', 'quantityLem': '1', 'pack': ['ORA.w200'], 'type': 'fruit', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': 'ORA-100', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 9.014482021331787}
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LLM CPU Time: 9.014482021331787