Input path: /home/debian/html/nutritwin/output_llm/661d2302111fa/input.json
Output path: /home/debian/html/nutritwin/output_llm/661d2302111fa/output.json
Input text: J'ai mangé quatre oeufs durs avec de la mayonnaise
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 mangé quatre oeufs durs avec de la mayonnaise
==================================================================================================================================
==================================== 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 mangé quatre oeufs durs avec de la mayonnaise###.
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 mangé quatre oeufs durs avec de la mayonnaise".
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 mangé quatre oeufs durs avec de la mayonnaise".
The sentence indicates that the person ate four hard-boiled eggs with mayonnaise. The food items are "oeufs durs" (hard-boiled eggs) and "mayonnaise". The quantity is specified for "oeufs durs" as "quatre" (four). The cooking mode for "oeufs durs" is "durs" (hard-boiled). There's no brand or company mentioned for any of the food items. The type of food is "oeufs" (eggs) and "mayonnaise". The event is a "declaration" as the eating is in the past. The time of day when the food was eaten is not specified in the sentence.
Here's the result formatted in JSON:
```json
[
{
"name": "oeufs durs",
"quantity": "quatre",
"cooking": "durs",
"brand": "",
"company": "",
"type": "oeufs",
"time": "unknown",
"event": "declaration"
},
{
"name": "mayonnaise",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "mayonnaise",
"time": "unknown",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "J'ai mangé quatre oeufs durs avec de la mayonnaise".
The sentence indicates that the person ate four hard-boiled eggs with mayonnaise. The food items are "oeufs durs" (hard-boiled eggs) and "mayonnaise". The quantity is specified for "oeufs durs" as "quatre" (four). The cooking mode for "oeufs durs" is "durs" (hard-boiled). There's no brand or company mentioned for any of the food items. The type of food is "oeufs" (eggs) and "mayonnaise". The event is a "declaration" as the eating is in the past. The time of day when the food was eaten is not specified in the sentence.
Here's the result formatted in JSON:
```json
[
{
"name": "oeufs durs",
"quantity": "quatre",
"cooking": "durs",
"brand": "",
"company": "",
"type": "oeufs",
"time": "unknown",
"event": "declaration"
},
{
"name": "mayonnaise",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "mayonnaise",
"time": "unknown",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "oeufs durs", "quantity": "quatre", "cooking": "durs", "brand": "", "company": "", "type": "oeufs", "time": "unknown", "event": "declaration" }, { "name": "mayonnaise", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "mayonnaise", "time": "unknown", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'oeufs durs', 'quantity': 'quatre', 'cooking': 'durs', 'brand': '', 'company': '', 'type': 'oeufs', 'time': 'unknown', 'event': 'declaration'}, {'name': 'mayonnaise', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'mayonnaise', 'time': 'unknown', 'event': 'declaration'}], 'cost': 0.07463999999999998}
--------------------------------------------------------------------------------
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 '% oeuf dur %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Oeuf Dur - oeuf dur - - - 34213 - - - KCA#0c9196f2d28e211ac0aeb81d4c9361a9
Oeufs Durs Mayonnaise - oeuf dur mayonnaise - - - 663 - - - KCA#83f10a89cbed801d608ea90ed01c9822
Oeufs Durs Farcis à la Tapenade - oeuf dur farci tapenade - - - 5 - - - KCA#af83d09a7ef0f36516f2c0e50c960f19
----------------------------------------------------
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 '% mayonnaise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Mayonnaise - mayonnaise - - - 10947 - - - KCA#f730f0a51c023e7391c34448b45fae40
Mayonnaise Piquante - mayonnaise piquante - - - 22 - - - KCA#112f3302bf7b965d8aa0a3d19f569e36
Mayonnaise à l'Huile de Tournesol - mayonnaise huile de tournesol - - - 181 - - - KCA#050ac243eafcff755bc4f515e10a1560
Mayonnaise Allégée en Matières Grasses - mayonnaise allegee en matiere grasse - - - 510 - - - KCA#d64a40af3fef944c351452c9849a32cf
Moules à la Mayonnaise - moule mayonnaise - - - 18 - - - KCA#d897514f209bce30c3e937c4d5b460da
Oeufs Durs Mayonnaise - oeuf dur mayonnaise - - - 663 - - - KCA#83f10a89cbed801d608ea90ed01c9822
Crevettes à la Mayonnaise - crevette mayonnaise - - - 53 - - - KCA#008a9811e12a43807907909aad094d60
Langouste Mayonnaise - langouste mayonnaise - - - 7 - - - KCA#e959d71726baa72432e3c3feba5b0975
Langues de Mouton à la Mayonnaise - langue de mouton mayonnaise - - - 1 - - - KCA#14f65e5fe485252b36546e4beef2c460
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': "J'ai mangé quatre oeufs durs avec de la mayonnaise", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Oeuf Dur', 'normName': ' oeuf dur ', 'comment': '', 'normComment': '', 'rank': 34213, 'id': 'KCA#0c9196f2d28e211ac0aeb81d4c9361a9', 'quantity': 'quatre', 'quantityLem': '4', 'pack': ['OEU.w60'], 'type': 'oeufs', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'declaration', 'serving': 'OEU-400', 'posiNormName': 0}, {'name': 'Mayonnaise', 'normName': ' mayonnaise ', 'comment': '', 'normComment': '', 'rank': 10947, 'id': 'KCA#f730f0a51c023e7391c34448b45fae40', 'quantity': '', 'quantityLem': '', 'pack': ['MAY.w15', 'CSS.w15'], 'type': 'mayonnaise', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': ''}, 'cputime': 12.29994249343872}
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LLM CPU Time: 12.29994249343872