Input path: /home/debian/html/nutritwin/output_llm/660b0aedb9cba/input.json
Output path: /home/debian/html/nutritwin/output_llm/660b0aedb9cba/output.json
Input text: Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure
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 avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
{
"intents": ["Capture the user food consumption", "Capture the user physical activity"]
}
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
{
"intents": ["Capture the user food consumption", "Capture the user physical activity"]
}
------------------------------------------------------
------------------------ After simplification ------------------------
{"intents": ["Capture the user food consumption", "Capture the user physical activity"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure".
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: "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure".
The food items identified are "baguette" and "vache qui rit".
The JSON formatted result would be:
[
{
"name": "baguette",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "boulangerie",
"time": "petit-déjeuner",
"event": "declaration"
},
{
"name": "vache qui rit",
"quantity": "deux portions",
"cooking": "",
"brand": "La vache qui rit",
"company": "Bel Group",
"type": "fromage",
"time": "petit-déjeuner",
"event": "declaration"
}
]
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence: "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure".
The food items identified are "baguette" and "vache qui rit".
The JSON formatted result would be:
[
{
"name": "baguette",
"quantity": "un morceau",
"cooking": "",
"brand": "",
"company": "",
"type": "boulangerie",
"time": "petit-déjeuner",
"event": "declaration"
},
{
"name": "vache qui rit",
"quantity": "deux portions",
"cooking": "",
"brand": "La vache qui rit",
"company": "Bel Group",
"type": "fromage",
"time": "petit-déjeuner",
"event": "declaration"
}
]
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "baguette", "quantity": "un morceau", "cooking": "", "brand": "", "company": "", "type": "boulangerie", "time": "petit-déjeuner", "event": "declaration" }, { "name": "vache qui rit", "quantity": "deux portions", "cooking": "", "brand": "La vache qui rit", "company": "Bel Group", "type": "fromage", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'baguette', 'quantity': 'un morceau', 'cooking': '', 'brand': '', 'company': '', 'type': 'boulangerie', 'time': 'petit-déjeuner', 'event': 'declaration'}, {'name': 'vache qui rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'La vache qui rit', 'company': 'Bel Group', 'type': 'fromage', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.06437999999999999}
--------------------------------------------------------------------------------
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 '% baguette %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Baguette - baguette - - - 13304 - - - KCA#4c58eb37d72a06c3eb6b4eca05a7eafc
Baguette Beurrée - baguette beurree - - - 6728 - - - KCA#81e38c2ee900b3581038d641b4a91c7f
Baguette Beurre Confiture - baguette beurre confiture - - - 5301 - - - KCA#d399b90a645a52039b2f409debeaa686
Pain, Baguette Courante - pain baguette courante - - - 1505 - - - KCA#b8216428789ad27dd11c8e9619cd3ad8
Pain, Baguette sans Sel - pain baguette san sel - - - 144 - - - KCA#9792a779f917b0a8bf1fc9c776628d0a
Sandwich Baguette - sandwich baguette - - - 0 - - - CIQ#ecdbce2254ce082246ccea95b54322d3
Sandwich Baguette - sandwich baguette - jambon, beurre - - 544 - - - CIQ#bd804df922badefbc8215232b9b741aa
Sandwich Baguette - sandwich baguette - salami, beurre - - 59 - - - CIQ#92b9f1c35fd21237d9716ba633faf6c3
Sandwich Baguette - sandwich baguette - jambon emmental - - 0 - - - CIQ#a3044be4730437e3137525aaa8469e38
Sandwich Baguette - sandwich baguette - pâté, cornichons - - 138 - - - CIQ#dffdf1e5117ae64f00c22627ab3670f2
Sandwich Baguette - sandwich baguette - camembert, beurre - - 23 - - - CIQ#0080a72a2d54a3ea5e04c0c631ac01fd
Sandwich Baguette - sandwich baguette - saucisson, beurre - - 0 - - - CIQ#64a51f36b8fcf7fb6aa69713d78a7477
Sandwich Baguette - sandwich baguette - saumon fumé, beurre - - 191 - - - CIQ#f319acba3059dd568c3ec0b09ffee8cd
Sandwich Baguette - sandwich baguette - thon, maïs, crudités - - 0 - - - CIQ#6bc3fa7c33408c471a521687ea57c022
Sandwich Baguette - sandwich baguette - jambon, emmental, beurre - - 5174 - - - CIQ#d4b56c51ab3ed4856726dc5540397da5
Sandwich Baguette - sandwich baguette - merguez, ketchup moutarde - - 71 - - - CIQ#5f4dda655b3b11f243dc5af84eb97c1f
Sandwich Baguette - sandwich baguette - crudités diverses, mayonnaise - - 17 - - - CIQ#80a019abb0e07979e1fadd760efb9be0
Sandwich Baguette - sandwich baguette - oeuf, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#ce1d206be86434bec3c26d455f9689e4
Sandwich Baguette - sandwich baguette - porc, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#8e8c84ec0b67513a1590bfedccec16eb
Sandwich Baguette - sandwich baguette - thon, crudités, tomate, salade, mayonnaise - - 0 - - - CIQ#2216cbeb818ce287e644567239e12d90
----------------------------------------------------
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 '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%'
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 '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%'
Third 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 '% vache qui rit %' AND V_NormAggr LIKE '% la vache qui rit %' AND V_NormAggr LIKE '% bel group %'
-------------------------------------------
------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 '% vache qui rit %' AND V_NormAggr LIKE '% la vache qui rit %' AND V_NormAggr LIKE '% bel group %'
-------------------------------------------
-------------------------------------------
==================================== Prompt =============================================
I need to identify physical activities information from sentences.
Analyze the following french sentence: "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure".
Identify all sports or physical activities, its current name and its duration if it exists.
If possible, map the name on one of the following activities in french: ["Basket-ball", "Cyclisme", "Boxe", "Danse", "Zumba", "Plong\u00e9e sous-marine", "Escrime", "Football", "Jardinage", "Golf", "Gymnastique", "Judo", "Karat\u00e9", "Corde \u00e0 sauter", "Rameur d'appartement", "Course \u00e0 pied", "Faire l'amour", "Ski de descente", "Natation", "Tennis", "Tapis de course", "A\u00e9robic", "Ski de fond", "danse", "V\u00e9lo d'appartement", "Aquabiking", "V\u00e9lo elliptique", "Exercice physique", "Equitation", "sports", "Escalade", "sports", "Patins en ligne", "sports", "Tennis de table", "sports", "Marche", "Activit\u00e9s nautiques et aquatiques", "Marche avec un chien", "T\u00e2ches m\u00e9nag\u00e8res", "Marche nordique"].
Ignore what it is not mapped to sport or physical activities.
"Ménage" is similar to "Nettoyage".
Cooking or eating are not a sport or a physical activities.
Define "intensity" of the sport. Map for "intensity" on one of these values: ["unknown", "low", "moderate", "high", "intense", "very intense"].
Duration must be a number.
Duration must be in minute.
If a physical activity will occur in the future, the event is an "intent".
If a physical activity has occured in the past, the event is a "declaration".
Map this event of the physical activity on ["intent", "declaration", "unknown"].
Ignore the negative action .
If the duration is unknown, set duration to 0.
Format the result for sport in french in JSON in an array of tuples {"name":, "duration":, "intensity":, "event":}.
When there is no solution, the result is: []
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Based on the provided French sentence, "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure", the physical activity identified is running.
The JSON formatted result would be:
[
{
"name": "Course à pied",
"duration": 30,
"intensity": "moderate",
"event": "declaration"
}
]
Explanation:
- The physical activity "Course à pied" (Running) is mapped from "courir dans le parc" (running in the park).
- The duration is "une demi-heure" (half an hour), which is 30 minutes.
- The intensity is assumed to be "moderate" as the sentence does not provide specific information about the intensity of the running.
- The event is a "declaration" as the running activity has already occurred in the past.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on the provided French sentence, "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure", the physical activity identified is running.
The JSON formatted result would be:
[
{
"name": "Course à pied",
"duration": 30,
"intensity": "moderate",
"event": "declaration"
}
]
Explanation:
- The physical activity "Course à pied" (Running) is mapped from "courir dans le parc" (running in the park).
- The duration is "une demi-heure" (half an hour), which is 30 minutes.
- The intensity is assumed to be "moderate" as the sentence does not provide specific information about the intensity of the running.
- The event is a "declaration" as the running activity has already occurred in the past.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Course à pied", "duration": 30, "intensity": "moderate", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Course à pied', 'duration': 30, 'intensity': 'moderate', 'event': 'declaration'}], 'cost': 0.06738}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin avec un morceau de baguette j'ai mangé deux portions de vache qui rit je suis allé ensuite courir dans le parc d'à côté une demi-heure", 'intents': ['Capture the user food consumption', 'Capture the user physical activity'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Baguette', 'normName': ' baguette ', 'comment': '', 'normComment': '', 'rank': 13304, 'id': 'KCA#4c58eb37d72a06c3eb6b4eca05a7eafc', 'quantity': 'un morceau', 'quantityLem': '1 morceau', 'pack': ['BAG.w25', 'PAI.w30'], 'type': 'boulangerie', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': 'BAG-100', 'posiNormName': 0}], 'activity': [{'trigram': 'RUN', 'duration': 30, 'event': 'declaration', 'level': 'RUN06'}]}, 'cputime': 18.669084072113037}
----------------------------------------------------------------------------------
LLM CPU Time: 18.669084072113037