Input path: /home/debian/html/nutritwin/output_llm/661018eb38efa/input.json
Output path: /home/debian/html/nutritwin/output_llm/661018eb38efa/output.json
Input text: Fraises au sucre
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: Fraises au sucre
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Fraises au sucre###.
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: "Fraises au sucre".
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 "Fraises au sucre".
The sentence mentions a food item, "Fraises" (Strawberries) and an ingredient, "sucre" (sugar). However, the quantity, brand, company, cooking mode, and time of consumption are not specified. Also, the sentence does not indicate whether the consumption is a past event, a future intent, or unknown.
Here's the formatted result in French:
```json
[
{
"name": "Fraises",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "",
"event": "unknown"
},
{
"name": "sucre",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "ingredient",
"time": "",
"event": "unknown"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Fraises au sucre".
The sentence mentions a food item, "Fraises" (Strawberries) and an ingredient, "sucre" (sugar). However, the quantity, brand, company, cooking mode, and time of consumption are not specified. Also, the sentence does not indicate whether the consumption is a past event, a future intent, or unknown.
Here's the formatted result in French:
```json
[
{
"name": "Fraises",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "",
"event": "unknown"
},
{
"name": "sucre",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "ingredient",
"time": "",
"event": "unknown"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Fraises", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "fruit", "time": "", "event": "unknown" }, { "name": "sucre", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "ingredient", "time": "", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Fraises', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'fruit', 'time': '', 'event': 'unknown'}, {'name': 'sucre', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'ingredient', 'time': '', 'event': 'unknown'}], 'cost': 0.059579999999999994}
--------------------------------------------------------------------------------
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 '% fraise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Fraise - fraise - crue - - 0 - - - CIQ#a1e7daabf0eef222eb55cee2d9464244
Fraises - fraise - - - 17048 - - - KCA#985bbbfd3e0790251a131d2472a2a13a
Fraises au Citron - fraise citron - - - 776 - - - KCA#edebc181ffc1519049f190184ad0fe5b
Fraise au Bordeaux - fraise bordeau - - - 71 - - - KCA#f4486f0c128d82d8130e08a45c8139dc
Fraises à la Chantilly - fraise chantilly - - - 368 - - - KCA#c87d0d789e71fbc647a3c82f331cc574
Jus de Fraise - ju de fraise - - - 261 - - - KCA#4fdf0443bdabb9f07e0c95aee4fadd2c
Glace à la Fraise - glace fraise - - - 343 - - - KCA#9aa783cbd5442c9a3a7dbac2061e61b1
Tarte aux Fraises - tarte au fraise - aux fraises - - 0 - - - KCA#9334fede766e490d6e36f4982732d2c8
Smoothie Fraise, Miel et Lait de Soja - smoothie fraise miel lait de soja - de soja - - 0 - - - KCA#0df871f30036f11a511b24cd8865c9d1
Gaufre aux Fraises - gaufre au fraise - - - 65 - - - KCA#8de4904b4103fe53986c7f4c61264b68
Bruschette à la Fraise, à la Banane et à la Ricotta - bruschette fraise banane ricotta - - - 2 - - - KCA#fd9db147f698ab1c84b0905704258a5f
Actimel Goût Fraise - actimel gout fraise - - - 415 - - - KCA#7a40c0ab695dfb5c44d0c4e63af769a2
Confiture de Fraise - confiture de fraise - extra ou classique - - 0 - - - CIQ#41b7efec1a5bddcbc9466fbd067f31bf
Crème Glacée à la Fraise - creme glacee fraise - - - 45 - - - KCA#6fce1a9e8f62321ee65590b95d8a9dbf
Jus d'Orange, Mangue et Fraise - ju orange mangue fraise - - - 60 - - - KCA#12cc18043b0813e5110bb808101edc8e
Boisson Lactée Aromatisée à la Fraise - boisson lactee aromatisee fraise - sucrée, au lait partiellement écrémé, enrichie à la vitamine D - - 0 - - - CIQ#3b0dcd3193f9f8c572eef4e1e4988355
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
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 '% sucre %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Sucre - sucre - en poudre - - 13047 - - - KCA#a61491f490cb44bcf59ef948a0097b8f
Sucre - sucre - en morceau - - 0 - - - KCA#54f1b4b11e3cacc4441b29dc0d4e6d7d
Sucre d'Orge - sucre orge - sucre d'orge - - 0 - - - KCA#0aad9125e3f7faaedd95c65b67cdfff8
Thé Sucré (1 Sucre) - the sucre - sucré (1 sucre) - - 0 - - - KCA#ce556337e0d9788306e32b24fe0fe081
Tarte au Sucre - tarte sucre - au sucre - - 0 - - - KCA#b88be5d2e1f4fc863d3a9d26612348c4
Thé sans Sucre - the san sucre - sans sucre - - 0 - - - KCA#9de5d7e3a39cb14df7a2014ed9319364
Cramique au Sucre - cramique sucre - - - 0 - - - KCA#206b9e9912d03ca3e5d1fb7e0ec65a02
Thé au Lait Sucré - the lait sucre - 1 sucre - - 2799 - - - KCA#f79219b7fdb1e0186da32547f7467cc3
Compote sans Sucre Ajouté - compote san sucre ajoute - - - 2324 - - - KCA#17a6c2151d31a2bb189254fd2c58c402
Crêpe Beurre Sucre - crepe beurre sucre - - - 1261 - - - KCA#adf06f3567a044e35baea32b960ad4dc
Thé au Lait sans Sucre - the lait san sucre - sans sucre - - 0 - - - KCA#978c8f4bf945ce13218480e6d937996a
Yaourt Maigre Sucré - yaourt maigre sucre - maigre sucré - - 0 - - - KCA#ab6f36de7f77a66f9719584243652846
Yaourt Nature Sucré - yaourt nature sucre - nature sucré - - 0 - - - KCA#04a07931e0bb88a2fa79ac588ff372a1
Yaourt Nature Sucré Canne - yaourt nature sucre canne - sucré canne - - 0 - - - KCA#d4d5ace21d166e38b4293d4e7494b03b
Lait Concentré Sucré - lait concentre sucre - entier - - 54 - - - CIQ#d4f364c31af4c7307ce5a6915bf7b666
Thé à la Menthe sans Sucre - the menthe san sucre - sans sucre - - 0 - - - KCA#d6b018fd859ac0e6ee70bca51b09db90
Lait Concentré Sucré Entier - lait concentre sucre entier - - - 0 - - - KCA#88a049a8f0f8d6c0874b091aa262d73d
Chewing-gum sans Sucre - chewing gum san sucre - - - 1111 - - - KCA#bee105cc0aa2520383874741d086579b
Chocolat Noir sans Sucres Ajoutés - chocolat noir san sucre ajoute - avec édulcorants, en tablette - - 0 - - - CIQ#3cece312c84cb7ddd4bcc80edf31a153
Chocolat au Lait sans Sucres Ajoutés - chocolat lait san sucre ajoute - avec édulcorants, tablette - - 0 - - - CIQ#09bc5fef8a8b1113265bb2a0ddc95b2f
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': 'Fraises au sucre', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Fraise', 'normName': ' fraise ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 0, 'id': 'CIQ#a1e7daabf0eef222eb55cee2d9464244', 'quantity': '', 'quantityLem': '', 'pack': ['FRA.w100'], 'type': 'fruit', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}, {'name': 'Sucre', 'normName': ' sucre ', 'comment': 'en poudre', 'normComment': ' en poudre ', 'rank': 13047, 'id': 'KCA#a61491f490cb44bcf59ef948a0097b8f', 'quantity': '', 'quantityLem': '', 'pack': ['CSS.w5'], 'type': 'ingredient', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 7.10045313835144}
----------------------------------------------------------------------------------
LLM CPU Time: 7.10045313835144