Input path: /home/debian/html/nutritwin/output_llm/6612c7e73f96f/input.json
Output path: /home/debian/html/nutritwin/output_llm/6612c7e73f96f/output.json
Input text: Lentilles tomates cerises fêta
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: Lentilles tomates cerises fêta
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###Lentilles tomates cerises fêta###.
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: "Lentilles tomates cerises fêta".
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 the instructions provided, here's the analysis of the sentence "Lentilles tomates cerises fêta".
Since the sentence does not provide specific quantities, brands, companies, cooking modes, times of consumption, or events (declaration, intent, unknown), these fields will be left blank or set to "unknown".
The sentence appears to list ingredients rather than a prepared dish, so the "name" and "type" fields will be used to capture these.
Here's the result in the requested JSON format:
```json
[
{
"name": "lentilles",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume sec",
"time": "unknown",
"event": "unknown"
},
{
"name": "tomates cerises",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume",
"time": "unknown",
"event": "unknown"
},
{
"name": "fêta",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "fromage",
"time": "unknown",
"event": "unknown"
}
]
```
Each tuple in the array represents an ingredient found in the sentence. The "name" field is the name of the ingredient, and the "type" field is the type of food it represents. The "quantity", "cooking", "brand", "company", "time", and "event" fields are left blank or set to "unknown" because this information is not provided in the sentence.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on the instructions provided, here's the analysis of the sentence "Lentilles tomates cerises fêta".
Since the sentence does not provide specific quantities, brands, companies, cooking modes, times of consumption, or events (declaration, intent, unknown), these fields will be left blank or set to "unknown".
The sentence appears to list ingredients rather than a prepared dish, so the "name" and "type" fields will be used to capture these.
Here's the result in the requested JSON format:
```json
[
{
"name": "lentilles",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume sec",
"time": "unknown",
"event": "unknown"
},
{
"name": "tomates cerises",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "légume",
"time": "unknown",
"event": "unknown"
},
{
"name": "fêta",
"quantity": "",
"cooking": "",
"brand": "",
"company": "",
"type": "fromage",
"time": "unknown",
"event": "unknown"
}
]
```
Each tuple in the array represents an ingredient found in the sentence. The "name" field is the name of the ingredient, and the "type" field is the type of food it represents. The "quantity", "cooking", "brand", "company", "time", and "event" fields are left blank or set to "unknown" because this information is not provided in the sentence.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "lentilles", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume sec", "time": "unknown", "event": "unknown" }, { "name": "tomates cerises", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "légume", "time": "unknown", "event": "unknown" }, { "name": "fêta", "quantity": "", "cooking": "", "brand": "", "company": "", "type": "fromage", "time": "unknown", "event": "unknown" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'lentilles', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'légume sec', 'time': 'unknown', 'event': 'unknown'}, {'name': 'tomates cerises', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'légume', 'time': 'unknown', 'event': 'unknown'}, {'name': 'fêta', 'quantity': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'fromage', 'time': 'unknown', 'event': 'unknown'}], 'cost': 0.07973999999999999}
--------------------------------------------------------------------------------
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 '% lentille %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Lentille Verte - lentille verte - bouillie/cuite à l'eau - - 0 - - - CIQ#2c651245fd51430a08f5f743a38e625d
Lentilles au Chou - lentille chou - - - 80 - - - KCA#489e1f0cd1ca649bf933982eeea09c76
Lentilles au Chorizo - lentille chorizo - - - 37 - - - KCA#861eb5d32ed6b0481dec0520a24603f9
Lentilles au Tapioca - lentille tapioca - - - 14 - - - KCA#ae447a6202b0cfdacefc0178b618fa9f
Lentilles à la Tomate - lentille tomate - - - 90 - - - KCA#390cc8fc9ac0347cc368d9edb55c75ba
Lentilles à l'Indienne - lentille indienne - - - 131 - - - KCA#aafe9bf5fa367006ca8495636212dd2c
Lentilles aux Epinards - lentille au epinard - - - 22 - - - KCA#27e5618f833b2a5e6ef3f51546ae28c8
Lentilles au Petit Salé - lentille petit sale - - - 111 - - - KCA#d5a13af958f13e7553290f9759daf561
Lentilles à l'Alsacienne - lentille alsacienne - - - 11 - - - KCA#139499582757ddc0bc0c3343a90c1e3e
Lentilles à la Mexicaine - lentille mexicaine - - - 6 - - - KCA#ac43d43bdd48b5149d3e11ecdc00e940
Lentilles à la Dijonnaise - lentille dijonnaise - - - 17 - - - KCA#44c977cc329aca863f2d3bade08576ca
Lentilles au Fromage Blanc - lentille fromage blanc - - - 15 - - - KCA#9dc98801283c17ef5c78fb1b99fbbde7
Lentilles Cuisinées Conserve - lentille cuisinee conserve - - - 92 - - - KCA#eb0782e831ea8edc9e8d7b277f60258e
Lentilles en Salade à l'Anchois - lentille en salade anchoi - - - 149 - - - KCA#e6f38f200d1a50536880339bae2385a6
Lentilles Brunes à la Courgette et au Chorizo - lentille brune courgette chorizo - - - 6 - - - KCA#488b3d0b5e6b068cb458940010656fd5
Salade Lentilles Tofu - salade lentille tofu - - - 274 - - - KCA#30c6087242cdba6fd93fe05b010645b7
Porc aux Lentilles - porc au lentille - - - 15 - - - KCA#936ca9b6d1276ef782aa1eee7aa55433
Soupe de Lentilles - soupe de lentille - de lentilles - - 0 - - - KCA#9087063b7a7bfcac582648b7dae34ee5
Soupe aux Lentilles - soupe au lentille - - - 0 - - - CIQ#5c3dd6ee1e4355ffb521410c8cfdaf7f
Potage aux Lentilles - potage au lentille - - - 28 - - - KCA#c2f96b29ac915e6438bbe8d85a78e370
----------------------------------------------------
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 '% tomate cerise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Tomate Cerise - tomate cerise - crue - - 0 - - - CIQ#9f76e2172737f480f1c9b66f3627bfb0
Tomate Cerise - tomate cerise - tomate cerise - - 0 - - - KCA#fc7d1647e177b261c9a22262037f6216
Tomates Cerises Rôties aux Broccolinis - tomate cerise rotie au broccolini - et graines de citrouille - - 7 - - - KCA#b4e4bc20b89f5f2678a4843a5d0f40ea
----------------------------------------------------
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 '% feta %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Feta - feta - - - 4690 - - - KCA#836376d4d946da7f776d2aecb0221de2
Fromage Type Feta, au Lait de Vache - fromage type feta lait de vache - - - 82 - - - KCA#8dcf81e6e056a6deaf623d8439fc3078
Fritas à la Tomate, Feta et Pancetta - frita tomate feta pancetta - - - 26 - - - KCA#e91a4ef92f19079767cd8ab9a9fd6102
Salade de Betterave, Haricots, Feta et Menthe - salade de betterave haricot feta menthe - - - 106 - - - KCA#f31a5e8ed43442368982779c1513d16f
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
--------------------------------- final result -----------------------------------
{'prompt': 'Lentilles tomates cerises fêta', 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [{'name': 'Lentille Verte', 'normName': ' lentille verte ', 'comment': "bouillie/cuite à l'eau", 'normComment': ' bouillie/cuite eau ', 'rank': 0, 'id': 'CIQ#2c651245fd51430a08f5f743a38e625d', 'quantity': '', 'quantityLem': '', 'pack': ['LEG.w150'], 'type': 'légume sec', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}, {'name': 'Tomate Cerise', 'normName': ' tomate cerise ', 'comment': 'crue', 'normComment': ' crue ', 'rank': 0, 'id': 'CIQ#9f76e2172737f480f1c9b66f3627bfb0', 'quantity': '', 'quantityLem': '', 'pack': ['CER.w150'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}, {'name': 'Feta', 'normName': ' feta ', 'comment': '', 'normComment': '', 'rank': 4690, 'id': 'KCA#836376d4d946da7f776d2aecb0221de2', 'quantity': '', 'quantityLem': '', 'pack': ['CUB.w20'], 'type': 'fromage', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'unknown', 'event': 'unknown', 'serving': '', 'posiNormName': 0}], 'activity': []}, 'cputime': 11.835751056671143}
----------------------------------------------------------------------------------
LLM CPU Time: 11.835751056671143