Input path: /home/debian/html/nutritwin/output_llm/671b6d44aec47/input.json
Output path: /home/debian/html/nutritwin/output_llm/671b6d44aec47/output.json
Input text:
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:
==================================================================================================================================
###########################################
# For image extraction, GPT4 is used #
###########################################
==================================== Prompt =============================================
In the image, identify all the foods and the beverages.
For each of them, identify the "name", the "type", the "quantity", if it exists, the "brand" and the "cooking" mode.
"Portions", like "tranche", are quantities.
Ignore what it is not connected to nutrition, beverage or food.
When the "brand" is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "".
Identify what "type" of food.
Identify the "company" to which the "brand" belongs.
Estimate the "weight" in grams or centiliters for each result.
Identify the time is the current time, map it on the closest case: "petit-déjeuner", "déjeuner", "grignotage" or "dîner".
When the "name" has synonyms, use the most common name, example: "yaourt" is more common than "yogourt".
Format the result for each ingredient of food & beverage in french in JSON in an array of tuples {"name":, "quantity":, "weight":, "cooking":, "brand":, "company":, "type":, "time":, "event": "declaration"}.
=========================================================================================
Image recognition....
------------------------------ LLM Raw response -----------------------------
[
{
"name": "fèves",
"quantity": "",
"weight": "200",
"cooking": "cuites",
"brand": "",
"company": "",
"type": "légume",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "lardons",
"quantity": "",
"weight": "50",
"cooking": "cuits",
"brand": "",
"company": "",
"type": "viande",
"time": "déjeuner",
"event": "declaration"
}
]
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
[
{
"name": "fèves",
"quantity": "",
"weight": "200",
"cooking": "cuites",
"brand": "",
"company": "",
"type": "légume",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "lardons",
"quantity": "",
"weight": "50",
"cooking": "cuits",
"brand": "",
"company": "",
"type": "viande",
"time": "déjeuner",
"event": "declaration"
}
]
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "fèves", "quantity": "", "weight": "200", "cooking": "cuites", "brand": "", "company": "", "type": "légume", "time": "déjeuner", "event": "declaration" }, { "name": "lardons", "quantity": "", "weight": "50", "cooking": "cuits", "brand": "", "company": "", "type": "viande", "time": "déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'fèves', 'quantity': '', 'weight': '200', 'cooking': 'cuites', 'brand': '', 'company': '', 'type': 'légume', 'time': 'déjeuner', 'event': 'declaration'}, {'name': 'lardons', 'quantity': '', 'weight': '50', 'cooking': 'cuits', 'brand': '', 'company': '', 'type': 'viande', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'fèves', 'quantity': '', 'weight': '200', 'cooking': 'cuites', 'brand': '', 'company': '', 'type': 'légume', 'time': 'déjeuner', 'event': 'declaration'}
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 '% feve %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Fève - feve - bouillie/cuite à l'eau - - 0 - - - CIQ#a5006d3b91f3195fc254fb21f7b77d34
Salade aux Fèves et au Fromage de Chèvre - salade au feve fromage de chevre - - - 207 - - - KCA#6e81ecb25023c8af886949335c52a64b
Orecchiette aux Fèves et à la Ricotta - orecchiette au feve ricotta - - - 3 - - - KCA#b5bbe089e9316ca6615970910b2beb3b
Falafel ou Boulette de Pois-chiche Et/ou Fève - falafel ou boulette de poi chiche et/ou feve - - - 0 - - - CIQ#39c9188b52b072617f11cb33e93f63d9
Falafel ou Boulette de Pois-chiche Et/ou Fève - falafel ou boulette de poi chiche et/ou feve - frite - - 0 - - - CIQ#8f95207f021791a672b8c691de586b4c
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
----------- result to be analyzed -----------
{'name': 'lardons', 'quantity': '', 'weight': '50', 'cooking': 'cuits', 'brand': '', 'company': '', 'type': 'viande', 'time': 'déjeuner', 'event': 'declaration'}
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 '% lardon %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Lardon Fumé - lardon fume - - - 27 - - - CIQ#488435eb56e2566d01ee32965be54aa0
Lardon Fumé - lardon fume - cru - - 90 - - - CIQ#5a50dd050185cfc51ff408607f13d692
Lardon Nature - lardon nature - - - 0 - - - CIQ#b5bd9ee84e702c3950f798216effa991
Lardon Nature - lardon nature - cru - - 146 - - - CIQ#259853d8e70300bc6b1eea02b216c975
Lardons 25% Sel - lardon 25% sel - - - 34 - - - KCA#15b35ee6f61a9cfa7244c172e14a62db
Pizza aux Lardons - pizza au lardon - oignons et fromage - - 0 - - - CIQ#2ff2fb0af20f513208206f7883b4b537
Omelette aux Lardons - omelette au lardon - - - 607 - - - CIQ#91e89f2662faf022d17d0a50fd266c22
Haricots Verts Lardons - haricot vert lardon - - - 100 - - - KCA#6874f69a8ead359116ae7a390050180e
Pizza au Chèvre et Lardons - pizza chevre lardon - - - 39 - - - CIQ#ddb5e916c47938a27d061776967f9b9d
Salade Chaude au Chou Rouge et aux Lardons - salade chaude chou rouge au lardon - - - 29 - - - KCA#325dfeb5ff8f83b2e8217c45164b8046
Flammenkueche ou Tarte Flambée aux Lardons - flammenkueche ou tarte flambee au lardon - - - 0 - - - CIQ#18ad352c306f2ccb62d0fffd37f882ae
----------------------------------------------------
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': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Fève', 'normName': ' feve ', 'comment': "bouillie/cuite à l'eau", 'normComment': ' bouillie/cuite eau ', 'rank': 0, 'id': 'CIQ#a5006d3b91f3195fc254fb21f7b77d34', 'quantity': '', 'quantityLem': '', 'pack': ['LEG.w150'], 'type': 'légume', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}, {'name': 'Lardon Fumé', 'normName': ' lardon fume ', 'comment': '', 'normComment': '', 'rank': 27, 'id': 'CIQ#488435eb56e2566d01ee32965be54aa0', 'quantity': '', 'quantityLem': '', 'pack': ['PIG.w200'], 'type': 'viande', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.936969041824341}
----------------------------------------------------------------------------------
LLM CPU Time: 3.936969041824341