Input path: /home/debian/html/nutritwin/output_llm/672caae531061/input.json
Output path: /home/debian/html/nutritwin/output_llm/672caae531061/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 -----------------------------
Pour analyser cette image, il faut noter que je n'ai pas la possibilité de déterminer avec précision des informations telles que la marque ou le poids des aliments et des boissons, sauf s'il s'agit d'un produit très reconnaissable. Cependant, je vais fournir une estimation basée sur l’apparence de ce que je vois :
```json
[
{"name": "oeuf", "quantity": "1", "weight": "50g", "cooking": "au plat", "brand": "", "company": "", "type": "aliment", "time": "petit-déjeuner", "event": "declaration"},
{"name": "purée de tomate", "quantity": "portion", "weight": "100g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"},
{"name": "fromage feta", "quantity": "portion", "weight": "30g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"}
]
```
Il est important de noter que les poids sont des estimations puisqu'il est impossible de les mesurer avec précision à partir de l'image. De
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Pour analyser cette image, il faut noter que je n'ai pas la possibilité de déterminer avec précision des informations telles que la marque ou le poids des aliments et des boissons, sauf s'il s'agit d'un produit très reconnaissable. Cependant, je vais fournir une estimation basée sur l’apparence de ce que je vois :
```json
[
{"name": "oeuf", "quantity": "1", "weight": "50g", "cooking": "au plat", "brand": "", "company": "", "type": "aliment", "time": "petit-déjeuner", "event": "declaration"},
{"name": "purée de tomate", "quantity": "portion", "weight": "100g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"},
{"name": "fromage feta", "quantity": "portion", "weight": "30g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"}
]
```
Il est important de noter que les poids sont des estimations puisqu'il est impossible de les mesurer avec précision à partir de l'image. De
------------------------------------------------------
------------------------ After simplification ------------------------
[ {"name": "oeuf", "quantity": "1", "weight": "50g", "cooking": "au plat", "brand": "", "company": "", "type": "aliment", "time": "petit-déjeuner", "event": "declaration"}, {"name": "purée de tomate", "quantity": "portion", "weight": "100g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"}, {"name": "fromage feta", "quantity": "portion", "weight": "30g approx", "cooking": "", "brand": "", "company": "", "type": "aliment", "time": "déjeuner", "event": "declaration"}]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'oeuf', 'quantity': '1', 'weight': '50g', 'cooking': 'au plat', 'brand': '', 'company': '', 'type': 'aliment', 'time': 'petit-déjeuner', 'event': 'declaration'}, {'name': 'purée de tomate', 'quantity': 'portion', 'weight': '100g approx', 'cooking': '', 'brand': '', 'company': '', 'type': 'aliment', 'time': 'déjeuner', 'event': 'declaration'}, {'name': 'fromage feta', 'quantity': 'portion', 'weight': '30g approx', 'cooking': '', 'brand': '', 'company': '', 'type': 'aliment', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'oeuf', 'quantity': '1', 'weight': '50g', 'cooking': 'au plat', 'brand': '', 'company': '', 'type': 'aliment', 'time': 'petit-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 '% oeuf %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Oeuf - oeuf - cru - - 177 - - - CIQ#89c78a1c04879b2ae973694f50092c79
Oeuf - oeuf - dur - - 0 - - - CIQ#fda269f79263c80adf5b9b2c3c29c1d7
Oeuf - oeuf - poché - - 0 - - - CIQ#8d04a52d9c575bdba000c6f1cf343ab0
Oeuf - oeuf - en poudre - - 0 - - - CIQ#f4b4ef030ae3fcf5bbfea0a792a9ab66
Oeuf - oeuf - à la coque - - 3414 - - - CIQ#37567ba433b1d5278fcb1a7813128c96
Oeuf - oeuf - blanc, blanc d'oeuf - - 0 - - - CIQ#f8541a0a53cfc718c4be702af74b13a6
Oeuf - oeuf - jaune, jaune d'oeuf - - 0 - - - CIQ#caff0c1a1a02e4d086dd987b784e898a
Oeuf - oeuf - au plat, frit, salé - - 0 - - - CIQ#f9852838d9a21ae4940ea5102b58e8d1
Oeuf - oeuf - blanc, blanc d'oeuf, cru - - 0 - - - CIQ#91658f86dcc6220b09b2ffc7d5e4d309
Oeuf - oeuf - jaune, jaune d'oeuf, cru - - 0 - - - CIQ#cab44469339c33f14bf4c536019e8f57
Oeuf - oeuf - au plat, sans matière grasse - - 0 - - - CIQ#36e518c64c0e0c5a908f4674e1587a9c
Oeuf - oeuf - brouillé, avec matière grasse - - 0 - - - CIQ#89ffd23269a5b9a6910f6a7bb1a17945
Oeuf - oeuf - blanc, blanc d'oeuf, en poudre - - 0 - - - CIQ#6dc23efe8a247a89ac865e3539278bb1
Oeuf - oeuf - jaune, jaune d'oeuf, en poudre - - 0 - - - CIQ#20ab10b969e15e835fce7d54c1815eeb
Oeuf Dur - oeuf dur - - - 34213 - - - KCA#0c9196f2d28e211ac0aeb81d4c9361a9
Oeuf Poché - oeuf poche - - - 645 - - - KCA#1759edc574d011bf3a8af743ed941e6e
Oeuf d'Oie - oeuf oie - cru - - 0 - - - CIQ#d5a0273c1cb314a819952b4272379b24
Oeuf Miroir - oeuf miroir - - - 11945 - - - KCA#5cf9cdac852c9777e406442fcfd51315
Oeufs Panés - oeuf pane - - - 15 - - - KCA#476eea019750878ec03fbc60bcfa9020
Oeufs Frits - oeuf frit - aux Tomates Provençales - - 13 - - - KCA#1939ad70c8a272d6285ac0509778f087
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'purée de tomate', 'quantity': 'portion', 'weight': '100g approx', 'cooking': '', 'brand': '', 'company': '', 'type': 'aliment', '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 '% puree de tomate %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Purée de Tomate - puree de tomate - - - 64 - - - KCA#85d33ede4d716a9cdfe18fb5ff2aa38a
Purée de Tomate au Basilic - puree de tomate basilic - - - 29 - - - KCA#04648cc2d8d08621e97f44cd26f33635
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'fromage feta', 'quantity': 'portion', 'weight': '30g approx', 'cooking': '', 'brand': '', 'company': '', 'type': 'aliment', '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 '% fromage feta %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
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 '% fromage feta %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Dés de Féta - de de feta - - Belle France - 0 - 3258563430353 - 3258563430353 - OFF#6eedfc92add70c0959b4322eafc40ecd
Salade de Falafel et de Boulgour de Blé - salade de falafel de boulgour de ble - - Marks & Spencer - 0 - 61370944 - 61370944 - OFF#d4d42afc8e08d36350009f093bdfcfd0
Salade de Pâtes Fromage Feta et Tomates Rôties Lentement - salade de pate fromage feta tomate rotie lentement - - Marks & Spencer - 0 - 00900720 - 00900720 - OFF#44be29b809c06b2b7693d8b9eb9672a7
Tomates Concombre Fromage Feta et Olives - tomate concombre fromage feta olive - - Marks & Spencer - 0 - 00901871 - 00901871 - OFF#d4d3b931e0de1d845cfb198521ee2320
----------------------------------------------------
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': 'Oeuf', 'normName': ' oeuf ', 'comment': 'cru', 'normComment': ' cru ', 'rank': 177, 'id': 'CIQ#89c78a1c04879b2ae973694f50092c79', 'quantity': '1', 'quantityLem': '1', 'pack': ['OEU.w60'], 'type': 'aliment', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': 'OEU-100', 'posiNormName': 0}, {'name': 'Purée de Tomate', 'normName': ' puree de tomate ', 'comment': '', 'normComment': '', 'rank': 64, 'id': 'KCA#85d33ede4d716a9cdfe18fb5ff2aa38a', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['APL.w150'], 'type': 'aliment', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': 'APL-10', 'posiNormName': 0}, {'name': 'Dés de Féta', 'normName': ' de de feta ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#6eedfc92add70c0959b4322eafc40ecd', 'quantity': 'portion', 'quantityLem': 'portion', 'pack': ['MIM.w20', 'CAM.w30'], 'type': 'aliment', 'gtin': '3258563430353', 'gtinRef': '3258563430353', 'brand': 'Belle France', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 6.837894678115845}
----------------------------------------------------------------------------------
LLM CPU Time: 6.837894678115845