Input path: /home/debian/html/nutritwin/output_llm/66f396a94740f/input.json
Output path: /home/debian/html/nutritwin/output_llm/66f396a94740f/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 -----------------------------
```json
[
{
"name": "riz long grain",
"quantity": "",
"weight": "",
"cooking": "à ébullition",
"brand": "",
"company": "",
"type": "féculent",
"time": "dîner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "riz long grain",
"quantity": "",
"weight": "",
"cooking": "à ébullition",
"brand": "",
"company": "",
"type": "féculent",
"time": "dîner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "riz long grain", "quantity": "", "weight": "", "cooking": "à ébullition", "brand": "", "company": "", "type": "féculent", "time": "dîner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'riz long grain', 'quantity': '', 'weight': '', 'cooking': 'à ébullition', 'brand': '', 'company': '', 'type': 'féculent', 'time': 'dîner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'riz long grain', 'quantity': '', 'weight': '', 'cooking': 'à ébullition', 'brand': '', 'company': '', 'type': 'féculent', 'time': 'dîner', '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 '% riz long grain %' 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 '% riz long grain %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
3 Riz - riz - - Panzani - 0 - 3038359003400 - 3038359003400 - OFF#b5f5b3054b6637ceec83c0cb541dc801
Riz Thaï - riz thai - - Cora - 0 - 3257980616722 - 3257980616722 - OFF#5712d262ec4562b636f7236720384c28
Riz Thai - riz thai - - Auchan - 0 - 3596710413188 - 3596710413188 - OFF#a59f4cf2e1230b9fedbb860234f5110e
Riz Thaï - riz thai - - Dia - 0 - 8480017306340 - 8480017306340 - OFF#a00afcb9d7f75ba97dab17affe4c3298
Riz Palla - riz palla - - Mars - 0 - 3487400002075 - 3487400000620 - OFF#ee3cebfbf19e3ed785b7d612db2099d3
Riz Quinoa - riz quinoa - - Panzani - 0 - 3038359004254 - 3038359004254 - OFF#eb47d21e124b951c4c927df0bc7002ec
Pilau Rice - pilau rice - - Mars - 0 - 5010034515000 - 5010034515000 - OFF#eed63494231e571feb4b03b6bb1451f7
Riz Arborio - riz arborio - - Carrefour - 0 - 3184830056318 - 3184830056318 - OFF#bcf444766cbe8dad13132928d441b737
Riz Basmati - riz basmati - - Auchan - 0 - 3254560035584 - 3254560035584 - OFF#cab00df09cbef731f7d0e4944aa026a4
Riz Basmati - riz basmati - - Franprix - 0 - 3263850564413 - 3263850564413 - OFF#ba2e61422e7e2052bfbe08dba4269957
Riz Basmati - riz basmati - - Auchan - 0 - 3596710387427 - 3254560035584 - OFF#b005b934e5da2a5b0e209c4f535c038e
Riz Basmati - riz basmati - - Auchan - 0 - 3596710396863 - 3254560035584 - OFF#f4d1315bb1849353b88602bc482ad7fb
Riz Basmati - riz basmati - - Auchan - 0 - 3596710387403 - 3254560035584 - OFF#1ca53b48ea918e83057c96cb005b9b23
Riz Basmati - riz basmati - - Auchan - 0 - 3596710308026 - 3254560035584 - OFF#4df99f075ed51517df3dce17c31db796
Riz Basmati - riz basmati - - Auchan - 0 - 3596710477333 - 3254560035584 - OFF#d16387eab8fe664a4037c87ada985650
Riz Basmati - riz basmati - - Auchan - 0 - 3396710308026 - 3254560035584 - OFF#4cf970f5ad7581dec845198e116ebe01
Riz Basmati - riz basmati - - Auchan - 0 - 3254560667556 - 3254560035584 - OFF#b90544264a43a7a45a50165813bc0bd4
Riz Basmati - riz basmati - - Auchan - 0 - 3596710477326 - 3254560035584 - OFF#462328f06e0f924f7529960c80956dc1
Riz Basmati - riz basmati - - Franprix - 0 - 3263850564611 - 3263850564413 - OFF#879e38442770f80fcd2aaafe2ac511c0
Netto Riz Long - netto riz long - - Les Mousquetaires - 0 - 3250390748250 - 3250390748250 - OFF#4ff6d0cfdda1e3a45fa958d6b5733ebf
----------------------------------------------------
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': '3 Riz', 'normName': ' riz ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#b5f5b3054b6637ceec83c0cb541dc801', 'quantity': '', 'quantityLem': '', 'pack': ['BOR.w150'], 'type': 'féculent', 'gtin': '3038359003400', 'gtinRef': '3038359003400', 'brand': 'Panzani', 'time': 'dîner', 'event': 'declaration', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 2.8117430210113525}
----------------------------------------------------------------------------------
LLM CPU Time: 2.8117430210113525