Input path: /home/debian/html/nutritwin/output_llm/66ed828c6b68e/input.json
Output path: /home/debian/html/nutritwin/output_llm/66ed828c6b68e/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": "ketchup",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "Heinz",
"company": "H.J. Heinz Company",
"type": "condiment",
"time": "",
"event": "declaration"
}
]
```
Je ne peux pas déterminer la quantité ou le poids exact du ketchup à partir de l'image, ni l'heure de la journée correspondante pour lier à un repas spécifique comme le petit-déjeuner, le déjeuner, le grignotage ou le dîner.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "ketchup",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "Heinz",
"company": "H.J. Heinz Company",
"type": "condiment",
"time": "",
"event": "declaration"
}
]
```
Je ne peux pas déterminer la quantité ou le poids exact du ketchup à partir de l'image, ni l'heure de la journée correspondante pour lier à un repas spécifique comme le petit-déjeuner, le déjeuner, le grignotage ou le dîner.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "ketchup", "quantity": "", "weight": "", "cooking": "", "brand": "Heinz", "company": "H.J. Heinz Company", "type": "condiment", "time": "", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'ketchup', 'quantity': '', 'weight': '', 'cooking': '', 'brand': 'Heinz', 'company': 'H.J. Heinz Company', 'type': 'condiment', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'ketchup', 'quantity': '', 'weight': '', 'cooking': '', 'brand': 'Heinz', 'company': 'H.J. Heinz Company', 'type': 'condiment', 'time': '', '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 '% ketchup %' AND V_NormTrademark LIKE '%heinz%'
------------- Found solution (max 20) --------------
Ketchup - ketchup - - Heinz - 0 - 50157617 - 50157617 - OFF#92f25108392b0d57b1287c850ed0dbc6
Ketchup - ketchup - - Heinz - 0 - 87157178 - 50157617 - OFF#1bee3ab59c8f91faba6f73ea1fdea9be
Ketchup - ketchup - - Heinz - 0 - 50157778 - 50157617 - OFF#4775fa1ab2a0fc49fe94c5b9dda7e6e1
Ketchup - ketchup - - Heinz - 0 - 51154400 - 50157617 - OFF#a4a4b8ca3d3ddbc3114bfe39886ed51e
Ketchup - ketchup - - Heinz - 0 - 8715700421674 - 50157617 - OFF#813037718607814a420e493f582827d8
Ketchup - ketchup - - Heinz - 0 - 74854363 - 50157617 - OFF#c7c2c23fd37f3ab6aef5cc61127fc034
Ketchup - ketchup - - Heinz - 0 - 87157543 - 50157617 - OFF#860ed079a77b9144a644584ddf4cbe59
Ketchup - ketchup - - Heinz - 0 - 8715700404257 - 50157617 - OFF#5362c7af75a074d329785c9b3de07ce8
Ketchup - ketchup - - Heinz - 0 - 8715700115849 - 50157617 - OFF#4c9de3af4eaf87e78e56466ef90b63e4
Ketchup - ketchup - - Heinz - 0 - 8715700117911 - 50157617 - OFF#84cf684445410a1d96d493b1e01a14c9
Ketchup - ketchup - - Heinz - 0 - 8715700122021 - 50157617 - OFF#2ffb951b1c2d3cd96b2b5bdf0835e0bf
Ketchup - ketchup - - Heinz - 0 - 8715700122069 - 50157617 - OFF#36ff9dda6c33a47af961f8e96ce74e41
Ketchup - ketchup - - Heinz - 0 - 8715700122014 - 50157617 - OFF#3d8cde6fcf279be9ff7a2caa2f3ce942
Ketchup - ketchup - - Heinz - 0 - 8715700419688 - 50157617 - OFF#ca9c6a363881953928bbcc3bbb9eb6c8
Ketchup BIO - ketchup bio - - Heinz - 0 - 8715700110103 - 8715700110103 - OFF#af0451ae16ae7cb366153394c70d9f83
Ketchup Heinz - ketchup heinz - - Heinz - 0 - 5900783001773 - 5900783001773 - OFF#15a5c34d345f0d453de68a0bbcc74559
Ketchup Heinz - ketchup heinz - - Heinz - 0 - 8715700017006 - 5900783001773 - OFF#e71db3366c4cd03b9a891268df8e2758
Ketchup Heinz - ketchup heinz - - Heinz - 0 - 87156515 - 5900783001773 - OFF#ed27c8456d1c7069997430dd2af646d4
Ketchup Tomato - ketchup tomato - - Heinz - 0 - 8715700421476 - 8715700421476 - OFF#4171f89a431b22fea995f5543cf28867
Ketchup à la Tomate - ketchup tomate - - Heinz - 0 - 8715700110202 - 8715700110202 - OFF#c7725ee743d651f66124fb515144fbab
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
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': 'Ketchup', 'normName': ' ketchup ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#92f25108392b0d57b1287c850ed0dbc6', 'quantity': '', 'quantityLem': '', 'pack': ['CSL.w18', 'CCL.w5'], 'type': 'condiment', 'gtin': '50157617', 'gtinRef': '50157617', 'brand': 'Heinz', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.889436960220337}
----------------------------------------------------------------------------------
LLM CPU Time: 4.889436960220337