Input path: /home/debian/html/nutritwin/output_llm/670e414d2efcb/input.json
Output path: /home/debian/html/nutritwin/output_llm/670e414d2efcb/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": "Babybel",
"quantity": "1 portion",
"weight": "20",
"cooking": "",
"brand": "Babybel",
"company": "Bel",
"type": "fromage",
"time": "grignotage",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "Babybel",
"quantity": "1 portion",
"weight": "20",
"cooking": "",
"brand": "Babybel",
"company": "Bel",
"type": "fromage",
"time": "grignotage",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Babybel", "quantity": "1 portion", "weight": "20", "cooking": "", "brand": "Babybel", "company": "Bel", "type": "fromage", "time": "grignotage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Babybel', 'quantity': '1 portion', 'weight': '20', 'cooking': '', 'brand': 'Babybel', 'company': 'Bel', 'type': 'fromage', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Babybel', 'quantity': '1 portion', 'weight': '20', 'cooking': '', 'brand': 'Babybel', 'company': 'Bel', 'type': 'fromage', 'time': 'grignotage', '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 '% babybel %' AND V_NormTrademark LIKE '%babybel%'
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 '% babybel %' AND V_NormTrademark LIKE '%babybel%'
Third 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 '% babybel %' AND V_NormAggr LIKE '% babybel %' AND V_NormAggr LIKE '% bel %'
------------- Found solution (max 20) --------------
Babybel - babybel - - group Bel - 0 - 3073780460262 - 3073780460262 - OFF#ab5692c8a74a7bb6128db8fe9f7947f2
Babybel - babybel - - group Bel - 0 - 3073780975063 - 3073780460262 - OFF#5003711f2f2e372cc24eebf535c00b7c
Babybel - babybel - - group Bel - 0 - 3073780918763 - 3073780460262 - OFF#39d6e30d44a3d2271716d0c701c43ebb
Babybel - babybel - - group Bel - 0 - 3073780968515 - 3073780460262 - OFF#cacef27920ea49182e1ba2d69d51afcf
Babybel - babybel - - group Bel - 0 - 3073781091533 - 3073780460262 - OFF#9a62971731070ae14a622a4f52dc36b2
Babybel - babybel - - group Bel - 0 - 3073781094497 - 3073780460262 - OFF#7f25361b90854edf9900d1c27021a138
Babybel - babybel - - group Bel - 0 - 3073781106374 - 3073780460262 - OFF#ab66adf8ddb737cbe7dcaa73d6bc9932
Babybel - babybel - - group Bel - 0 - 3073781079272 - 3073780460262 - OFF#4a30a558957d0bfcb54e0b9b3a01ffe2
Babybel - babybel - - group Bel - 0 - 3073781106435 - 3073780460262 - OFF#9af70bb2e2a75fc46bb4247488338eb0
Babybel - babybel - - group Bel - 0 - 3073781178005 - 3073780460262 - OFF#06f38a1485a2240c4e966708bfae800e
Babybel - babybel - - group Bel - 0 - 3073781069716 - 3073780460262 - OFF#70e32e2dad98168f63c7ef06780119b0
Babybel BIO - babybel bio - - group Bel - 0 - 3073781108842 - 3073781108842 - OFF#01eecd9f0ffc34903325eacdec23b1f3
Babybel Light - babybel light - - group Bel - 0 - 3073781106398 - 3073781106398 - OFF#dd7f97d138a0adceae191d200759a064
Babybel Protein - babybel protein - - group Bel - 0 - 3073781136814 - 3073781136814 - OFF#fb8e06962da28872ad4eb74c5273dc4d
Babybel Tranches - babybel tranche - - group Bel - 0 - 3073780874687 - 3073780874687 - OFF#6b9aa75f0dfe2ea3773eb06f9c2a699a
Babybel Moelleux Généreux - babybel moelleu genereu - - group Bel - 0 - 3073783318195 - 3073783318195 - OFF#b08496017502daebd522e1e08c93b1cf
Mini Babybel - mini babybel - - group Bel - 0 - 26045306 - 26045306 - OFF#299471cfbd11a1784f549d877808f074
Mini Babybel - mini babybel - - group Bel - 0 - 3073781071955 - 26045306 - OFF#b22ea355af5887bb44f5994d83ffa1ca
Mini Babybel - mini babybel - - group Bel - 0 - 8132874887318 - 26045306 - OFF#6adb42d347e29677a1a740fe3d6bc98c
Mini Babybel - mini babybel - - group Bel - 0 - 3073781088915 - 26045306 - OFF#f39f9520710719516fcb96c17da7f5dd
----------------------------------------------------
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
ERROR: no solution for picto in the first solution
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': 'Babybel', 'normName': ' babybel ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#ab5692c8a74a7bb6128db8fe9f7947f2', 'quantity': '1 portion', 'quantityLem': '1 portion', 'pack': ['UN2.w20'], 'type': 'fromage', 'gtin': '3073780460262', 'gtinRef': '3073780460262', 'brand': 'group Bel', 'time': 'grignotage', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.7936861515045166}
----------------------------------------------------------------------------------
LLM CPU Time: 3.7936861515045166