Input path: /home/debian/html/nutritwin/output_llm/6742307726c0c/input.json
Output path: /home/debian/html/nutritwin/output_llm/6742307726c0c/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": "yaourt",
"quantity": "1",
"weight": "125",
"cooking": "",
"brand": "Activia",
"company": "Danone",
"type": "produit laitier",
"time": "grignotage",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "yaourt",
"quantity": "1",
"weight": "125",
"cooking": "",
"brand": "Activia",
"company": "Danone",
"type": "produit laitier",
"time": "grignotage",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "yaourt", "quantity": "1", "weight": "125", "cooking": "", "brand": "Activia", "company": "Danone", "type": "produit laitier", "time": "grignotage", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'yaourt', 'quantity': '1', 'weight': '125', 'cooking': '', 'brand': 'Activia', 'company': 'Danone', 'type': 'produit laitier', 'time': 'grignotage', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'yaourt', 'quantity': '1', 'weight': '125', 'cooking': '', 'brand': 'Activia', 'company': 'Danone', 'type': 'produit laitier', '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 '% yaourt %' AND V_NormTrademark LIKE '%activia%'
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 '% yaourt %' AND V_NormTrademark LIKE '%activia%'
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 '% yaourt %' AND V_NormAggr LIKE '% activia %' AND V_NormAggr LIKE '% danone %'
------------- Found solution (max 20) --------------
Activia - activia - - Danone - 0 - 5601050033960 - 13565927 - OFF#07641080b0b6c4fa7fdf5963d51fc10a
Activia Saveur Vanille - activia saveur vanille - - Danone - 0 - 3033491024542 - 3033490913267 - OFF#583497a8a27005b2a5cd956847648e08
Activia Céréales Granola Lait d'Amarante - activia cereale granola lait amarante - - Danone - 0 - 3033491360510 - 3033491360510 - OFF#f5b866ac271d54873a923048c1a52a84
Activia Bifidus Cereales Graines de Pavot - activia bifidu cereale graine de pavot - - Danone - 0 - 3033491360527 - 3033491235412 - OFF#a4b31a55550eda6fa84dc28bd48b3552
Activia Bifidus Actiregularis Fusion Fraise Grenade - activia bifidu actiregulari fusion fraise grenade - - Danone - 0 - 5410146416019 - 5410146416019 - OFF#531c30b765f393bba2acf46cf613a487
Yaourt Activia - yaourt activia - - Danone - 0 - 5601050033458 - 5601050033458 - OFF#1f0d77a5f5259e9f2e56862f3f1baaa5
Yaourt Activia - yaourt activia - - Danone - 0 - 3330261040307 - 5601050033458 - OFF#536bc5cb05abee8b3f5ab96b9be25862
Yaourt Activia au Bifidus - yaourt activia bifidu - - Danone - 0 - 11284178 - 11284178 - OFF#6f0b3ad51fbecf7f8560128e54384809
Yaourt Brassé Nature Activia - yaourt brasse nature activia - - Danone - 0 - 3033491370328 - 3033491370328 - OFF#0bbff08ebbc09dfc781db6636f954377
Yaourts Fruits Exotiques Activia - yaourt fruit exotique activia - - Danone - 0 - 3033491188046 - 3033491188046 - OFF#096312a3cde3b9001c9a2998a259a95a
Yaourt Activia au Bifidus Nature Sucré - yaourt activia bifidu nature sucre - - Danone - 0 - 3368850002979 - 3368850002979 - OFF#ad5c03b52ea3dc1582e777f6cd3709fe
Activia Yaourt à Boire - activia yaourt boire - - Danone - 0 - 5601050033878 - 5601050033878 - OFF#cac91a72812fc842fe44249cf9ff0ca5
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Activia', 'normName': ' activia ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#07641080b0b6c4fa7fdf5963d51fc10a', 'quantity': '1', 'quantityLem': '1', 'pack': ['YA2.w125', 'YA9.w125'], 'type': 'produit laitier', 'gtin': '5601050033960', 'gtinRef': '13565927', 'brand': 'Danone', 'time': 'grignotage', 'event': 'declaration', 'serving': 'YA2-100', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 2.884333848953247}
----------------------------------------------------------------------------------
LLM CPU Time: 2.884333848953247