Input path: /home/debian/html/nutritwin/output_llm/67350de9c806d/input.json
Output path: /home/debian/html/nutritwin/output_llm/67350de9c806d/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": "Brie",
"quantity": "1",
"weight": "500g",
"cooking": "",
"brand": "Auchan",
"company": "Groupe Auchan",
"type": "Fromage",
"time": "",
"event": "declaration"
}
]
```
Pour répondre complètement à la demande, il faudrait que l'image fournisse les informations nécessaires pour déterminer l'heure du jour (pour assigner "petit-déjeuner", "déjeuner", "grignotage", ou "dîner"), mais aucune horloge ou indication de temps n'est visible dans cette image.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "Brie",
"quantity": "1",
"weight": "500g",
"cooking": "",
"brand": "Auchan",
"company": "Groupe Auchan",
"type": "Fromage",
"time": "",
"event": "declaration"
}
]
```
Pour répondre complètement à la demande, il faudrait que l'image fournisse les informations nécessaires pour déterminer l'heure du jour (pour assigner "petit-déjeuner", "déjeuner", "grignotage", ou "dîner"), mais aucune horloge ou indication de temps n'est visible dans cette image.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Brie", "quantity": "1", "weight": "500g", "cooking": "", "brand": "Auchan", "company": "Groupe Auchan", "type": "Fromage", "time": "", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Brie', 'quantity': '1', 'weight': '500g', 'cooking': '', 'brand': 'Auchan', 'company': 'Groupe Auchan', 'type': 'Fromage', 'time': '', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Brie', 'quantity': '1', 'weight': '500g', 'cooking': '', 'brand': 'Auchan', 'company': 'Groupe Auchan', 'type': 'Fromage', '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 '% brie %' AND V_NormTrademark LIKE '%auchan%'
------------- Found solution (max 20) --------------
Brie BIO - brie bio - - Auchan - 0 - 7214632023099 - 7214632023099 - OFF#d25c96364006746a17a5ddf370b1201a
Brie de Meaux - brie de meau - - Auchan - 0 - 3596710333271 - 3596710333271 - OFF#26130c2df39b877237f0311400896050
Brie de Meaux Affiné AOP - brie de meau affine aop - - Auchan - 0 - 2092019019096 - 2092019019096 - OFF#63ae449c221a36ac7f9d23e87f2b6f22
Petit Brie - petit brie - - Auchan - 0 - 3596710748556 - 3596710354023 - OFF#0ba2943a334427e5068c603ef5aca30d
Le Petit Brie - petit brie - - Auchan - 0 - 3596710354023 - 3596710354023 - OFF#25e883c0d485b01085c45f56b1e64bd4
Pointe de Brie - pointe de brie - - Auchan - 0 - 3077200963111 - 3077200963111 - OFF#6f336c57fe4091a7a05e452195ea1200
Pointe de Brie - pointe de brie - - Auchan - 0 - 3596710092680 - 3077200963111 - OFF#88a5043d5e48724957b0029c030b634a
Pointe de Brie - pointe de brie - - Auchan - 0 - 3286710092680 - 3077200963111 - OFF#7a745a0cb4f8e23a3203053a3c9cc6fc
----------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Brie BIO', 'normName': ' brie bio ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#d25c96364006746a17a5ddf370b1201a', 'quantity': '1', 'quantityLem': '1', 'pack': ['UN2.w20'], 'type': 'Fromage', 'gtin': '7214632023099', 'gtinRef': '7214632023099', 'brand': 'Auchan', 'time': '', 'event': 'declaration', 'serving': 'UN2-10', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.924301624298096}
----------------------------------------------------------------------------------
LLM CPU Time: 4.924301624298096