Input path: /home/debian/html/nutritwin/output_llm/670bb53c95603/input.json
Output path: /home/debian/html/nutritwin/output_llm/670bb53c95603/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 -----------------------------
I can provide information based on what is visible in this image, but please note that certain specifics like weight and quantity cannot be accurately determined without additional context or packaging details. Here is an interpretation in the requested format:
```json
[
{
"name": "Confiture de fraise",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "",
"company": "",
"type": "Confiture",
"time": "petit-déjeuner",
"event": "declaration"
},
{
"name": "Tartine",
"quantity": "",
"weight": "",
"cooking": "Tartiné",
"brand": "",
"company": "",
"type": "Produit de boulangerie",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
Please note that since the brand is not discernible from the image, I've left the "brand" and "company" fields empty. The image does not allow me to estimate the weight in grams or the quantity of the product depicted. The food type and mealtime suggested here are based on common uses for this type of food—confiture de fraise is customarily used as a breakfast item on toast in many cultures, implying that the time might correspond to "petit-déjeuner." However, without knowing
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
I can provide information based on what is visible in this image, but please note that certain specifics like weight and quantity cannot be accurately determined without additional context or packaging details. Here is an interpretation in the requested format:
```json
[
{
"name": "Confiture de fraise",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "",
"company": "",
"type": "Confiture",
"time": "petit-déjeuner",
"event": "declaration"
},
{
"name": "Tartine",
"quantity": "",
"weight": "",
"cooking": "Tartiné",
"brand": "",
"company": "",
"type": "Produit de boulangerie",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
Please note that since the brand is not discernible from the image, I've left the "brand" and "company" fields empty. The image does not allow me to estimate the weight in grams or the quantity of the product depicted. The food type and mealtime suggested here are based on common uses for this type of food—confiture de fraise is customarily used as a breakfast item on toast in many cultures, implying that the time might correspond to "petit-déjeuner." However, without knowing
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Confiture de fraise", "quantity": "", "weight": "", "cooking": "", "brand": "", "company": "", "type": "Confiture", "time": "petit-déjeuner", "event": "declaration" }, { "name": "Tartine", "quantity": "", "weight": "", "cooking": "Tartiné", "brand": "", "company": "", "type": "Produit de boulangerie", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Confiture de fraise', 'quantity': '', 'weight': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'Confiture', 'time': 'petit-déjeuner', 'event': 'declaration'}, {'name': 'Tartine', 'quantity': '', 'weight': '', 'cooking': 'Tartiné', 'brand': '', 'company': '', 'type': 'Produit de boulangerie', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'Confiture de fraise', 'quantity': '', 'weight': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'Confiture', 'time': 'petit-déjeuner', '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 '% confiture de fraise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Confiture de Fraise - confiture de fraise - extra ou classique - - 0 - - - CIQ#41b7efec1a5bddcbc9466fbd067f31bf
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
----------- result to be analyzed -----------
{'name': 'Tartine', 'quantity': '', 'weight': '', 'cooking': 'Tartiné', 'brand': '', 'company': '', 'type': 'Produit de boulangerie', 'time': 'petit-déjeuner', '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 '% tartine %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Tartine - tartine - 50g pain, 30g pâte à tartiner - - 850 - - - KCA#4dea84eda45b1bb9527b4b39d474cacc
Tartine - tartine - 50g pain, 10g Beurre, 30g confiture - - 7418 - - - KCA#021bd6fe85becd19fab9b68620d61c71
Tartine de Miel - tartine de miel - de miel - - 0 - - - KCA#f1120bad0f1824670c66545c12c253b8
Tartine Sarrasin - tartine sarrasin - tartine sarrasin - - 0 - - - KCA#b669c214d32d4f79a42a01ca6d4e8bd0
Tartine Craquante - tartine craquante - extrudée et grillée - - 0 - - - CIQ#b0f8ac9e031af58a2e32774b534cbd6e
Tartine de Confiture - tartine de confiture - de confiture - - 0 - - - KCA#6c5c28a4f42a6ca22e6e6d39dc7c28dc
Tartine de Beurre Doux - tartine de beurre dou - beurre doux - - 0 - - - KCA#73ab96821f0df4bf67f8d4d45dc538ef
----------------------------------------------------
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': 'Confiture de Fraise', 'normName': ' confiture de fraise ', 'comment': 'extra ou classique', 'normComment': ' extra ou classique ', 'rank': 0, 'id': 'CIQ#41b7efec1a5bddcbc9466fbd067f31bf', 'quantity': '', 'quantityLem': '', 'pack': ['CCL.w6', 'CSL.w20'], 'type': 'Confiture', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}, {'name': 'Tartine', 'normName': ' tartine ', 'comment': '50g pain, 30g pâte à tartiner', 'normComment': ' 50g pain 30g pate tartiner ', 'rank': 850, 'id': 'KCA#4dea84eda45b1bb9527b4b39d474cacc', 'quantity': '', 'quantityLem': '', 'pack': ['TRT.w80', 'BAG.w80'], 'type': 'Produit de boulangerie', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'petit-déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 6.923973321914673}
----------------------------------------------------------------------------------
LLM CPU Time: 6.923973321914673