Input path: /home/debian/html/nutritwin/output_llm/672767090d50a/input.json
Output path: /home/debian/html/nutritwin/output_llm/672767090d50a/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": "calamars",
"quantity": "",
"weight": "",
"cooking": "sauté",
"brand": "",
"company": "",
"type": "fruits de mer",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "persil",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "",
"company": "",
"type": "herbe aromatique",
"time": "déjeuner",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "calamars",
"quantity": "",
"weight": "",
"cooking": "sauté",
"brand": "",
"company": "",
"type": "fruits de mer",
"time": "déjeuner",
"event": "declaration"
},
{
"name": "persil",
"quantity": "",
"weight": "",
"cooking": "",
"brand": "",
"company": "",
"type": "herbe aromatique",
"time": "déjeuner",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "calamars", "quantity": "", "weight": "", "cooking": "sauté", "brand": "", "company": "", "type": "fruits de mer", "time": "déjeuner", "event": "declaration" }, { "name": "persil", "quantity": "", "weight": "", "cooking": "", "brand": "", "company": "", "type": "herbe aromatique", "time": "déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'calamars', 'quantity': '', 'weight': '', 'cooking': 'sauté', 'brand': '', 'company': '', 'type': 'fruits de mer', 'time': 'déjeuner', 'event': 'declaration'}, {'name': 'persil', 'quantity': '', 'weight': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'herbe aromatique', 'time': 'déjeuner', 'event': 'declaration'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'calamars', 'quantity': '', 'weight': '', 'cooking': 'sauté', 'brand': '', 'company': '', 'type': 'fruits de mer', 'time': '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 '% calamar %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Calamars - calamar - - - 958 - - - KCA#ad965717d04a56afdec0b6c3772b1911
Calamars Frits - calamar frit - - - 711 - - - KCA#6da42c48ea5ae366396e8e6e9a603a4d
Calamars Farcis - calamar farci - à la feta et au piment - - 42 - - - KCA#7bc05b2dab85abe6c36f381315d811d3
Calamars Farcis - calamar farci - à la Truite fumée et au Basilic - - 16 - - - KCA#ce1b02fd0277bd7c1be0f06e2891623f
Calamars au Piment - calamar piment - et salade à la Menthe et au Soja - - 67 - - - KCA#5ff0376d2a07cee1ab1c0f8bf16fb66c
Sushi Calamar - sushi calamar - sushi calamar - - 0 - - - KCA#2f153aab490c0dd5b7247f243b8fa91b
Calmar ou Calamar ou Encornet - calmar ou calamar ou encornet - bouilli/cuit à l'eau - - 0 - - - CIQ#63c9f1e1025048fb239cb091c4bdd27d
Calmar ou Calamar ou Encornet - calmar ou calamar ou encornet - à la romaine, beignet - - 0 - - - CIQ#c4d5067f0740f5215b0be263635a181b
Calmar ou Calamar ou Encornet - calmar ou calamar ou encornet - frit ou poêlé avec matière grasse - - 0 - - - CIQ#80f367681df6cd28e5e8a490accaf9e3
Salade Tiède aux Calamars Farcis et aux Tomates - salade tiede au calamar farci au tomate - - - 21 - - - KCA#98e2df0580d33ecbaa8780f5fe4e0d95
----------------------------------------------------
ERROR: Wrong quantity: ''
ERROR: no solution for picto in the first solution
----------- result to be analyzed -----------
{'name': 'persil', 'quantity': '', 'weight': '', 'cooking': '', 'brand': '', 'company': '', 'type': 'herbe aromatique', 'time': '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 '% persil %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
------------- Found solution (max 20) --------------
Persil - persil - frais - - 264 - - - CIQ#ec2a412afc8ab4d24534b5d56978d9fc
Persil - persil - séché - - 0 - - - CIQ#96195436809abdbaadb6b847f330fbea
Persil Frais - persil frai - - - 0 - - - KCA#7290c8b50f37041789c140f8b2b14147
Potage au Persil - potage persil - - - 24 - - - KCA#83508b75925c5e37dacb218e5f75fa0b
Steaks Sauce Persil - steak sauce persil - sauce persil - - 0 - - - KCA#6524592e1b23ceffe76c9e0ff784370d
Cuisses de Grenouilles au Persil et au Citron - cuisse de grenouille persil citron - - - 53 - - - KCA#157469cef0a2f47698d66ffdddb40fb7
----------------------------------------------------
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': 'Calamars', 'normName': ' calamar ', 'comment': '', 'normComment': '', 'rank': 958, 'id': 'KCA#ad965717d04a56afdec0b6c3772b1911', 'quantity': '', 'quantityLem': '', 'pack': ['POI.w150'], 'type': 'fruits de mer', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}, {'name': 'Persil', 'normName': ' persil ', 'comment': 'frais', 'normComment': ' frai ', 'rank': 264, 'id': 'CIQ#ec2a412afc8ab4d24534b5d56978d9fc', 'quantity': '', 'quantityLem': '', 'pack': ['XXX.w20'], 'type': 'herbe aromatique', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': 'déjeuner', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 4.235730409622192}
----------------------------------------------------------------------------------
LLM CPU Time: 4.235730409622192