Input path: /home/debian/html/nutritwin/output_llm/661bf048a9bd8/input.json
Output path: /home/debian/html/nutritwin/output_llm/661bf048a9bd8/output.json
Input text: Ce matin j'ai mangé une orange
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: Ce matin j'ai mangé une orange
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###Ce matin j'ai mangé une orange###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
{
"intents": ["Capture the user food consumption"]
}
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
{
"intents": ["Capture the user food consumption"]
}
------------------------------------------------------
------------------------ After simplification ------------------------
{"intents": ["Capture the user food consumption"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "Ce matin j'ai mangé une orange".
I want to identify for the food or beverage: the name, the type, the quantity for each ingredient and, if it exists, identify the brand, the cooking mode and the company name.
Containers, like "canette" or "verre", are quantities and not ingredients or food product.
"Portions", like "tranche", are quantities.
"Quantity" is in french.
"Company" is the company of the brand.
"Quignon" is a quantity.
Ignore what it is not connected to nutrition, beverage or food.
Music and is not nutrition.
Extract how the product is consumed.
In the name, ignore the level of cooking mode.
When brand is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "".
Ignore the actions.
The restaurants are not brand.
Identify what type of food.
Ignore food with a negative verb, ex "Je n'ai pas pris de viande".
Do not extract ingredients for product with a brand.
If the food or beverage consumption is in the past, the event is a "declaration", for example: "J'ai mangé du pain", the event is a declaration.
If the food or beverage consumption will be in the future or even soon, the event is an "intent", for example: "Je vais manger du pain", the event is an intent.
Otherwise the event is unknown.
Map the event of eating on ["intent", "declaration", "unknown"].
Identify the time of day when the foods were eaten and map it on "petit-déjeuner", "déjeuner", "grignotage" or "dîner".
Format the result in french in JSON in an array of tuples {"name":, "quantity":, "cooking":, "brand":, "company":, "type":, "time":, "event":}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai mangé une orange".
The food information can be represented in JSON format as follows:
```json
[
{
"name": "une orange",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
Explanation:
- The name of the food is "une orange" (an orange).
- The quantity is "une" (one).
- The cooking mode is not specified in the sentence.
- The brand is not specified and "orange" is not a brand but a type of fruit.
- The company is not specified.
- The type of food is a fruit.
- The time of day when the food was eaten is "ce matin" which corresponds to "petit-déjeuner" (breakfast).
- The event is a "declaration" because the food consumption is in the past.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "Ce matin j'ai mangé une orange".
The food information can be represented in JSON format as follows:
```json
[
{
"name": "une orange",
"quantity": "une",
"cooking": "",
"brand": "",
"company": "",
"type": "fruit",
"time": "petit-déjeuner",
"event": "declaration"
}
]
```
Explanation:
- The name of the food is "une orange" (an orange).
- The quantity is "une" (one).
- The cooking mode is not specified in the sentence.
- The brand is not specified and "orange" is not a brand but a type of fruit.
- The company is not specified.
- The type of food is a fruit.
- The time of day when the food was eaten is "ce matin" which corresponds to "petit-déjeuner" (breakfast).
- The event is a "declaration" because the food consumption is in the past.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "une orange", "quantity": "une", "cooking": "", "brand": "", "company": "", "type": "fruit", "time": "petit-déjeuner", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'une orange', 'quantity': 'une', 'cooking': '', 'brand': '', 'company': '', 'type': 'fruit', 'time': 'petit-déjeuner', 'event': 'declaration'}], 'cost': 0.06197999999999999}
--------------------------------------------------------------------------------
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 '% une orange %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
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 '% une orange %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
------ERROR--------------------------------
No solution for query: 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 '% une orange %' AND V_NormTrademark LIKE '%%'
-------------------------------------------
-------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "Ce matin j'ai mangé une orange", 'intents': ['Capture the user food consumption'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': [], 'response': ''}, 'cputime': 10.722252607345581}
----------------------------------------------------------------------------------
LLM CPU Time: 10.722252607345581