Input path: /home/debian/html/nutritwin/output_llm/660cea25908a3/input.json
Output path: /home/debian/html/nutritwin/output_llm/660cea25908a3/output.json
Input text: J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté
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: J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Capture the user food consumption", "Capture the user physical activity", "Other intent"], the intents of the prompt: ###J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
{
"intents": ["Capture the user food consumption", "Capture the user physical activity"]
}
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
{
"intents": ["Capture the user food consumption", "Capture the user physical activity"]
}
------------------------------------------------------
------------------------ After simplification ------------------------
{"intents": ["Capture the user food consumption", "Capture the user physical activity"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
I need to identify food information from sentences.
Analyze the following french sentence: "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
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 "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
The food information extracted from the sentence is as follows:
```json
[
{
"name": "Vache Qui Rit",
"quantity": "deux portions",
"cooking": "",
"brand": "La vache qui rit",
"company": "Bel Group",
"type": "fromage fondu",
"time": "unknown",
"event": "declaration"
}
]
```
Explanation:
- The food product is "Vache Qui Rit", which is a well-known brand of processed cheese.
- The quantity is "deux portions" (two portions).
- The cooking mode is not specified in the sentence.
- The brand is "La vache qui rit", which is the name of the product itself.
- The company that owns the brand is "Bel Group".
- The type of food is "fromage fondu" (processed cheese).
- The time of day when the food was eaten is not specified in the sentence, so it's marked as "unknown".
- The event is a "declaration" because the sentence is stating that the person ate the food in the past.
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, here's the analysis of the sentence "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
The food information extracted from the sentence is as follows:
```json
[
{
"name": "Vache Qui Rit",
"quantity": "deux portions",
"cooking": "",
"brand": "La vache qui rit",
"company": "Bel Group",
"type": "fromage fondu",
"time": "unknown",
"event": "declaration"
}
]
```
Explanation:
- The food product is "Vache Qui Rit", which is a well-known brand of processed cheese.
- The quantity is "deux portions" (two portions).
- The cooking mode is not specified in the sentence.
- The brand is "La vache qui rit", which is the name of the product itself.
- The company that owns the brand is "Bel Group".
- The type of food is "fromage fondu" (processed cheese).
- The time of day when the food was eaten is not specified in the sentence, so it's marked as "unknown".
- The event is a "declaration" because the sentence is stating that the person ate the food in the past.
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Vache Qui Rit", "quantity": "deux portions", "cooking": "", "brand": "La vache qui rit", "company": "Bel Group", "type": "fromage fondu", "time": "unknown", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Vache Qui Rit', 'quantity': 'deux portions', 'cooking': '', 'brand': 'La vache qui rit', 'company': 'Bel Group', 'type': 'fromage fondu', 'time': 'unknown', 'event': 'declaration'}], 'cost': 0.07044}
--------------------------------------------------------------------------------
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 '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%'
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 '% vache qui rit %' AND V_NormTrademark LIKE '%la vache qui rit%'
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 '% vache qui rit %' AND V_NormAggr LIKE '% la vache qui rit %' AND V_NormAggr LIKE '% bel group %'
-------------------------------------------
------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 '% vache qui rit %' AND V_NormAggr LIKE '% la vache qui rit %' AND V_NormAggr LIKE '% bel group %'
-------------------------------------------
-------------------------------------------
==================================== Prompt =============================================
I need to identify physical activities information from sentences.
Analyze the following french sentence: "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
Identify all sports or physical activities, its current name and its duration if it exists.
If possible, map the name on one of the following activities in french: ["Basket-ball", "Cyclisme", "Boxe", "Danse", "Zumba", "Plong\u00e9e sous-marine", "Escrime", "Football", "Jardinage", "Golf", "Gymnastique", "Judo", "Karat\u00e9", "Corde \u00e0 sauter", "Rameur d'appartement", "Course \u00e0 pied", "Faire l'amour", "Ski de descente", "Natation", "Tennis", "Tapis de course", "A\u00e9robic", "Ski de fond", "danse", "V\u00e9lo d'appartement", "Aquabiking", "V\u00e9lo elliptique", "Exercice physique", "Equitation", "sports", "Escalade", "sports", "Patins en ligne", "sports", "Tennis de table", "sports", "Marche", "Activit\u00e9s nautiques et aquatiques", "Marche avec un chien", "T\u00e2ches m\u00e9nag\u00e8res", "Marche nordique"].
Ignore what it is not mapped to sport or physical activities.
"Ménage" is similar to "Nettoyage".
Cooking or eating are not a sport or a physical activities.
Define "intensity" of the sport. Map for "intensity" on one of these values: ["unknown", "low", "moderate", "high", "intense", "very intense"].
Duration must be a number.
Duration must be in minute.
If a physical activity will occur in the future, the event is an "intent".
If a physical activity has occured in the past, the event is a "declaration".
Map this event of the physical activity on ["intent", "declaration", "unknown"].
Ignore the negative action .
If the duration is unknown, set duration to 0.
Format the result for sport in french in JSON in an array of tuples {"name":, "duration":, "intensity":, "event":}.
When there is no solution, the result is: []
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Based on your instructions, I have analyzed the sentence "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
The physical activity identified is "running" which can be mapped to "Course à pied" in the provided list. The duration of the activity is half an hour, which is 30 minutes. The intensity of running can vary, but for this analysis, I will consider it as "moderate". The event is a "declaration" as it has occurred in the past.
Here is the result formatted in JSON:
```json
[
{
"name": "Course à pied",
"duration": 30,
"intensity": "moderate",
"event": "declaration"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
Based on your instructions, I have analyzed the sentence "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté".
The physical activity identified is "running" which can be mapped to "Course à pied" in the provided list. The duration of the activity is half an hour, which is 30 minutes. The intensity of running can vary, but for this analysis, I will consider it as "moderate". The event is a "declaration" as it has occurred in the past.
Here is the result formatted in JSON:
```json
[
{
"name": "Course à pied",
"duration": 30,
"intensity": "moderate",
"event": "declaration"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "Course à pied", "duration": 30, "intensity": "moderate", "event": "declaration" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'Course à pied', 'duration': 30, 'intensity': 'moderate', 'event': 'declaration'}], 'cost': 0.062279999999999995}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': "J'ai mangé deux portions de Vache Qui Rit et je suis allé courir une demi-heure dans le parc d'à côté", 'intents': ['Capture the user food consumption', 'Capture the user physical activity'], 'model': 'mistral-large-latest', 'solutions': {'nutrition': [], 'activity': [{'trigram': 'RUN', 'duration': 30, 'event': 'declaration', 'level': 'RUN06'}]}, 'cputime': 13.861944913864136}
----------------------------------------------------------------------------------
LLM CPU Time: 13.861944913864136