Input path: /home/debian/html/nutritwin/output_llm/68e392032db62/input.json Output path: /home/debian/html/nutritwin/output_llm/68e392032db62/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: ================================================================================================================================== Image to be analyzed: /home/debian/html/nutritwin/output_llm/68e392032db62/capture.jpg ############################################################################################## # For image extraction, pixtral-large-2411 is used # ############################################################################################## ==================================== Prompt ============================================= In the image, identify all the foods and beverages, convert them into an array of JSON with consumed foods. Ignore what it is not connected to nutrition, beverage or food. When a food or a beverage has several instances unify them on a single food or beverage and add the quantities of each. The attribute name must remain in English but the result, so the attribute value, must be in french, and only in french. Provide a solution without explanation. Use only the food & beverage ontology described in this Turtle/RDF model: """ @prefix food: . @prefix rdfs: . @prefix xsd: . @prefix owl: . @prefix prov: . food: a owl:Ontology ; rdfs:comment "Definition of the food archetype"@en . food:name a owl:DatatypeProperty; rdfs:label "name"@en; rdfs:comment """Food or beverage identifier, the name should not contain information related to quantity or container (like glass...). Ignore food or beverage when it is not consumed in the past, now or in the future. The cooking mode is not in the name. The name is only in french."""@en; rdfs:range xsd:string. food:quantity a owl:DatatypeProperty ; rdfs:label "quantity"@en; rdfs:comment "The quantity of food or drink that is or was consumed. Quantity is only in french. Here are examples: 'un quignon', 'un cornet', 'un verre', 'une tranche', 'une boule', 'un', 'deux', 'trois',... Keep the same language."@en; rdfs:range xsd:string. food:cookingMethod a owl:DatatypeProperty ; rdfs:label "cooking method"@en; rdfs:comment "The cooking method of food. The cooking method is in french."@en; rdfs:range xsd:string. food:type a owl:DatatypeProperty ; rdfs:label "type of food"@en; rdfs:comment "Identify the type of food."@en; rdfs:range xsd:string. food:food a food:type ; rdfs:label "food" . food:beverage a food:type ; rdfs:label "beverage" . food:timeOfTheDay a owl:DatatypeProperty ; rdfs:label "time of the day"@en; rdfs:comment "Time of the day when food or drink was consumed."@en; rdfs:range xsd:string. food:breakfast a food:timeOfTheDay ; rdfs:label "breakfast" . food:lunch a food:timeOfTheDay ; rdfs:label "lunch" . food:snacking a food:timeOfTheDay ; rdfs:label "snacking" . food:dinner a food:timeOfTheDay ; rdfs:label "dinner" . food:brand a owl:DatatypeProperty ; rdfs:label "Brand"@en; rdfs:comment """Food or beverage brand. The restaurants are not brand. When the name is very known (ex: Activia, Coca) and the brand is not mentioned, guess the brand."""@en; rdfs:range xsd:string. food:company a owl:DatatypeProperty ; rdfs:label "Company"@en; rdfs:comment "Product company."@en; rdfs:range xsd:string. food:enumEvent a rdfs:Class . food:event a owl:DatatypeProperty ; rdfs:label "event"@en; rdfs:comment "Event of eating or drinking. Each must have an event"@en; rdfs:range food:enumEvent. food:intent a food:enumEvent ; rdfs:label "intent" . rdfs:comment "When the event should happen"@en. food:declaration a food:enumEvent ; rdfs:label "declaration" . rdfs:comment "When the event has already occured"@en. food:unknownEvent a food:enumEvent ; rdfs:label "unknown" ; rdfs:comment "When the event is unknown in the day"@en. food:event a owl:DatatypeProperty ; rdfs:label "event"@en; rdfs:comment "Event of eating or drinking. Each must have an event"@en; rdfs:range food:enumEvent. food:intent a food:enumEvent ; rdfs:label "intent" . rdfs:comment "When the event should happen"@en. food:declaration a food:enumEvent ; rdfs:label "declaration" . rdfs:comment "When the event has already occured"@en. food:unknownEvent a food:enumEvent ; rdfs:label "unknown" ; rdfs:comment "When the event is unknown in the day"@en. """ Here is an example of result: [ { "name": "blanquette de veau", "quantity": "un plat", "cookingMethod": "mijot\u00e9", "timeOfTheDay": "lunch", "company": "Leclerc", "type": "food", "event": "declaration" }, { "name": "eau", "brand": "Evian", "company": "Danone", "timeOfTheDay": "breakfast", "quantity": "un verre", "type": "beverage", "event": "intent" } ] ========================================================================================= ------------------------------ LLM Raw response ----------------------------- [ { "name": "beurre", "quantity": "un", "type": "food", "brand": "Elle & Vire", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "beurre", "quantity": "un", "type": "food", "brand": "Elle & Vire", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "beurre", "quantity": "un", "type": "food", "brand": "Elle & Vire", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'beurre', 'quantity': 'un', 'type': 'food', 'brand': 'Elle & Vire', 'event': 'unknownEvent'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'beurre', 'quantity': 'un', 'type': 'food', 'brand': 'Elle & Vire', 'event': 'unknownEvent'} 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 '% beurre %' AND V_NormTrademark LIKE '%elle vire%' --> CPU time in DB: 0.1249 seconds Word: Beurre - dist: 0.28680482506752014 - row: 6167 Word: Beurre Tendre - dist: 0.5099045634269714 - row: 5010 Word: BIO Beurre - dist: 0.5238842964172363 - row: 58088 Word: Beurre Doux - dist: 0.524537980556488 - row: 3865 Word: Beurrés Bretons Pur Beurre - dist: 0.5298148989677429 - row: 26721 Found embedding word: Beurre Second try (embedded): 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_Name = 'Beurre' ------------- Found solution (max 20) -------------- Beurre - beurre - - Marks & Spencer - 0 - 00342506 - 00342506 - OFF#f730e1d2e1afbf6a45ed94e905017d19 Beurre - beurre - - Président - 0 - 3155251205524 - 3155251205524 - OFF#d7d5b3d5e98409881862e5b43bf2524d Beurre - beurre - - Lactalis - 0 - 3155251210740 - 3155251210740 - OFF#197fafc440384174f6450e60d361efd7 Beurre - beurre - - Compagnie des fromages & RichesMonts - 0 - 31601269 - 31601269 - OFF#e906477c2095cce6118afdbbe0a49cf4 Beurre - beurre - - Carrefour - 0 - 3560071264079 - 3560071264079 - OFF#275402a0809cf74b9e690c6131656274 Beurre - beurre - - Auchan - 0 - 3596710469338 - 3596710469338 - OFF#937691e2f7b340a0bc8e7357416c63c3 Beurre - beurre - - Dia - 0 - 8480017010193 - 8480017010193 - OFF#73f3f893c4eac455b5920fca9dd1b803 Beurre - beurre - - Président - 0 - 3155253020941 - 3155251205524 - OFF#c28acee25fbc4799cabc638a8c88aee9 Beurre - beurre - - Compagnie des fromages & RichesMonts - 0 - 3451790899324 - 31601269 - OFF#783ef5ee33643ba2761c6213793d5dec Beurre - beurre - - Compagnie des fromages & RichesMonts - 0 - 3250391895908 - 31601269 - OFF#f96fe1b935069be99178432c52bd3258 Beurre - beurre - - Compagnie des fromages & RichesMonts - 0 - 3451790016776 - 31601269 - OFF#2c68e9b60e9c15752dfbe8be17f77611 Beurre - beurre - - Compagnie des fromages & RichesMonts - 0 - 3451790937781 - 31601269 - OFF#b3015fecad286103893fd2e734b99679 ---------------------------------------------------- ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/68e392032db62/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Beurre', 'normName': ' beurre ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#f730e1d2e1afbf6a45ed94e905017d19', 'quantity': 'un', 'quantityLem': '1', 'pack': ['BEU.w15'], 'type': 'food', 'gtin': '00342506', 'gtinRef': '00342506', 'brand': 'Marks & Spencer', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.5546634197235107} ---------------------------------------------------------------------------------- LLM CPU Time: 2.5546634197235107