Input path: /home/debian/html/nutritwin/output_llm/67d064137a034/input.json Output path: /home/debian/html/nutritwin/output_llm/67d064137a034/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/67d064137a034/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": "barre super nuts", "quantity": "une barre", "type": "food", "event": "declaration", "timeOfTheDay": "snacking" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "barre super nuts", "quantity": "une barre", "type": "food", "event": "declaration", "timeOfTheDay": "snacking" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "barre super nuts", "quantity": "une barre", "type": "food", "event": "declaration", "timeOfTheDay": "snacking" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'barre super nuts', 'quantity': 'une barre', 'type': 'food', 'event': 'declaration', 'timeOfTheDay': 'snacking'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'barre super nuts', 'quantity': 'une barre', 'type': 'food', 'event': 'declaration', 'timeOfTheDay': 'snacking'} 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 '% barre super nut %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1233 seconds Word: Nuts Barre Chocolatée - dist: 0.7064765691757202 - row: 62728 Word: Nuts Mini Barre Chocolatée - dist: 0.7329452633857727 - row: 62731 Word: Barre Extra - dist: 0.7380948662757874 - row: 14144 Word: Barre Protein - dist: 0.739116370677948 - row: 54581 Word: Nut Bars - dist: 0.7427066564559937 - row: 59200 Found embedding word: Nuts Barre Chocolatée 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 = 'Nuts Barre Chocolatée' ------------- Found solution (max 20) -------------- Nuts Barre Chocolatée - nut barre chocolatee - - Nestlé - 0 - 8593893720442 - 8593893720442 - OFF#715ac87a09aae218dfac556f766c864c ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/67d064137a034/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Nuts Barre Chocolatée', 'normName': ' nut barre chocolatee ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#715ac87a09aae218dfac556f766c864c', 'quantity': 'une barre', 'quantityLem': '1 barre', 'pack': ['BAR.w33', 'BAR.w21'], 'type': 'food', 'gtin': '8593893720442', 'gtinRef': '8593893720442', 'brand': 'Nestlé', 'time': 'snacking', 'event': 'declaration', 'serving': 'BAR-100', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 3.770519495010376} ---------------------------------------------------------------------------------- LLM CPU Time: 3.770519495010376