Input path: /home/debian/html/nutritwin/output_llm/680fb9ca04978/input.json Output path: /home/debian/html/nutritwin/output_llm/680fb9ca04978/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/680fb9ca04978/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": "fromage", "quantity": "un", "type": "food", "brand": "St Môret", "company": "Savencia Fromage & Dairy", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "fromage", "quantity": "un", "type": "food", "brand": "St Môret", "company": "Savencia Fromage & Dairy", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "fromage", "quantity": "un", "type": "food", "brand": "St M\u00f4ret", "company": "Savencia Fromage & Dairy", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'fromage', 'quantity': 'un', 'type': 'food', 'brand': 'St Môret', 'company': 'Savencia Fromage & Dairy', 'event': 'unknownEvent'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'fromage', 'quantity': 'un', 'type': 'food', 'brand': 'St Môret', 'company': 'Savencia Fromage & Dairy', '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 '% fromage %' AND V_NormTrademark LIKE '%st moret%' --> CPU time in DB: 0.1225 seconds Word: Fromage - dist: 0.32769086956977844 - row: 4143 Word: Sauce Fromage - dist: 0.5250937342643738 - row: 63091 Word: Fromage BIO - dist: 0.5329740047454834 - row: 18140 Word: 3d Fromage - dist: 0.5444847345352173 - row: 2347 Word: 4 Fromage - dist: 0.54536372423172 - row: 38952 Found embedding word: Fromage 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 = 'Fromage' ------------- Found solution (max 20) -------------- Fromage - fromage - - - 23096 - - - KCA#e2646df35885ba5fc75c406a551c9fbc Fromage - fromage - - Compagnie des fromages & RichesMonts - 0 - 0285515029580 - 0285515029580 - OFF#46d8215acc28c47b0b07d013148adf5c Fromage - fromage - - group Bel - 0 - 13788719 - 13788719 - OFF#b2594a09c910044e5afb9bff5d179964 Fromage - fromage - - Lidl - 0 - 20816285 - 20816285 - OFF#e19601d0fe8de107dc399a3c7c0572e4 Fromage - fromage - - Entremont - 0 - 3123937110141 - 3123937110141 - OFF#ebd97b208d9ef6da79138132ae0d9586 Fromage - fromage - - Auchan - 0 - 3176580822701 - 3176580822701 - OFF#4e2087d99cdb9e3460dced4dff702f19 Fromage - fromage - - Président - 0 - 3228022990216 - 3228022990216 - OFF#0e46a30f585bf8af795f8792a5fd84bf Fromage - fromage - - Leclerc - 0 - 3564709172491 - 3564709172491 - OFF#5523dd34d3aebf5a9583035d1994ce8e Fromage - fromage - - Lay's - 0 - 5900259099396 - 5900259099396 - OFF#e49e3167e2e23b0f986b2ffe37d3322e Fromage - fromage - - Compagnie des fromages & RichesMonts - 0 - 3090291386058 - 0285515029580 - OFF#59f31024519d353aa478f7ea65e795a6 Fromage - fromage - - Compagnie des fromages & RichesMonts - 0 - 5410942172416 - 0285515029580 - OFF#c623327be90dc52992f13b846414483f Fromage - fromage - - group Bel - 0 - 8721800403182 - 13788719 - OFF#805d86796003e7ce112d6115175babe6 Fromage - fromage - - group Bel - 0 - 3073781195972 - 13788719 - OFF#ad6c326b13a2e87bfe97f5e2bac26040 Fromage - fromage - - group Bel - 0 - 3073781194661 - 13788719 - OFF#c4010858cc1a17f00cdfb3ceca0df51f Fromage - fromage - - group Bel - 0 - 3073781143379 - 13788719 - OFF#2251a80925c24317df2d831aa3a6e02b Fromage - fromage - - group Bel - 0 - 3073780830843 - 13788719 - OFF#1bf26c3653f0da6a25c67e352f9e72da Fromage - fromage - - group Bel - 0 - 8341800402178 - 13788719 - OFF#7ce1b7a81887b0b7bd6294ecc1bfe751 ---------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/680fb9ca04978/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Fromage', 'normName': ' fromage ', 'comment': '', 'normComment': '', 'rank': 23096, 'id': 'KCA#e2646df35885ba5fc75c406a551c9fbc', 'quantity': 'un', 'quantityLem': '1', 'pack': ['CAM.w20', 'GRU.w20', 'MIM.w20', 'ROC.w20', 'CH2.w20'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'unknownEvent', 'serving': 'CAM-800', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.817493438720703} ---------------------------------------------------------------------------------- LLM CPU Time: 2.817493438720703