Input path: /home/debian/html/nutritwin/output_llm/67e0f49133997/input.json Output path: /home/debian/html/nutritwin/output_llm/67e0f49133997/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/67e0f49133997/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": "confiture de framboises", "quantity": "un pot", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "confiture de framboises", "quantity": "un pot", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "confiture de framboises", "quantity": "un pot", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'confiture de framboises', 'quantity': 'un pot', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'confiture de framboises', 'quantity': 'un pot', 'type': 'food', 'event': 'declaration'} 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 '% confiture de framboise %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1275 seconds Word: Confiture de Framboises - dist: 0.3533908724784851 - row: 7913 Word: Confiture de Framboise - dist: 0.3857310116291046 - row: 4251 Word: Confiture Framboises - dist: 0.3887827396392822 - row: 5730 Word: Confiture de Fraises et Framboises - dist: 0.41858309507369995 - row: 50737 Word: Confiture Framboise - dist: 0.42678317427635193 - row: 5420 Found embedding word: Confiture de Framboises 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 = 'Confiture de Framboises' ------------- Found solution (max 20) -------------- Confiture de Framboises - confiture de framboise - - Bonne Maman - 0 - 3045320009385 - 3045320009385 - OFF#458401a13bf50c27dc373e1a8681d770 Confiture de Framboises - confiture de framboise - - Intermarché - 0 - 3250390138631 - 3250390138631 - OFF#0abda881ec06d28319b373b77c64bc91 Confiture de Framboises - confiture de framboise - - Coteaux Nantais - 0 - 3301595002156 - 3301595002156 - OFF#1b3b6c639458f69fc813cfaf8b4aab37 Confiture de Framboises - confiture de framboise - - Leclerc - 0 - 3450970176842 - 3450970176842 - OFF#6ac6d877732ae588a565381234cef898 Confiture de Framboises - confiture de framboise - - Carrefour - 0 - 3560070701971 - 3560070701971 - OFF#681aa24aec5cc3c360498c35c14859f2 Confiture de Framboises - confiture de framboise - - Auchan - 0 - 3596710129904 - 3596710129904 - OFF#e67d652575d93c21e22edd6cb7307ec6 Confiture de Framboises - confiture de framboise - - Delhaize - 0 - 5400111087887 - 5400111087887 - OFF#3f86e2a3bc9c95b906115d9a98a57047 Confiture de Framboises - confiture de framboise - - U - 0 - 3256224759492 - 3256220497121 - OFF#c5e19488fc9c40a38568d69a1ffc42e9 Confiture de Framboises - confiture de framboise - - Coteaux Nantais - 0 - 64859859 - 3301595002156 - OFF#b64391fc372ce8972266b4349a6e8b0f Confiture de Framboises - confiture de framboise - - Coteaux Nantais - 0 - 3301595003054 - 3301595002156 - OFF#6a66717509d81d95cac10b692c3da5fa Confiture de Framboises - confiture de framboise - - Leclerc - 0 - 3564700554555 - 3450970176842 - OFF#faacf2ffe707a855593446409cea3f35 ---------------------------------------------------- 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/67e0f49133997/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Confiture de Framboises', 'normName': ' confiture de framboise ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#458401a13bf50c27dc373e1a8681d770', 'quantity': 'un pot', 'quantityLem': '1 pot', 'pack': ['CCL.w10'], 'type': 'food', 'gtin': '3045320009385', 'gtinRef': '3045320009385', 'brand': 'Bonne Maman', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.0875086784362793} ---------------------------------------------------------------------------------- LLM CPU Time: 3.0875086784362793