Input path: /home/debian/html/nutritwin/output_llm/691db172550c6/input.json Output path: /home/debian/html/nutritwin/output_llm/691db172550c6/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/691db172550c6/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": "chocolat", "quantity": "29g", "type": "food", "event": "declaration", "brand": "Milka", "company": "Mondelez" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "chocolat", "quantity": "29g", "type": "food", "event": "declaration", "brand": "Milka", "company": "Mondelez" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "chocolat", "quantity": "29g", "type": "food", "event": "declaration", "brand": "Milka", "company": "Mondelez" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'chocolat', 'quantity': '29g', 'type': 'food', 'event': 'declaration', 'brand': 'Milka', 'company': 'Mondelez'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'chocolat', 'quantity': '29g', 'type': 'food', 'event': 'declaration', 'brand': 'Milka', 'company': 'Mondelez'} 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 '% chocolat %' AND V_NormTrademark LIKE '%milka%' --> CPU time in DB: 0.1375 seconds Word: Chocolat - dist: 0.4263405203819275 - row: 3609 Word: Le Chocolat - dist: 0.4895692765712738 - row: 16413 Word: Chocolats - dist: 0.5121781229972839 - row: 51416 Word: Chocolat Dessert - dist: 0.5590576529502869 - row: 40816 Word: Chocolat Cake - dist: 0.5633316040039062 - row: 53198 Found embedding word: Chocolat 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 = 'Chocolat' ------------- Found solution (max 20) -------------- Chocolat - chocolat - - Lidl - 18 - 20807061 - 20807061 - OFF#44788ca73c28bc9fb1e68a9f21bf19fa Chocolat - chocolat - - Lindt - 4 - 3046920011440 - 3046920011440 - OFF#adcf6c655a866c4f398672e00c7a9a9e Chocolat - chocolat - - Mondelez International - 0 - 3045140105564 - 3045140105564 - OFF#107009c3e1aed6a7bc1b1a76685a2ad1 Chocolat - chocolat - - Casino - 0 - 3222476423931 - 3222476423931 - OFF#77f8e34e5d312fbefabf30f8527b78e7 Chocolat - chocolat - - U - 0 - 3256225738397 - 3256225738397 - OFF#88e87a75247d9b995e30139064d66ceb Chocolat - chocolat - - Cora - 0 - 3257980632005 - 3257980632005 - OFF#b683a87202e86470b337c6847d980d65 Chocolat - chocolat - - Moulin des Moines - 0 - 3347437001369 - 3347437001369 - OFF#b380ef62b824fe6f46b0b6d4de20c88e Chocolat - chocolat - - Leclerc - 0 - 3450970001274 - 3450970001274 - OFF#ceed89e021c7e186a8ca67c01f3a8b56 Chocolat - chocolat - - Auchan - 0 - 3596710347339 - 3596710347339 - OFF#3adb22e03d28bd6a36539ff6dde8ae32 Chocolat - chocolat - - Suchard - 0 - 3664346309202 - 3664346309202 - OFF#54bd603da7c4a394eac0c4b75ca1b11d Chocolat - chocolat - - Poulain - 0 - 3664346317115 - 3664346317115 - OFF#748cd12f9002f7d4846c058ff5814821 Chocolat - chocolat - - Leonidas - 0 - 5420006320325 - 5420006320325 - OFF#1977195fbf0f949a7f4eb49f1104da8c Chocolat - chocolat - - Lidl - 0 - 4056489182788 - 20807061 - OFF#92950981ae0a1b0a7de12ac1dc9c1d4e Chocolat - chocolat - - Mondelez International - 0 - 7622201766559 - 3045140105564 - OFF#23312519ffb2ca2c4ef12ae2c0172655 Chocolat - chocolat - - Lindt - 0 - 7610400087551 - 3046920011440 - OFF#231409b2a8ceffbce4b568249fd109d0 Chocolat - chocolat - - Lindt - 0 - 3770001938059 - 3046920011440 - OFF#505021980f274f1107ca29d91ddaf2d5 Chocolat - chocolat - - Lindt - 0 - 3046920126632 - 3046920011440 - OFF#93739e1dc2989da5fe2ae8c1e70009eb Chocolat - chocolat - - Lindt - 0 - 3046920071239 - 3046920011440 - OFF#1de2c46d1004d4d41158dc166bbedd5d Chocolat - chocolat - - Lindt - 0 - 3046920025003 - 3046920011440 - OFF#98eb2be017985fff59c7d49a6c8cb601 Chocolat - chocolat - - Lindt - 0 - 8712417101141 - 3046920011440 - OFF#99c2e847e2ae08082af91e1d8d0b53e3 ---------------------------------------------------- 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 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 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/691db172550c6/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Chocolat', 'normName': ' chocolat ', 'comment': '', 'normComment': '', 'rank': 18, 'id': 'OFF#44788ca73c28bc9fb1e68a9f21bf19fa', 'quantity': '29g', 'quantityLem': '29g', 'pack': ['UN2.w21'], 'type': 'food', 'gtin': '20807061', 'gtinRef': '20807061', 'brand': 'Lidl', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 5.326793432235718} ---------------------------------------------------------------------------------- LLM CPU Time: 5.326793432235718