Input path: /home/debian/html/nutritwin/output_llm/6794a12a1a3e4/input.json Output path: /home/debian/html/nutritwin/output_llm/6794a12a1a3e4/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/6794a12a1a3e4/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": "pain aux graines", "quantity": "deux tranches", "type": "food", "event": "declaration" }, { "name": "fromage", "quantity": "une tranche", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "pain aux graines", "quantity": "deux tranches", "type": "food", "event": "declaration" }, { "name": "fromage", "quantity": "une tranche", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "pain aux graines", "quantity": "deux tranches", "type": "food", "event": "declaration" }, { "name": "fromage", "quantity": "une tranche", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'pain aux graines', 'quantity': 'deux tranches', 'type': 'food', 'event': 'declaration'}, {'name': 'fromage', 'quantity': 'une tranche', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'pain aux graines', 'quantity': 'deux tranches', '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 '% pain au graine %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1118 seconds Word: Pain Complet aux Graines - dist: 0.5416164994239807 - row: 60576 Word: Pain aux 4 Graines - dist: 0.5656818747520447 - row: 15630 Word: Pain aux Graines sans Gluten - dist: 0.5849098563194275 - row: 56335 Word: Pain aux Graines de Lin - dist: 0.5932800769805908 - row: 27679 Word: Pain de Campagne aux Graines - dist: 0.5978596806526184 - row: 20308 Found embedding word: Pain Complet aux Graines 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 = 'Pain Complet aux Graines' ------------- Found solution (max 20) -------------- Pain Complet aux Graines - pain complet au graine - - Coop - 0 - 7624841876605 - 7624841876605 - OFF#c9e750984b0cfc729ba83a660d6704a2 ---------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'fromage', 'quantity': 'une tranche', '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 '% fromage %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Fromage - fromage - - - 23096 - - - KCA#e2646df35885ba5fc75c406a551c9fbc Fromage 45% MG - fromage 45% mg - - - 6874 - - - KCA#14ed2b0745972d44df97c5d52a44ac69 Fromage 20% MG - fromage 20% mg - - - 1124 - - - KCA#e32d6c98bf1d5f0a3c853a8f6bb7c3b3 Fromage 70% MG - fromage 70% mg - - - 494 - - - KCA#351b50fec02ae7c43d964985ac9086c6 Fromage de Tête - fromage de tete - - - 258 - - - CIQ#a80997979cdf84066ed5ed98f0291aef Fromage de Chèvre - fromage de chevre - - - 4537 - - - KCA#87a40b8f006dcb11aafd4e97014ed3f4 Fromage de Brebis - fromage de brebi - pâte pressée - - 0 - - - KCA#58787aec327646598cc7785b49eea77a Fromage de Brebis - fromage de brebi - pâte molle à croûte fleurie - - 0 - - - KCA#a463c1fc485a4f9d296ce6817ce2c361 Fromage de Chèvre - fromage de chevre - lactique affiné, au lait cru type Crottin - - 250 - - - KCA#2c01ba493c1fac82cabb393f8f3648a7 Fromage Frais 0% MG - fromage frai 0% mg - - - 519 - - - KCA#88f1992eded597fa4d19465f74683774 Fromage Fondu 25% MG - fromage fondu 25% mg - - - 3246 - - - KCA#d149670a9548a1b193a2c41eca41b75f Fromage Frais 30% MG - fromage frai 30% mg - - - 145 - - - KCA#7925728898a08e85f13745b60bc71320 Fromage Fondu 45% MG - fromage fondu 45% mg - - - 95 - - - KCA#6d8e1e183c61d211654c306cf3835256 Fromage Frais 20% MG - fromage frai 20% mg - - - 81 - - - KCA#0c277d2e26315ef0b610a1ac6f0b2c8f Fromage Fondu 70% MG - fromage fondu 70% mg - - - 55 - - - KCA#4310db392dfdcff70718326fee922034 Fromage Fondu 65% MG - fromage fondu 65% mg - - - 52 - - - KCA#df8a055eb661bce01be58e63581e3ace Fromage Blanc Nature - fromage blanc nature - 0% MG - - 24178 - - - CIQ#36c17f9437be97fba469ea7cd5441d75 Fromage Blanc Nature - fromage blanc nature - 3% MG environ - - 10606 - - - CIQ#4a1c07f162d63ff83801c1fb767aafcf Fromage Blanc Nature - fromage blanc nature - gourmand, 8% MG environ - - 0 - - - CIQ#4ec95c0d5d5444677063a6486af1e1c9 Fromage Fondu aux Noix - fromage fondu au noix - - - 23 - - - KCA#849bff96c14abb755613ff11508fe7c9 ---------------------------------------------------- ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/6794a12a1a3e4/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Pain Complet aux Graines', 'normName': ' pain complet au graine ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#c9e750984b0cfc729ba83a660d6704a2', 'quantity': 'deux tranches', 'quantityLem': '2 tranche', 'pack': ['TPA.w50'], 'type': 'food', 'gtin': '7624841876605', 'gtinRef': '7624841876605', 'brand': 'Coop', 'time': '', 'event': 'declaration', 'serving': 'TPA-200', 'posiNormName': -1}, {'name': 'Fromage', 'normName': ' fromage ', 'comment': '', 'normComment': '', 'rank': 23096, 'id': 'KCA#e2646df35885ba5fc75c406a551c9fbc', 'quantity': 'une tranche', 'quantityLem': '1 tranche', 'pack': ['CAM.w20', 'GRU.w20', 'MIM.w20', 'ROC.w20', 'CH2.w20'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 3.925590753555298} ---------------------------------------------------------------------------------- LLM CPU Time: 3.925590753555298