Input path: /home/debian/html/nutritwin/output_llm/692ed1abe582c/input.json Output path: /home/debian/html/nutritwin/output_llm/692ed1abe582c/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/692ed1abe582c/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": "girasoli", "quantity": "deux personnes", "cookingMethod": "cuisson 6 minutes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "pesto cr\u000e\u000emeux", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "ricotta", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "basilic", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "girasoli", "quantity": "deux personnes", "cookingMethod": "cuisson 6 minutes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "pesto cr\u000e\u000emeux", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "ricotta", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "basilic", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "girasoli", "quantity": "deux personnes", "cookingMethod": "cuisson 6 minutes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "pesto cr\u000e\u000emeux", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "ricotta", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" }, { "name": "basilic", "quantity": "deux personnes", "type": "food", "brand": "Lustucru", "company": "Lustucru", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'girasoli', 'quantity': 'deux personnes', 'cookingMethod': 'cuisson 6 minutes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', 'event': 'unknownEvent'}, {'name': 'pesto cr\x0e\x0emeux', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', 'event': 'unknownEvent'}, {'name': 'ricotta', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', 'event': 'unknownEvent'}, {'name': 'basilic', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', 'event': 'unknownEvent'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'girasoli', 'quantity': 'deux personnes', 'cookingMethod': 'cuisson 6 minutes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', '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 '% girasoli %' AND V_NormTrademark LIKE '%lustucru%' --> CPU time in DB: 0.1243 seconds Word: Girasoli - dist: 0.30699893832206726 - row: 54895 Word: Girasoli Fromages Italiens - dist: 0.5524083971977234 - row: 22403 Word: Girasoli A la Burrata - dist: 0.5879443287849426 - row: 39106 Word: Girasoli Carbonara - dist: 0.5885052680969238 - row: 24759 Word: Girasoli Tomate Mozzarella Basilic - dist: 0.5942708849906921 - row: 24732 Found embedding word: Girasoli 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 = 'Girasoli' ------------- Found solution (max 20) -------------- Girasoli - girasoli - - Carrefour - 0 - 5400101059603 - 5400101059603 - OFF#2d5f87845757cb6591228a4dab184ac7 Girasoli - girasoli - - Delhaize - 0 - 5400119519458 - 5400119519458 - OFF#39c9519372c379679a6abe0a8240682b ---------------------------------------------------- ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ----------- result to be analyzed ----------- {'name': 'pesto cr\x0e\x0emeux', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', '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 '% pesto crmeu %' AND V_NormTrademark LIKE '%lustucru%' --> CPU time in DB: 0.1264 seconds Word: Crème de Pesto - dist: 0.567492663860321 - row: 8985 Word: Pesto à la Pistache - dist: 0.5783693790435791 - row: 41989 Word: Pesto - dist: 0.588503897190094 - row: 6793 Word: Pesto Génois - dist: 0.5976950526237488 - row: 61408 Word: Pesto Avec Tomates Séchées - dist: 0.6104001402854919 - row: 61627 Found embedding word: Crème de Pesto 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 = 'Crème de Pesto' ------------- Found solution (max 20) -------------- Crème de Pesto - creme de pesto - - Benedicta - 0 - 3660603005085 - 3660603005085 - OFF#b3d6bae67a0a204b3963251a73515806 Crème de Pesto - creme de pesto - - Benedicta - 0 - 3660603005092 - 3660603005085 - OFF#d6f4d25aa285b085adcbe1220435450b ---------------------------------------------------- ERROR: no solution for picto in the first solution ERROR: no solution for picto in the first solution ----------- result to be analyzed ----------- {'name': 'ricotta', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', '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 '% ricotta %' AND V_NormTrademark LIKE '%lustucru%' --> CPU time in DB: 0.1361 seconds Word: Ricotta - dist: 0.3914923369884491 - row: 4290 Word: Ricotta BIO - dist: 0.5118317604064941 - row: 23510 Word: Ricottine - dist: 0.5187942981719971 - row: 13120 Word: Ricotta Recette Italienne - dist: 0.534857988357544 - row: 34578 Word: Ricotta Cremosa - dist: 0.5555064082145691 - row: 60817 Found embedding word: Ricotta 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 = 'Ricotta' ------------- Found solution (max 20) -------------- Ricotta - ricotta - - Lactalis - 0 - 04004271 - 04004271 - OFF#778ae25ca96920405170dcfba916a64a Ricotta - ricotta - - Aldi - 0 - 2006050016600 - 2006050016600 - OFF#c461cf4b38c5ff23aff8bea68c66e69c Ricotta - ricotta - - Casino - 0 - 3222472949084 - 3222472949084 - OFF#5c1473ac5bbf03c76de48e7d03877be3 Ricotta - ricotta - - Les Mousquetaires - 0 - 3250392550110 - 3250392550110 - OFF#d123504734825b00c5e719c145f8d6c7 Ricotta - ricotta - - Cora - 0 - 3257983010671 - 3257983010671 - OFF#bf30cbbab25c0cb369677c8b8ea4d2f9 Ricotta - ricotta - - Belle France - 0 - 3258561430850 - 3258561430850 - OFF#e270911e3ebbb80db9ebcdde5677fa8e Ricotta - ricotta - - Franprix - 0 - 3263856418819 - 3263856418819 - OFF#2e68e1c6ab2e862a749e9e5a1813c2a8 Ricotta - ricotta - - Monoprix - 0 - 3350031999642 - 3350031999642 - OFF#967b075d4f8850dcbdfb7f9867e0e673 Ricotta - ricotta - - Metro Chef - 0 - 3439495902556 - 3439495902556 - OFF#d9f084abf27e67e90fbd280468a730e8 Ricotta - ricotta - - Carrefour - 0 - 3560070514847 - 3560070514847 - OFF#05f8feb8b86e1ccabe913d62cffa078f Ricotta - ricotta - - Auchan - 0 - 3596710377312 - 3596710377312 - OFF#244e73ed262053255e3ee47f52a84b01 Ricotta - ricotta - - Lidl - 0 - 4056489796237 - 4056489796237 - OFF#4864956b2cd3823c720ab8b061dbe89a Ricotta - ricotta - - Delhaize - 0 - 5400112141809 - 5400112141809 - OFF#7b3cdd35f1916d32a2c19d22bd06bbcd Ricotta - ricotta - - Lactalis - 0 - 8000430193510 - 04004271 - OFF#fe324107c6868c1e6d0607a2b10f61b8 Ricotta - ricotta - - Lactalis - 0 - 5413209990990 - 04004271 - OFF#d5bad4455a16572ff41a512ad84a2c26 Ricotta - ricotta - - Lactalis - 0 - 2954360673904 - 04004271 - OFF#2ef0b27d7cd7dc939ebbac4fd177be46 Ricotta - ricotta - - Lactalis - 0 - 8000430194104 - 04004271 - OFF#a73bb71040ba59892d5e2d6a91965ce5 Ricotta - ricotta - - Auchan - 0 - 3596710446483 - 3596710377312 - OFF#5edd38d8ed297ec9582c2aeac9e6363a ---------------------------------------------------- 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 ----------- result to be analyzed ----------- {'name': 'basilic', 'quantity': 'deux personnes', 'type': 'food', 'brand': 'Lustucru', 'company': 'Lustucru', '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 '% basilic %' AND V_NormTrademark LIKE '%lustucru%' --> CPU time in DB: 0.1177 seconds Word: Basilic - dist: 0.3161274492740631 - row: 3974 Word: Basilico - dist: 0.5195747017860413 - row: 5044 Word: Olives Basilic - dist: 0.5427182912826538 - row: 16837 Word: Sauce Basilic - dist: 0.5608017444610596 - row: 35349 Word: Florette Basilic - dist: 0.5939298868179321 - row: 38357 Found embedding word: Basilic 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 = 'Basilic' ------------- Found solution (max 20) -------------- Basilic - basilic - - Panzani - 0 - 30383354190402 - 30383354190402 - OFF#72da6e072f4a83e1baf5e4e258f1a22f Basilic - basilic - frais - - 213 - - - CIQ#c3209fdb22917e746ba7b72c2fadd92e Basilic - basilic - séché - - 0 - - - CIQ#32ae428ee27457039c54d8808c40bb3f ---------------------------------------------------- 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/692ed1abe582c/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Girasoli', 'normName': ' girasoli ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#2d5f87845757cb6591228a4dab184ac7', 'quantity': 'deux personnes', 'quantityLem': '2 personne', 'pack': ['NOU.w78'], 'type': 'food', 'gtin': '5400101059603', 'gtinRef': '5400101059603', 'brand': 'Carrefour', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}, {'name': 'Crème de Pesto', 'normName': ' creme de pesto ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#b3d6bae67a0a204b3963251a73515806', 'quantity': 'deux personnes', 'quantityLem': '2 personne', 'pack': ['CSL.w18', 'CCL.w5'], 'type': 'food', 'gtin': '3660603005085', 'gtinRef': '3660603005085', 'brand': 'Benedicta', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': -1}, {'name': 'Ricotta', 'normName': ' ricotta ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#778ae25ca96920405170dcfba916a64a', 'quantity': 'deux personnes', 'quantityLem': '2 personne', 'pack': ['UN2.w20'], 'type': 'food', 'gtin': '04004271', 'gtinRef': '04004271', 'brand': 'Lactalis', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}, {'name': 'Basilic', 'normName': ' basilic ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#72da6e072f4a83e1baf5e4e258f1a22f', 'quantity': 'deux personnes', 'quantityLem': '2 personne', 'pack': ['CSL.w18', 'CCL.w5'], 'type': 'food', 'gtin': '30383354190402', 'gtinRef': '30383354190402', 'brand': 'Panzani', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 8.924744129180908} ---------------------------------------------------------------------------------- LLM CPU Time: 8.924744129180908