Input path: /home/debian/html/nutritwin/output_llm/68f91ed071bb7/input.json Output path: /home/debian/html/nutritwin/output_llm/68f91ed071bb7/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/68f91ed071bb7/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": "betterave", "quantity": "plusieurs morceaux", "cookingMethod": "cuit", "type": "food", "event": "declaration" }, { "name": "chou kale", "quantity": "quelques feuilles", "cookingMethod": "cru", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "betterave", "quantity": "plusieurs morceaux", "cookingMethod": "cuit", "type": "food", "event": "declaration" }, { "name": "chou kale", "quantity": "quelques feuilles", "cookingMethod": "cru", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "betterave", "quantity": "plusieurs morceaux", "cookingMethod": "cuit", "type": "food", "event": "declaration" }, { "name": "chou kale", "quantity": "quelques feuilles", "cookingMethod": "cru", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'betterave', 'quantity': 'plusieurs morceaux', 'cookingMethod': 'cuit', 'type': 'food', 'event': 'declaration'}, {'name': 'chou kale', 'quantity': 'quelques feuilles', 'cookingMethod': 'cru', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'betterave', 'quantity': 'plusieurs morceaux', 'cookingMethod': 'cuit', '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 '% betterave %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Betterave Rouge - betterave rouge - - - 8160 - - - CIQ#19e3af05ec2db8b4603c4be2bc446a39 Betterave Ménagère - betterave menagere - - - 196 - - - KCA#cf59645b55ec29f3def37e35399eb3d0 Jus de Betterave, Carotte et Epinard - ju de betterave carotte epinard - - - 190 - - - KCA#bc44fc6902bae2f6850e3afe6f063d2d Salade Betteraves et Agneau au Miel - salade betterave agneau miel - - - 24 - - - KCA#2166cb4870932bad02161df026c04633 Salade Betterave, Fenouil et Saumon au Carvi - salade betterave fenouil saumon carvi - - - 31 - - - KCA#7c82baca18b6e6cbeeeec05c39082e8f Salade de Betterave, Haricots, Feta et Menthe - salade de betterave haricot feta menthe - - - 106 - - - KCA#f31a5e8ed43442368982779c1513d16f Risotto aux Betteraves - risotto au betterave - et à la roquette - - 9 - - - KCA#dfb88f6aa624f0c1011b6e69bfa34b69 ---------------------------------------------------- ERROR: no solution for picto in the first solution ----------- result to be analyzed ----------- {'name': 'chou kale', 'quantity': 'quelques feuilles', 'cookingMethod': 'cru', '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 '% chou kale %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1199 seconds Word: Chou - dist: 0.6088587641716003 - row: 1764 Word: Kiwi Chou Kale Détox - dist: 0.6255658268928528 - row: 24302 Word: Choux Kale Surgelé - dist: 0.6344713568687439 - row: 30649 Word: Chou Chinois - dist: 0.6393099427223206 - row: 693 Word: Chou-fleur - dist: 0.6512842178344727 - row: 3811 Found embedding word: Chou 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 = 'Chou' ------------- Found solution (max 20) -------------- Chou - chou - - - 1970 - - - KCA#03da66f29a5aea409ea105a6a4386e78 ---------------------------------------------------- ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/68f91ed071bb7/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Betterave Rouge', 'normName': ' betterave rouge ', 'comment': '', 'normComment': '', 'rank': 8160, 'id': 'CIQ#19e3af05ec2db8b4603c4be2bc446a39', 'quantity': 'plusieurs morceaux', 'quantityLem': 'plusieur morceau', 'pack': ['LEG.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}, {'name': 'Chou', 'normName': ' chou ', 'comment': '', 'normComment': '', 'rank': 1970, 'id': 'KCA#03da66f29a5aea409ea105a6a4386e78', 'quantity': 'quelques feuilles', 'quantityLem': 'quelque feuille', 'pack': ['LEG.w150'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 3.189509391784668} ---------------------------------------------------------------------------------- LLM CPU Time: 3.189509391784668