Input path: /home/debian/html/nutritwin/output_llm/68073b8ddea89/input.json Output path: /home/debian/html/nutritwin/output_llm/68073b8ddea89/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/68073b8ddea89/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", "quantity": "cinq tranches", "cookingMethod": "grillé", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "confiture", "quantity": "trois tranches", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "jus d'orange", "quantity": "un verre", "brand": "Joker", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" }, { "name": "café au lait", "quantity": "une tasse", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "pain", "quantity": "cinq tranches", "cookingMethod": "grillé", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "confiture", "quantity": "trois tranches", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "jus d'orange", "quantity": "un verre", "brand": "Joker", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" }, { "name": "café au lait", "quantity": "une tasse", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "pain", "quantity": "cinq tranches", "cookingMethod": "grill\u00e9", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "confiture", "quantity": "trois tranches", "timeOfTheDay": "breakfast", "type": "food", "event": "declaration" }, { "name": "jus d'orange", "quantity": "un verre", "brand": "Joker", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" }, { "name": "caf\u00e9 au lait", "quantity": "une tasse", "timeOfTheDay": "breakfast", "type": "beverage", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'pain', 'quantity': 'cinq tranches', 'cookingMethod': 'grillé', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}, {'name': 'confiture', 'quantity': 'trois tranches', 'timeOfTheDay': 'breakfast', 'type': 'food', 'event': 'declaration'}, {'name': "jus d'orange", 'quantity': 'un verre', 'brand': 'Joker', 'timeOfTheDay': 'breakfast', 'type': 'beverage', 'event': 'declaration'}, {'name': 'café au lait', 'quantity': 'une tasse', 'timeOfTheDay': 'breakfast', 'type': 'beverage', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'pain', 'quantity': 'cinq tranches', 'cookingMethod': 'grillé', 'timeOfTheDay': 'breakfast', '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 %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Pain - pain - - - 261532 - - - CIQ#78316c0b820d8f80c640c9d0bc741c50 Pain - pain - sans gluten - - 29 - - - CIQ#9d6a800b4a9dbe9504fb68b26057ad7b Pain - pain - baguette, courante - - 0 - - - CIQ#c92016dc98d790db0bc7c949d601f5c2 Pain - pain - baguette ou boule, au levain - - 0 - - - CIQ#4b65f0348cbdd1f29daadea789369616 Pain - pain - baguette ou boule, de campagne - - 0 - - - CIQ#665da1982ec8e7e74501d57dc7e111b8 Pain - pain - baguette, de tradition française - - 0 - - - CIQ#e5e8a2a86b1a95d66e26a64c18c0b520 Pain - pain - baguette ou boule, bis, à la farine T80 ou T110 - - 0 - - - CIQ#233b9a74f0cc423be7b3fe6fa040567b Pain - pain - baguette ou boule, bio, à la farine T55 jusqu'à T110 - - 0 - - - CIQ#91fae3ae1c9b87dd0039d7caa03a7d72 Pain - pain - baguette ou boule, aux céréales et graines, artisanal - - 0 - - - CIQ#5fed24621fe6dde995398f020bf84d7d Pain Bis - pain bi - - - 77 - - - KCA#0d04d397f5620b8618c8972be2ce29a7 Pain Pita - pain pita - - - 951 - - - KCA#0a6b29619370c1e5c09e5ec16992feed Pain Azyme - pain azyme - - - 1038 - - - KCA#90d292248257ebd4aba91b7e0f6f67d7 Pain Perdu - pain perdu - - - 783 - - - CIQ#67427fe34e70bfc99fd131b16908c1ee Pain de Son - pain de son - - - 302 - - - KCA#3ccdb3c87985b4f83e1354ee3a2cebfd Pain au Son - pain son - - - 0 - - - CIQ#825cc00fe7ac81ed34e142fde0f6ddf4 Pain de Mie - pain de mie - au son - - 0 - - - CIQ#1f8d06921f1e892824b0f8cef870e840 Pain de Mie - pain de mie - complet - - 7211 - - - CIQ#d93405497d2314d29dbd770c5b956eeb Pain de Mie - pain de mie - courant - - 0 - - - CIQ#667832b5357e637fdb28760b7d6c2d8d Pain Grillé - pain grille - domestique - - 0 - - - CIQ#f4bc68c618fb825e526db4034e88b66a Pain de Mie - pain de mie - sans croûte - - 32 - - - CIQ#be3f663945b51703d39413cadc3becab ---------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'confiture', 'quantity': 'trois tranches', 'timeOfTheDay': 'breakfast', '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 %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Confiture - confiture - tout type de fruits, allégée en sucres, extra ou classique - - 53737 - - - CIQ#5f4c2ed2915b774d66dc3fdc2c79d576 Confiture Allégée - confiture allegee - - - 4771 - - - KCA#cbcbd871e5c12bc077c91127844d390d Confiture d'Abricot - confiture abricot - - - 2045 - - - KCA#bd7750e2da2d1f03f7823a609548dc4d Confiture de Fraise - confiture de fraise - extra ou classique - - 0 - - - CIQ#41b7efec1a5bddcbc9466fbd067f31bf Confiture d'Abricot - confiture abricot - extra ou classique - - 0 - - - CIQ#7f8af9147bbf56bdfca2fb0975456e74 Confiture de Myrtilles - confiture de myrtille - extra ou classique - - 0 - - - CIQ#6fafaf70d20aef30be6e54d58fe9c169 Confiture ou Marmelade - confiture ou marmelade - tout type de fruits, aliment moyen - - 0 - - - CIQ#527194b244454ffd3017dcaf9dc444fc Confiture ou Marmelade - confiture ou marmelade - tout type de fruits, teneur en sucre inconnue, aliment moyen - - 0 - - - CIQ#434fd1ed6d1ba3c29f3b281690f82147 Beignet à la Confiture - beignet confiture - - - 75 - - - CIQ#0cd55080bb8acc3682b2ca6955d19cfc Barquette à la Confiture - barquette confiture - - - 235 - - - KCA#2f3f3900e6eb51f51bb41804692824c8 Tartine de Confiture - tartine de confiture - de confiture - - 0 - - - KCA#6c5c28a4f42a6ca22e6e6d39dc7c28dc Baguette Beurre Confiture - baguette beurre confiture - - - 5301 - - - KCA#d399b90a645a52039b2f409debeaa686 Tarte Alsacienne à la Confiture - tarte alsacienne confiture - la confiture - - 0 - - - KCA#7b9ea702be358c77c2fbe92bde53ae9d ---------------------------------------------------- ERROR: no solution for picto in the first solution ----------- result to be analyzed ----------- {'name': "jus d'orange", 'quantity': 'un verre', 'brand': 'Joker', 'timeOfTheDay': 'breakfast', 'type': 'beverage', '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 '% ju orange %' AND V_NormTrademark LIKE '%joker%' --> CPU time in DB: 0.1196 seconds Word: Jus d'Orange - dist: 0.3314456343650818 - row: 3845 Word: Jus D Orange - dist: 0.41386309266090393 - row: 26391 Word: Jus Orange - dist: 0.42004847526550293 - row: 31088 Word: Jus d'Orange Pur Jus - dist: 0.43320274353027344 - row: 44448 Word: Jus d'Orange sans Pulpe - dist: 0.4421837627887726 - row: 4809 Found embedding word: Jus d'Orange Traceback (most recent call last): File "/home/debian/html/nutritwin/resources/KCALLMMainService.py", line 71, in omess = KCALLMMain.runEvent(event) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMMain.py", line 132, in runEvent resp = KCALLMMainSpeechToData.execute(speech, imagePath, image64, comment, appId, device, version, age, gender, longitude, latitude, test) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMMainSpeechToData.py", line 39, in execute omess = executeLLMSingle(text, imagePath, image64, comment, model) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMMainSpeechToData.py", line 195, in executeLLMSingle sols = KCALLMNutritionUtilities.getBestSolutions(jresult["response"], dbPath, dbEmbeddingPath, jVoca) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/debian/html/nutritwin/resources/KCALLMNutritionUtilities.py", line 395, in getBestSolutions dbCursor.execute(q) sqlite3.OperationalError: near "Orange": syntax error