Input path: /home/debian/html/nutritwin/output_llm/69027344648e7/input.json Output path: /home/debian/html/nutritwin/output_llm/69027344648e7/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/69027344648e7/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": "sushi", "quantity": "trois", "type": "food", "event": "declaration" }, { "name": "wasabi", "quantity": "une noisette", "type": "food", "event": "declaration" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "sushi", "quantity": "trois", "type": "food", "event": "declaration" }, { "name": "wasabi", "quantity": "une noisette", "type": "food", "event": "declaration" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "sushi", "quantity": "trois", "type": "food", "event": "declaration" }, { "name": "wasabi", "quantity": "une noisette", "type": "food", "event": "declaration" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'sushi', 'quantity': 'trois', 'type': 'food', 'event': 'declaration'}, {'name': 'wasabi', 'quantity': 'une noisette', 'type': 'food', 'event': 'declaration'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'sushi', 'quantity': 'trois', '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 '% sushi %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Sushi Bar - sushi bar - sushi bar - - 0 - - - KCA#23ad768529b82c5d2e5bcf8b2fdc1dc6 Sushi Thon - sushi thon - sushi thon - - 0 - - - KCA#bf7d4f805f71507bfe0fa237b4c20c5e Sushi Alose - sushi alose - sushi alose - - 0 - - - KCA#50b6dd4b8107fffc141eb695d086e76d Sushi Brême - sushi breme - sushi brême - - 0 - - - KCA#fcd07f7bfda411663e3efce35fcd2d87 Sushi Bonite - sushi bonite - sushi bonite - - 0 - - - KCA#26103e6d83bd7bcbfff4621a774783e1 Sushi Saumon - sushi saumon - sushi saumon - - 0 - - - KCA#f366d90248edc0d02f459cc18228171a Sushi Poulpe - sushi poulpe - sushi poulpe - - 0 - - - KCA#82b1c17681bb6ce8246af9440648ebce Sushi Merlan - sushi merlan - sushi merlan - - 0 - - - KCA#a4db710534f6cb14b7f283c8b0966461 Sushi Congre - sushi congre - sushi congre - - 0 - - - KCA#31eda5e8ebd48c8cd728ae52845a45b1 Sushi Surimi - sushi surimi - sushi surimi - - 0 - - - KCA#235eef0088d70ce9eb1e5e885479c58d Sushi Calamar - sushi calamar - sushi calamar - - 0 - - - KCA#2f153aab490c0dd5b7247f243b8fa91b Sushi Sardine - sushi sardine - sushi sardine - - 0 - - - KCA#81f953af4a8945bbcb76e20742a98ddf Sushi Crevette - sushi crevette - sushi crevette - - 0 - - - KCA#e6ea0415e3560f332701361113c4066c Sushi Maquereau - sushi maquereau - sushi maquereau - - 0 - - - KCA#d079d44cf9fd5ab5b9f19d2ebbd1cb33 Sushi Crabe Royal - sushi crabe royal - crabe royal - - 0 - - - KCA#8652d3aed097648818fbc99c69636081 Sushi Poisson Blanc - sushi poisson blanc - poisson blanc - - 0 - - - KCA#82e46ab1804dbf934aac779c24e710ed Sushi ou Maki aux Produits de la Mer - sushi ou maki au produit de mer - - - 0 - - - CIQ#482eb99d8c205c752cd5be8262ae7fee ---------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'wasabi', 'quantity': 'une noisette', '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 '% wasabi %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) ------------- Found solution (max 20) -------------- Wasabi - wasabi - - - 84 - - - KCA#8f6fb32b342bbbf7f8a357f2731e7597 Riz à l'Avocat et au Wasabi - riz avocat wasabi - - - 14 - - - KCA#573ba2a186eadbe23f8a9572bc99f30e ---------------------------------------------------- ERROR: no solution for picto in the first solution --------------------------------- final result ----------------------------------- {'prompt': '', 'model': 'mistral-large-2411', 'imagePath': '/home/debian/html/nutritwin/output_llm/69027344648e7/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Sushi Bar', 'normName': ' sushi bar ', 'comment': 'sushi bar', 'normComment': ' sushi bar ', 'rank': 0, 'id': 'KCA#23ad768529b82c5d2e5bcf8b2fdc1dc6', 'quantity': 'trois', 'quantityLem': '3', 'pack': ['SUS.w30'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': 'SUS-300', 'posiNormName': 0}, {'name': 'Wasabi', 'normName': ' wasabi ', 'comment': '', 'normComment': '', 'rank': 84, 'id': 'KCA#8f6fb32b342bbbf7f8a357f2731e7597', 'quantity': 'une noisette', 'quantityLem': '1 noisette', 'pack': ['CCS.w4'], 'type': 'food', 'gtin': '', 'gtinRef': '', 'brand': '', 'time': '', 'event': 'declaration', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 2.603182554244995} ---------------------------------------------------------------------------------- LLM CPU Time: 2.603182554244995