Input path: /home/debian/html/nutritwin/output_llm/67db1d2e41725/input.json Output path: /home/debian/html/nutritwin/output_llm/67db1d2e41725/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/67db1d2e41725/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": "frites rösti", "quantity": "500 grammes", "cookingMethod": "au four", "type": "food", "brand": "McCain", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "frites rösti", "quantity": "500 grammes", "cookingMethod": "au four", "type": "food", "brand": "McCain", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "frites r\u00f6sti", "quantity": "500 grammes", "cookingMethod": "au four", "type": "food", "brand": "McCain", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'frites rösti', 'quantity': '500 grammes', 'cookingMethod': 'au four', 'type': 'food', 'brand': 'McCain', 'event': 'unknownEvent'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'frites rösti', 'quantity': '500 grammes', 'cookingMethod': 'au four', 'type': 'food', 'brand': 'McCain', '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 '% frite rosti %' AND V_NormTrademark LIKE '%mccain%' --> CPU time in DB: 0.1132 seconds Word: Frites - dist: 0.5240097641944885 - row: 7685 Word: Frites Rustiques - dist: 0.5476354956626892 - row: 27572 Word: Pomme de Terre en Rösti - dist: 0.5599310398101807 - row: 2461 Word: Crousti' Frites - dist: 0.5600672960281372 - row: 34938 Word: Frite - dist: 0.5730298161506653 - row: 34155 Found embedding word: Frites 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 = 'Frites' ------------- Found solution (max 20) -------------- Frites - frite - - Burger King - 1 - 2000000036031 - 2000000036031 - OFF#15e50a056eec4e31d0d29e78b52f869f Frites - frite - - Marks & Spencer - 0 - 00898416 - 00898416 - OFF#56c8ae83be75040f53e4c88117d0d559 Frites - frite - - Casino - 0 - 3222477002241 - 3222477002241 - OFF#138ae1ecfd10b58f70f1eee773a65cfa Frites - frite - - Belle France - 0 - 3258561413044 - 3258561413044 - OFF#5bf33e915bcbae7f14e09622585a4469 Frites - frite - - Picard - 0 - 3270160125852 - 3270160125852 - OFF#6bf1ec91d3a358ec0ede3522c8849733 Frites - frite - - Thiriet - 0 - 3292590836276 - 3292590836276 - OFF#5f7613a48485d5753a97a9dee847d21a Frites - frite - - Carrefour - 0 - 3560070348459 - 3560070348459 - OFF#1ba3f7ab581d3b11fd2e5766dbf2ddb3 Frites - frite - - Auchan - 0 - 3596710035656 - 3596710035656 - OFF#5ce4d875067228f7fa910ae14e02b070 Frites - frite - - Haribo - 0 - 4001686330326 - 4001686330326 - OFF#9edf24748f3e86eb27cec1a2966244e2 Frites - frite - - Lidl - 0 - 4056489181569 - 4056489181569 - OFF#9d746d72f340bd041acde2d21c364e64 Frites - frite - - Delhaize - 0 - 5400111693163 - 5400111693163 - OFF#382a262619b8c94aa49b8889f480dea6 Frites - frite - - Picard - 0 - 3270160126262 - 3270160125852 - OFF#80406567c390aac8e9199b9c917a940f Frites - frite - - Thiriet - 0 - 3292590836818 - 3292590836276 - OFF#73c6434a690b35b8142df96a4b42903b Frites - frite - - Thiriet - 0 - 3292590836580 - 3292590836276 - OFF#43da676f9e390fe6a9c3af93caedf2c1 Frites - frite - - Thiriet - 0 - 3292590836870 - 3292590836276 - OFF#1ef53cdd5ad3d3770c459603ba627fdb Frites - frite - - Carrefour - 0 - 5400101254169 - 3560070348459 - OFF#ded4c0f04cd57179bb88b77b0a117e76 Frites - frite - - Carrefour - 0 - 3560070687114 - 3560070348459 - OFF#69bc1fbfec35fde3c95f074f525225d2 Frites - frite - - Auchan - 0 - 3596710418299 - 3596710035656 - OFF#3734c5cf805b1b015c54fd7167607913 Frites - frite - - Auchan - 0 - 3596710074464 - 3596710035656 - OFF#aaa4c5b1f81316267d7f114ddfc439ed Frites - frite - - Auchan - 0 - 3596710074457 - 3596710035656 - OFF#e14e2b10d8c995c9d34502ee1b250415 ---------------------------------------------------- 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 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/67db1d2e41725/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Frites', 'normName': ' frite ', 'comment': '', 'normComment': '', 'rank': 1, 'id': 'OFF#15e50a056eec4e31d0d29e78b52f869f', 'quantity': '500 grammes', 'quantityLem': '500 gramme', 'pack': ['FRI.w150'], 'type': 'food', 'gtin': '2000000036031', 'gtinRef': '2000000036031', 'brand': 'Burger King', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': -1}], 'activity': [], 'response': {}}, 'cputime': 11.034762382507324} ---------------------------------------------------------------------------------- LLM CPU Time: 11.034762382507324