Input path: /home/debian/html/nutritwin/output_llm/680fc8c3798d6/input.json Output path: /home/debian/html/nutritwin/output_llm/680fc8c3798d6/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/680fc8c3798d6/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": "Nutella", "quantity": "un pot", "type": "food", "event": "unknownEvent" } ] ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ [ { "name": "Nutella", "quantity": "un pot", "type": "food", "event": "unknownEvent" } ] ------------------------------------------------------ ------------------------ After simplification ------------------------ [ { "name": "Nutella", "quantity": "un pot", "type": "food", "event": "unknownEvent" } ] ---------------------------------------------------------------------- --------------------------------- LLM result ----------------------------------- {'response': [{'name': 'Nutella', 'quantity': 'un pot', 'type': 'food', 'event': 'unknownEvent'}], 'cost': 0.0} -------------------------------------------------------------------------------- ----------- result to be analyzed ----------- {'name': 'Nutella', 'quantity': 'un pot', 'type': 'food', '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 '% nutella %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL) --> CPU time in DB: 0.1347 seconds Word: Nutella - dist: 0.0059897564351558685 - row: 4481 Word: Nutella Ferrero - dist: 0.4360470175743103 - row: 9902 Word: Nutella Hazelnut Spread - dist: 0.43683353066444397 - row: 61235 Word: Nutella Mini - dist: 0.5035309195518494 - row: 61745 Word: Hazelnut Spread - dist: 0.5212731957435608 - row: 61347 Found embedding word: Nutella 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 = 'Nutella' ------------- Found solution (max 20) -------------- Nutella - nutella - - Ferrero - 0 - 0015070422003 - 0015070422003 - OFF#3bf6251e2a381a3bf8b429a25ecb7907 Nutella - nutella - - Ferrero - 0 - 3017620420443 - 0015070422003 - OFF#8e5ad1a1ee42b9521a5bc2ed7e7d6a48 Nutella - nutella - - Ferrero - 0 - 3017620730702 - 0015070422003 - OFF#5381553d576ba966af506ee8efc3b9b8 Nutella - nutella - - Ferrero - 0 - 3017620429484 - 0015070422003 - OFF#a8593478b1db5bdf6d5efde99f22229b Nutella - nutella - - Ferrero - 0 - 3017620429262 - 0015070422003 - OFF#c7fb4108d540f42e63a8facd1caf77a9 Nutella - nutella - - Ferrero - 0 - 80761006 - 0015070422003 - OFF#7b7696f610aa74a0e51c89eaeffcc793 Nutella - nutella - - Ferrero - 0 - 3017620428302 - 0015070422003 - OFF#69b0b1f36e523826f0bebd082b8da2e3 Nutella - nutella - - Ferrero - 0 - 3017620428005 - 0015070422003 - OFF#78e80951a2371c942cbaf0b7ab3dfaea Nutella - nutella - - Ferrero - 0 - 291417 - 0015070422003 - OFF#e6596e6b68ce722d4f54a2fb84808c0b Nutella - nutella - - Ferrero - 0 - 3017620401701 - 0015070422003 - OFF#5bfb7a511b75d4be79bfc132e6c0111e Nutella - nutella - - Ferrero - 0 - 3017620425400 - 0015070422003 - OFF#a2bcc28020455eaece62d12a33febdff Nutella - nutella - - Ferrero - 0 - 3017620425035 - 0015070422003 - OFF#67d2cc6f8c0fb1117a583d720abf1522 Nutella - nutella - - Ferrero - 0 - 3017620424403 - 0015070422003 - OFF#cdb2350f26862b6c9fa7cca0d8d825d5 Nutella - nutella - - Ferrero - 0 - 3017620423468 - 0015070422003 - OFF#b05eef49e82dc0fda4d6dae502c64c3b Nutella - nutella - - Ferrero - 0 - 3017620402135 - 0015070422003 - OFF#a451d82bb999bd153a26fc63e311b919 Nutella - nutella - - Ferrero - 0 - 3017620422027 - 0015070422003 - OFF#1569653a0d4b52049755d1f2c4986d76 Nutella - nutella - - Ferrero - 0 - 3017620422003 - 0015070422003 - OFF#ba47e599859bce59a6e0ebb6c6da8116 Nutella - nutella - - Ferrero - 0 - 3017620420009 - 0015070422003 - OFF#d1393af0ff1023f647f113ff083f72a7 Nutella - nutella - - Ferrero - 0 - 3017624047813 - 0015070422003 - OFF#4d5b2e2fd64cba3e63ce6e67b4fdb9b9 Nutella - nutella - - Ferrero - 0 - 3017620421044 - 0015070422003 - OFF#3d7a80205df4bf3a8ed242a66d8c5cb6 ---------------------------------------------------- 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 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 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/680fc8c3798d6/capture.jpg', 'intents': ['Identify foods and beverages in an image'], 'solutions': {'nutrition': [{'name': 'Nutella', 'normName': ' nutella ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#3bf6251e2a381a3bf8b429a25ecb7907', 'quantity': 'un pot', 'quantityLem': '1 pot', 'pack': ['CSS.w15', 'CCS.w9', 'TRT.w30'], 'type': 'food', 'gtin': '0015070422003', 'gtinRef': '0015070422003', 'brand': 'Ferrero', 'time': '', 'event': 'unknownEvent', 'serving': '', 'posiNormName': 0}], 'activity': [], 'response': {}}, 'cputime': 5.855588674545288} ---------------------------------------------------------------------------------- LLM CPU Time: 5.855588674545288