Input path: /home/debian/html/nutritwin/output_llm/674709d02ff82/input.json Output path: /home/debian/html/nutritwin/output_llm/674709d02ff82/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: ================================================================================================================================== ########################################### # For image extraction, GPT4 is used # ########################################### ==================================== Prompt ============================================= In the image, identify all the foods and the beverages. For each of them, identify the "name", the "type", the "quantity", if it exists, the "brand" and the "cooking" mode. "Portions", like "tranche", are quantities. Ignore what it is not connected to nutrition, beverage or food. When the "brand" is not specified and the product is very well-known (like "Coca-Cola"), provide the brand name in "brand", otherwise set "brand" to "". Identify what "type" of food. Identify the "company" to which the "brand" belongs. Estimate the "weight" in grams or centiliters for each result. Identify the time is the current time, map it on the closest case: "petit-déjeuner", "déjeuner", "grignotage" or "dîner". When the "name" has synonyms, use the most common name, example: "yaourt" is more common than "yogourt". Format the result for each ingredient of food & beverage in french in JSON in an array of tuples {"name":, "quantity":, "weight":, "cooking":, "brand":, "company":, "type":, "time":, "event": "declaration"}. ========================================================================================= Image recognition.... ------------------------------ LLM Raw response ----------------------------- Il semblerait que cette image montre une salade de fruits et de noix, mais comme il est impossible de déterminer avec certitude les ingrédients spécifiques, les marques, les quantités et les poids sans informations supplémentaires, je vais lister des suppositions basées sur ce qui semble visible. Cela dit, la "brand", la "company" et le "weight" resteront indéterminés dans cette situation. De même, l'heure du repas ou le "time" ne peut être déduite de cette image. Voici l'approximation formatée selon votre demande : ```json [ {"name": "pomme", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "banane", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "raisins secs", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit séché", "time": "", "event": "declaration"}, {"name": "noix", "quantity": "", "weight ----------------------------------------------------------------------------- ----------------- Make it compliant ------------------ Il semblerait que cette image montre une salade de fruits et de noix, mais comme il est impossible de déterminer avec certitude les ingrédients spécifiques, les marques, les quantités et les poids sans informations supplémentaires, je vais lister des suppositions basées sur ce qui semble visible. Cela dit, la "brand", la "company" et le "weight" resteront indéterminés dans cette situation. De même, l'heure du repas ou le "time" ne peut être déduite de cette image. Voici l'approximation formatée selon votre demande : ```json [ {"name": "pomme", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "banane", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "raisins secs", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit séché", "time": "", "event": "declaration"}, {"name": "noix", "quantity": "", "weight ------------------------------------------------------ ------------------------ After simplification ------------------------ [ {"name": "pomme", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "banane", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "raisins secs", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit séché", "time": "", "event": "declaration"}, {"name": "noix", "quantity": "", "weight ---------------------------------------------------------------------- ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ERROR: impossible to parse [II]: Il semblerait que cette image montre une salade de fruits et de noix, mais comme il est impossible de déterminer avec certitude les ingrédients spécifiques, les marques, les quantités et les poids sans informations supplémentaires, je vais lister des suppositions basées sur ce qui semble visible. Cela dit, la "brand", la "company" et le "weight" resteront indéterminés dans cette situation. De même, l'heure du repas ou le "time" ne peut être déduite de cette image. Voici l'approximation formatée selon votre demande : ```json [ {"name": "pomme", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "banane", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "raisins secs", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit séché", "time": "", "event": "declaration"}, {"name": "noix", "quantity": "", "weight The extracted string is [ {"name": "pomme", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "banane", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit", "time": "", "event": "declaration"}, {"name": "raisins secs", "quantity": "", "weight": "", "cooking": "cru", "brand": "", "company": "", "type": "fruit séché", "time": "", "event": "declaration"}, {"name": "noix", "quantity": "", "weight ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ --------------------------------- LLM result ----------------------------------- {'response': {}, 'cost': 0.0} -------------------------------------------------------------------------------- --------------------------------- final result ----------------------------------- {'prompt': '', 'intents': ['Identify food in an image'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [], 'activity': [], 'response': {}}, 'cputime': 7.7984302043914795} ---------------------------------------------------------------------------------- LLM CPU Time: 7.7984302043914795