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"per_unit_metric_amount": 2,
"total_nutritional_content": {
"fat": 0.02,
"iron": 0.13,
"zinc": 0.02,
"fiber": 0.04,
"sugar": 0,
"folate": 3,
"niacin": 0.03,
"sodium": 1.22,
"calcium": 4.16,
"omega_3": 0,
"protein": 0.07,
"calories": 1,
"magnesium": 1.1,
"potassium": 14.76,
"trans_fat": 0,
"vitamin_a": 7.72,
"vitamin_c": 1.7,
"vitamin_d": 0,
"vitamin_e": 0,
"vitamin_k": 0,
"vitamin_b6": 0,
"cholesterol": 0,
"vitamin_b12": 0,
"carbohydrate": 0.14,
"saturated_fat": 0,
"monounsaturated_fat": 0.02,
"polyunsaturated_fat": 0
}
},
"food_id": 0,
"allergens": [],
"food_name": {
"de": "Frischer Dill",
"en": "Dill Weed, Fresh",
"fr": "Aneth Frais",
"it": "Aneto Fresco",
"tr": "Taze Dereotu"
},
"diet_compat": {
"vegan": "yes",
"plantbased": "yes",
"vegetarian": "yes",
"flexitarian": "yes",
"pescatarian": "yes"
},
"match_score": null,
"suggested_serving": {
"serving_id": 0,
"number_of_units": "0.02",
"metric_serving_unit": "g",
"serving_description": "100 g",
"metric_measure_amount": 100,
"metric_serving_description": "g"
}
}
]
}Nutriscan Dish Scan API
Platebase Dish Scan analyzes a food photo or dish description and returns nutrition, diet compatibility, allergens, and a likely ingredient list.
- Runtime (p50)
- 1m
- Estimated price
- Usage-based
Overview
NutriScan | Dish Scan Overview
NutriScan | Dish Scan is a byterise text-to-text model designed to turn food photos or dish descriptions into structured nutrition intelligence. Hosted on each::labs, this model helps teams quickly understand ingredients, allergens, diet compatibility, and high-level nutritional properties from natural language inputs. The primary differentiator of NutriScan | Dish Scan is its focus on nutrition-aware analysis rather than generic text understanding, making it purpose-built for food, health, and diet workflows. By combining ingredient parsing, allergen detection, and diet rules in a single text output, it offers a compact way to power nutrition features, menu assistants, and wellness applications without building complex rule systems from scratch.
Capabilities
Capabilities
- Parses natural language dish descriptions into clear ingredient lists, including primary components and common add-ons.
- Identifies potential allergens such as gluten, dairy, nuts, eggs, shellfish, and soy based on described ingredients.
- Assesses diet compatibility for common patterns (e.g., vegetarian, vegan, gluten-free, lactose-free, high-protein) when sufficient information is provided.
- Provides concise nutrition notes, such as whether a dish is likely high in calories, sugars, fats, or fiber, using qualitative rather than exact numeric estimates.
- Flags hidden ingredients that often carry allergens (e.g., sauces, dressings, breaded coatings) when mentioned in the description.
- Summarizes ingredients and dietary flags in structured text that can be post-processed by applications on each::labs.
- Supports workflow integration via the NutriScan | Dish Scan API, enabling programmatic nutrition checks across menus or recipe databases.
- Leverages the byterise text-to-text specialization for food-related semantics, improving interpretation of culinary terms and preparation styles.
Use cases
Use Cases for NutriScan | Dish Scan
NutriScan | Dish Scan is suited to a variety of roles, from product teams to content creators and developers. A restaurant or food delivery app can use the NutriScan | Dish Scan API to automatically extract ingredients and allergen flags from menu descriptions, for example: “Analyze all items in this menu text and return allergen warnings and diet tags.” A nutrition-focused creator can enrich recipe posts by generating quick diet compatibility summaries: “Summarize whether this oatmeal recipe with almond milk, berries, and honey suits vegan, vegetarian, and gluten-free diets.” Developers building wellness dashboards can connect byterise text-to-text outputs to user profiles, letting users paste meal descriptions like “beef stir-fry with vegetables and white rice” and receive structured compatibility and risk notes. Marketers for health brands can use the model to sanity-check claims around “high-protein” or “low-carb” dishes by asking for a nutrition-oriented qualitative assessment.
Tips & tricks
Tips and Tricks
To get reliable results from NutriScan | Dish Scan, structure prompts so the model can clearly identify ingredients, cooking methods, and serving size. Explicitly ask for sections such as “ingredients,” “allergens,” “diet compatibility,” and “notes” to keep the output easy to parse. For the NutriScan | Dish Scan API, include instructions about the desired format, for example bullet lists or JSON-style key/value pairs. When using byterise text-to-text models, avoid overly vague descriptions like “a typical pasta dish” and instead list what is visible or known.
Example prompts:
“Analyze this dish: grilled salmon with olive oil, lemon, steamed broccoli, and brown rice. List ingredients, allergens, diet compatibility, and a short nutrition summary.”
“For a veggie burger with whole-grain bun, lettuce, tomato, vegan mayo, and sweet potato fries, return ingredients, potential allergens, and whether it fits vegan, vegetarian, and gluten-free diets.”
“Given this description of a chicken Caesar salad with croutons and parmesan, provide ingredients, common allergens, and any diet conflicts.”
Technical spec
Technical Specifications
- Model category: byterise text-to-text, optimized for food and nutrition analysis from textual descriptions.
- Inputs: Natural language dish description or metadata derived from a food photo (e.g., list of ingredients, cooking method, portion size).
- Outputs: Structured text blocks covering ingredients, allergens, dietary compatibility, and nutrition notes, suitable for downstream parsing.
- Formats: UTF-8 text input and output; works well with JSON-like or list-based prompts when requested.
- Performance: Typical latency is comparable to modern text-to-text LLM calls; exact timings depend on request size and infrastructure.
- Architecture: Built on a large language model backbone fine-tuned for food and nutrition semantics; specific architecture details are not publicly documented.
Things to be aware of
Things to Be Aware Of
NutriScan | Dish Scan depends heavily on the completeness and accuracy of the input description; missing ingredients or unclear portions can lead to incomplete allergen and diet assessments. It may not capture regional or brand-specific recipe variations, so users should treat outputs as guidance rather than medical advice. Overly compressed prompts combining many dishes at once can make the text-to-text reasoning less precise, so batch large menus thoughtfully. When integrating through the NutriScan | Dish Scan API, plan for validation and potential human review for high-risk contexts such as clinical nutrition or severe allergy management.
Important NoticeNutriScan provides AI-generated nutritional analysis for informational purposes only.
- Nutritional values, allergen detection, ingredient measurements, and dietary compatibility results are estimates and may be incomplete or inaccurate.
- Outputs must not be considered medical advice, diagnosis, or treatment recommendations.
- The API must not be used as the sole basis for determining whether food is safe for consumption or suitable for individuals with allergies or medical conditions.
- Users are solely responsible for independently reviewing and verifying all outputs before making dietary or health-related decisions.
- Human verification is strongly recommended for all health and safety-related use cases.
AI model outputs are probabilistic in nature and may contain inaccuracies. Neither Eachlabs nor the model provider guarantees the accuracy or completeness of any output.
Key considerations
Key Considerations
NutriScan | Dish Scan works best when the input description is explicit about ingredients, preparation method, and portion size. Users should treat this model as an ingredient and diet reasoning engine rather than a strict calorie counter, as exact macronutrient values may not be available for every dish. It is ideal for applications where quick, explainable nutrition insights matter more than lab-grade precision, such as menu labeling assistants or diet compatibility checkers. When evaluating cost versus performance, consider that richer, more detailed prompts typically yield better structured outputs, which can reduce downstream validation work.
Limitations
Limitations
NutriScan | Dish Scan does not provide laboratory-grade nutrient quantification and should not be used as a substitute for certified dietary analysis tools. It may struggle with highly ambiguous dish names, proprietary recipes, or incomplete ingredient lists, and it cannot infer exact micronutrient content. Outputs are qualitative text, not guaranteed to follow a strict schema unless the prompt enforces structure, which means downstream parsing may need normalization. The model is not a diagnostic tool and should not be relied on for medical decisions, especially in cases of severe allergies or complex dietary conditions.
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About Nutriscan Dish Scan API
What is NutriScan Dish Scan?
NutriScan Dish Scan estimates the nutrition of a prepared meal from either a food photo or a short text description. It returns nutrition facts, diet compatibility, detected allergens, and a likely ingredient breakdown, which is useful when there is no packaging or barcode to read. Results are AI-generated for informational use.