POST /report

Analyze an entire menu and get a comprehensive quality report: duplicates, noise detection, cuisine breakdown, non-food items, and an overall quality score. Useful for onboarding new restaurants or auditing existing catalog data.

Request

Parameter	Type	Required	Description
items	string[]	Yes	Full menu item list to analyze. 1-2000 items, max 500 chars each.
restaurant_name	string	No	Restaurant name for the report header. Default: empty string.

{
  "items": [
    "Chicken Biryani",
    "**NEW** Murgh Biryani Serves 2",
    "Paneer Tikka",
    "Panner Tika",
    "Masala Dosa",
    "Paper Napkins",
    "Cigarette Lighter"
  ],
  "restaurant_name": "Sample Restaurant"
}

Response

Field	Type	Description
restaurant_name	string	Restaurant name (if provided)
total_items	int	Total menu items analyzed
noise_analysis	object	Promotional text and formatting noise analysis
dedup_summary	object	Duplicate item detection summary
cuisine_breakdown	object or null	Cuisine distribution (null if classifier not loaded)
non_food_items	object[]	Items detected as non-food
quality_score	int	Overall menu quality score (0-100, higher is better)
processing_time_ms	float	Processing time in milliseconds

noise_analysis

Field	Type	Description
items_with_noise	int	Count of items with promotional or formatting noise
noise_percentage	float	Percentage of menu items with noise
examples	object[]	Up to 5 examples showing original and cleaned text

dedup_summary

Field	Type	Description
total_clusters	int	Number of duplicate groups found
total_duplicate_items	int	Total excess duplicates across all clusters
top_clusters	object[]	Largest duplicate clusters (up to 10)

cuisine_breakdown

Field	Type	Description
distribution	object	Cuisine label to item count mapping, e.g. {"North Indian": 12, "Chinese": 5}

non_food_items

Field	Type	Description
item	string	Item text flagged as non-food
category	string	Non-food category: pharmacy, grocery, or merchandise

Example

import requests

response = requests.post("https://embed.statode.com/report",
    headers={"X-API-Key": "YOUR_KEY", "Content-Type": "application/json"},
    json={
        "items": menu_items,
        "restaurant_name": "Biryani House"
    }
)

report = response.json()
print(f"Quality Score: {report['quality_score']}/100")
print(f"Duplicates: {report['dedup_summary']['total_duplicate_items']} excess items")
print(f"Noise: {report['noise_analysis']['noise_percentage']:.0f}% of items have noise")
print(f"Non-food: {len(report['non_food_items'])} items flagged")

Cost

3.0 credits per item.

Notes

• This endpoint runs the full analysis pipeline (embed + dedup + classify + noise detection + non-food detection), which is why it costs more than individual endpoints.
• The quality score penalizes: high duplicate percentage, noise, non-food items, and low cuisine diversity.
• Non-food detection catches items like paper napkins, cigarettes, phone chargers, and other merchandise that sometimes appears in delivery platform catalogs.
• For menus over 2000 items, split into batches.