Taste Testing Module

The taste endpoints let you analyze and compare finished dishes and ingredients.

Analyzing a Dish

POST /v1/taste/analyze

Analyze the flavor profile of any dish or ingredient in your session.

import requests

API_BASE = "https://api.tomatopy.pizza/v1"
HEADERS = {
    "Content-Type": "application/json",
    "X-API-Key": "your-api-key",
    "X-Session-ID": "your-session-id"
}

# Basic analysis
response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
    "dish_id": "bkd_vwx234",
    "depth": "basic"
})
profile = response.json()

print(f"Sweetness: {profile['sweetness']}")  # 0.3
print(f"Acidity: {profile['acidity']}")      # 0.7
print(f"Umami: {profile['umami']}")          # 0.8

Comprehensive Analysis

Include aroma and texture data for a fuller picture:

# Comprehensive analysis with aroma and texture
response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
    "dish_id": "bkd_vwx234",
    "depth": "comprehensive",
    "include_aroma": True,
    "include_texture": True
})
profile = response.json()

# Basic taste metrics
print(f"Sweetness: {profile['sweetness']}")
print(f"Acidity: {profile['acidity']}")
print(f"Umami: {profile['umami']}")

# Aroma metrics
print(f"Aroma intensity: {profile['aroma']['intensity']}")
print(f"Aroma notes: {profile['aroma']['notes']}")

# Texture metrics
print(f"Texture score: {profile['texture']['score']}")
print(f"Firmness: {profile['texture']['firmness']}")

Request Body

Field	Type	Required	Description
dish_id	string	yes	ID of any dish or ingredient in the session
depth	string	no	"basic" or "comprehensive" (default: "basic")
include_aroma	boolean	no	Include aroma analysis (default: false)
include_texture	boolean	no	Include texture analysis (default: false)

Comparing Dishes

POST /v1/taste/compare

Compare multiple dishes or ingredients side by side on specific metrics.

# Compare three sauces
response = requests.post(f"{API_BASE}/taste/compare", headers=HEADERS, json={
    "dish_ids": ["sce_001", "sce_002", "sce_003"],
    "metrics": ["sweetness", "acidity", "umami"]
})
comparison = response.json()

# Access comparison results
for dish in comparison["results"]:
    print(f"Dish {dish['id']}:")
    print(f"  Sweetness: {dish['sweetness']}")
    print(f"  Acidity: {dish['acidity']}")
    print(f"  Umami: {dish['umami']}")

# Rankings
print(f"Sweetest: {comparison['rankings']['sweetness'][0]}")
print(f"Most acidic: {comparison['rankings']['acidity'][0]}")

Request Body

Field	Type	Required	Description
dish_ids	array of strings	yes	IDs of dishes to compare (minimum 2)
metrics	array of strings	yes	Metrics to compare (e.g., "sweetness", "acidity", "umami", "aroma", "texture")

Analyzing Different Stages

You can analyze ingredients at any stage of the pipeline -- raw tomatoes, sliced tomatoes, sauces, or finished pizzas:

# Analyze a raw tomato
response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
    "dish_id": tomato["id"],
    "depth": "comprehensive",
    "include_aroma": True
})
raw_profile = response.json()

# Analyze the sauce made from that tomato
response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
    "dish_id": sauce["id"],
    "depth": "comprehensive",
    "include_aroma": True
})
sauce_profile = response.json()

# Compare raw vs cooked
response = requests.post(f"{API_BASE}/taste/compare", headers=HEADERS, json={
    "dish_ids": [tomato["id"], sauce["id"]],
    "metrics": ["sweetness", "acidity", "umami"]
})
comparison = response.json()
print("Raw vs Cooked comparison:", comparison)

Error Handling

# Handle invalid dish ID
response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
    "dish_id": "invalid_id",
    "depth": "basic"
})

if response.status_code == 400:
    error = response.json()
    print(f"Error: {error['message']}")  # "Dish not found in session"

# Handle empty comparison list
response = requests.post(f"{API_BASE}/taste/compare", headers=HEADERS, json={
    "dish_ids": [],
    "metrics": ["sweetness"]
})

if response.status_code == 400:
    error = response.json()
    print(f"Error: {error['message']}")  # "At least 2 dish IDs required"

Best Practices

1. Choose the Right Depth

   Use "basic" for quick checks and "comprehensive" when you need full details. Comprehensive analysis includes more data but takes longer.

2. Compare at the Same Stage

   For meaningful comparisons, compare dishes at the same pipeline stage (e.g., sauce vs sauce, not sauce vs raw tomato).

3. Use Aroma and Texture for Final Products

   # Best for finished pizzas
   response = requests.post(f"{API_BASE}/taste/analyze", headers=HEADERS, json={
       "dish_id": baked_pizza["id"],
       "depth": "comprehensive",
       "include_aroma": True,
       "include_texture": True
   })

Endpoint Reference

Endpoint	Method	Description
/v1/taste/analyze	POST	Analyze flavor profile of a dish
/v1/taste/compare	POST	Compare multiple dishes on specific metrics

Next Steps

• Session Management - Debug and track your session

• Tutorials - Learn advanced flavor profiling techniques

• Best Practices - API usage tips