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def validate_response(response_json):
"""
Validates the response JSON for completeness and brand/product name uniqueness.
Args:
response_json (dict): The JSON response to validate
Returns:
bool: True if validation passes, False otherwise
"""
try:
# 1. Check if all required fields are present and not empty
required_fields = [
"productName", "brandName", "ingredients", "servingSize",
"packagingSize", "servingsPerPack", "nutritionalInformation",
"fssaiLicenseNumbers", "claims", "shelfLife"
]
# Check if any required field is missing or empty
for field in required_fields:
if field not in response_json or not response_json[field]:
return False
# Type checking for specific fields
if not isinstance(response_json["ingredients"], list):
return False
if not isinstance(response_json["servingSize"], dict):
return False
if not isinstance(response_json["packagingSize"], dict):
return False
if not isinstance(response_json["servingsPerPack"], (int, float)):
return False
if not isinstance(response_json["nutritionalInformation"], list):
return False
if not isinstance(response_json["fssaiLicenseNumbers"], list):
return False
if not isinstance(response_json["claims"], list):
return False
if not isinstance(response_json["shelfLife"], str):
return False
# Validate serving size and packaging size structure
for size_field in ["servingSize", "packagingSize"]:
if not all(key in response_json[size_field] for key in ["quantity", "unit"]):
return False
if not isinstance(response_json[size_field]["quantity"], (int, float)):
return False
if not isinstance(response_json[size_field]["unit"], str):
return False
# 2. Check if brand name and product name have common words
# Convert to lowercase and split into words, removing any special characters
import re
def clean_and_split_text(text):
# Remove special characters and convert to lowercase
cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text.lower())
# Split into words and remove empty strings
return set(word for word in cleaned_text.split() if word)
brand_words = clean_and_split_text(response_json["brandName"])
product_words = clean_and_split_text(response_json["productName"])
# Check for common words
if brand_words.intersection(product_words):
return False
return True
except (KeyError, TypeError, AttributeError):
return False
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