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from smolagents.tools import Tool
import pronouncing
import difflib
import json
import string
class ParodyWordSuggestionTool(Tool):
name = "parody_word_suggester"
description = """Suggests rhyming funny words using CMU dictionary pronunciations.
Returns similar-sounding words that rhyme, especially focusing on common vowel sounds."""
inputs = {'target': {'type': 'string', 'description': 'The word you want to find rhyming alternatives for'}, 'word_list_str': {'type': 'string', 'description': 'JSON string of word list (e.g. \'["word1", "word2"]\')'}, 'min_similarity': {'type': 'string', 'description': 'Minimum similarity threshold (0.0-1.0)', 'nullable': True}}
output_type = "string"
def _are_vowels_similar(self, v1: str, v2: str) -> bool:
"""Check if two vowel sounds are similar enough to rhyme."""
# Strip stress markers
v1 = v1.rstrip('012')
v2 = v2.rstrip('012')
# Define groups of similar vowel sounds
similar_vowels = [
{'AH', 'UH'}, # Short u sounds
{'AE', 'EH'}, # Short e/a sounds
{'IY', 'IH'}, # Long/short i sounds
{'AO', 'AA'}, # Open o/a sounds
{'UW', 'UH'}, # Long/short oo sounds
]
# Direct match
if v1 == v2:
return True
# Check if they're in the same similarity group
for group in similar_vowels:
if v1 in group and v2 in group:
return True
return False
def _contains_vowel(self, phone: str, vowels: list) -> bool:
"""Helper function to check if a phone contains any vowel from the list."""
for v in vowels:
if v in phone:
return True
return False
def forward(self, target: str, word_list_str: str, min_similarity: str = "0.5") -> str:
"""Get rhyming word suggestions."""
import pronouncing
import string
import json
from difflib import SequenceMatcher
VOWEL_LETTERS = ['A', 'E', 'I', 'O', 'U']
target = target.lower().strip(string.punctuation)
min_similarity = float(min_similarity)
suggestions = []
# Parse JSON string to list
try:
words = json.loads(word_list_str)
except json.JSONDecodeError:
return json.dumps({
"error": "Invalid JSON string for word_list_str",
"suggestions": []
}, indent=2)
# Get target pronunciation
target_phones = pronouncing.phones_for_word(target)
if not target_phones:
return json.dumps({
"error": f"'{target}' not found in CMU dictionary",
"suggestions": []
}, indent=2)
target_phones = target_phones[0]
target_phone_list = target_phones.split()
# Find the last vowel in target
target_last_vowel = None
target_last_vowel_idx = -1
for i, phone in enumerate(target_phone_list):
if self._contains_vowel(phone, VOWEL_LETTERS):
target_last_vowel = phone
target_last_vowel_idx = i
# Check each word
for word in words:
word = word.lower().strip(string.punctuation)
phones = pronouncing.phones_for_word(word)
if phones:
word_phones = phones[0]
word_phone_list = word_phones.split()
# Find last vowel in word
word_last_vowel = None
word_last_vowel_idx = -1
for i, phone in enumerate(word_phone_list):
if self._contains_vowel(phone, VOWEL_LETTERS):
word_last_vowel = phone
word_last_vowel_idx = i
# 1. Rhyme score (most important - 60%)
rhyme_score = 0
if word_last_vowel and target_last_vowel:
# Check if the vowels are similar
if self._are_vowels_similar(word_last_vowel, target_last_vowel):
# Check if the endings after the vowel match
if (word_phone_list[word_last_vowel_idx:] ==
target_phone_list[target_last_vowel_idx:]):
rhyme_score = 1.0
# Partial match for similar endings
elif (len(word_phone_list) > word_last_vowel_idx + 1 and
len(target_phone_list) > target_last_vowel_idx + 1 and
word_phone_list[-1] == target_phone_list[-1]):
rhyme_score = 0.8
# 2. Syllable match (25%)
target_syl = pronouncing.syllable_count(target_phones)
word_syl = pronouncing.syllable_count(word_phones)
syllable_score = 1.0 if target_syl == word_syl else 0.0
# 3. Overall similarity (15%)
string_similarity = SequenceMatcher(None, target, word).ratio()
# Combined score
similarity = (rhyme_score * 0.6) + (syllable_score * 0.25) + (string_similarity * 0.15)
if similarity >= min_similarity:
suggestions.append({
"word": word,
"similarity": round(similarity, 3),
"rhyme_match": rhyme_score > 0,
"rhyme_score": round(rhyme_score, 3),
"syllable_match": syllable_score == 1.0,
"string_similarity": round(string_similarity, 3),
"syllables": word_syl,
"phones": word_phones
})
# Sort by similarity score descending
suggestions.sort(key=lambda x: x["similarity"], reverse=True)
result = {
"target": target,
"target_syllables": pronouncing.syllable_count(target_phones),
"target_phones": target_phones,
"suggestions": suggestions
}
return json.dumps(result, indent=2)
def __init__(self, *args, **kwargs):
self.is_initialized = False
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