from smolagents.tools import Tool import json import string import pronouncing class ParodyWordSuggestionTool(Tool): name = "parody_word_suggester" description = """Suggests rhyming funny words using CMU dictionary and custom 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}, 'custom_phones': {'type': 'object', 'description': 'Optional dictionary of custom word pronunciations', 'nullable': True}} output_type = "string" VOWEL_REF = "AH,AX|UH|AE,EH|IY,IH|AO,AA|UW|AY,EY|OW,AO|AW,AO|OY,OW|ER,AXR" def _get_vowel_groups(self): groups = [] group_strs = self.VOWEL_REF.split("|") for group_str in group_strs: groups.append(group_str.split(",")) return groups def _get_word_phones(self, word, custom_phones=None): """Get phones for a word, checking custom dictionary first.""" if custom_phones and word in custom_phones: return custom_phones[word]["primary_phones"] import pronouncing phones = pronouncing.phones_for_word(word) return phones[0] if phones else None def _get_last_syllable(self, phones: list) -> tuple: """Extract the last syllable (vowel + remaining consonants).""" last_vowel_idx = -1 last_vowel = None vowel_groups = self._get_vowel_groups() for i, phone in enumerate(phones): base_phone = phone.rstrip('012') for group in vowel_groups: if base_phone in group: last_vowel_idx = i last_vowel = base_phone break if last_vowel_idx == -1: return None, [] remaining = phones[last_vowel_idx + 1:] return last_vowel, remaining def _strip_stress(self, phones: list) -> list: result = [] for phone in phones: result.append(phone.rstrip('012')) return result def _vowels_match(self, v1: str, v2: str) -> bool: v1 = v1.rstrip('012') v2 = v2.rstrip('012') if v1 == v2: return True vowel_groups = self._get_vowel_groups() for group in vowel_groups: if v1 in group and v2 in group: return True return False def _calculate_similarity(self, word1, phones1, word2, phones2): """Calculate similarity score using improved metrics.""" # Initialize all variables word_vowel = None word_end = [] target_vowel = None target_end = [] phone_diff = 0 max_phones = 0 length_score = 0.0 rhyme_score = 0.0 stress_score = 0.0 i = 0 # For loop counter word_end_clean = [] target_end_clean = [] matched = 0 common_length = 0 phone_list1 = phones1.split() phone_list2 = phones2.split() # Calculate length similarity score phone_diff = abs(len(phone_list1) - len(phone_list2)) max_phones = max(len(phone_list1), len(phone_list2)) length_score = 1.0 if phone_diff == 0 else 1.0 - (phone_diff / max_phones) # Get last syllable components result1 = self._get_last_syllable(phone_list1) result2 = self._get_last_syllable(phone_list2) word_vowel, word_end = result1 target_vowel, target_end = result2 # Calculate rhyme score rhyme_score = 0.0 if word_vowel and target_vowel: if self._vowels_match(word_vowel, target_vowel): word_end_clean = self._strip_stress(word_end) target_end_clean = self._strip_stress(target_end) if word_end_clean == target_end_clean: rhyme_score = 1.0 else: # Partial rhyme based on ending similarity common_length = min(len(word_end_clean), len(target_end_clean)) matched = 0 for i in range(common_length): if word_end_clean[i] == target_end_clean[i]: matched += 1 rhyme_score = 0.6 * (matched / max(len(word_end_clean), len(target_end_clean))) # Calculate stress pattern similarity import pronouncing stress1 = pronouncing.stresses(phones1) stress2 = pronouncing.stresses(phones2) stress_score = 1.0 if stress1 == stress2 else 0.5 # Weighted combination (60% rhyme, 30% length, 10% stress) similarity = ( (rhyme_score * 0.6) + (length_score * 0.3) + (stress_score * 0.1) ) # Cap at 1.0 similarity = min(1.0, similarity) return { "similarity": round(similarity, 3), "rhyme_score": round(rhyme_score, 3), "length_score": round(length_score, 3), "stress_score": round(stress_score, 3), "phone_length_difference": phone_diff } def forward(self, target: str, word_list_str: str, min_similarity: str = "0.5", custom_phones: dict = None) -> str: import pronouncing import string import json # Initialize all variables target = target.lower().strip(string.punctuation) min_similarity = float(min_similarity) suggestions = [] word_vowel = None word_end = [] target_vowel = None target_end = [] valid_words = [] invalid_words = [] target_phone_list = [] # 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 = self._get_word_phones(target, custom_phones) if not target_phones: return json.dumps({ "error": f"Target word '{target}' not found in dictionary or custom phones", "suggestions": [] }, indent=2) # Filter word list valid_words = [] invalid_words = [] for word in words: word = word.lower().strip(string.punctuation) if self._get_word_phones(word, custom_phones): valid_words.append(word) else: invalid_words.append(word) if not valid_words: return json.dumps({ "error": "No valid words found in dictionary or custom phones", "invalid_words": invalid_words, "suggestions": [] }, indent=2) target_phone_list = target_phones.split() target_vowel, target_end = self._get_last_syllable(target_phone_list) # Check each word for word in valid_words: word_phones = self._get_word_phones(word, custom_phones) if word_phones: similarity_result = self._calculate_similarity(word, word_phones, target, target_phones) if similarity_result["similarity"] >= min_similarity: word_phone_list = word_phones.split() word_vowel, word_end = self._get_last_syllable(word_phone_list) suggestions.append({ "word": word, "similarity": similarity_result["similarity"], "rhyme_score": similarity_result["rhyme_score"], "length_score": similarity_result["length_score"], "stress_score": similarity_result["stress_score"], "phone_length_difference": similarity_result["phone_length_difference"], "phones": word_phones, "last_vowel": word_vowel, "ending": " ".join(word_end) if word_end else "", "is_custom": word in custom_phones if custom_phones else False }) # Sort by similarity score descending suggestions.sort(key=lambda x: x["similarity"], reverse=True) result = { "target": target, "target_phones": target_phones, "target_last_vowel": target_vowel, "target_ending": " ".join(target_end) if target_end else "", "invalid_words": invalid_words, "suggestions": suggestions } return json.dumps(result, indent=2) def __init__(self, *args, **kwargs): self.is_initialized = False