from smolagents.tools import Tool
import pronouncing
import json
import difflib
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 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
    
        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()
    
        # 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()
            
                # 1. Rhyme score (most important - 60%)
                rhyme_score = 0
                if len(word_phone_list) > 1 and len(target_phone_list) > 1:
                    # Check if words share the same ending (vowel + final consonants)
                    vowel_plus_end = -2  # Index of the vowel in final syllable
                    while vowel_plus_end < -1:
                        if 'A' in word_phone_list[vowel_plus_end] or 'E' in word_phone_list[vowel_plus_end] or 'I' in word_phone_list[vowel_plus_end] or 'O' in word_phone_list[vowel_plus_end] or 'U' in word_phone_list[vowel_plus_end]:
                            break
                        vowel_plus_end += 1
                
                    if vowel_plus_end == -1:
                        vowel_plus_end = -2  # Fall back if no vowel found
                    
                    # Check if the ending (from vowel onwards) matches
                    if word_phone_list[vowel_plus_end:] == target_phone_list[vowel_plus_end:]:
                        rhyme_score = 1.0
            
                # 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%) - using string similarity
                string_similarity = SequenceMatcher(None, target, word).ratio()
            
                # Combined score (60% rhyme, 25% syllables, 15% similarity)
                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 == 1.0,
                        "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