pyproject.toml

import yaml
import json
import markdown
from fuzzywuzzy import fuzz, process
from typing import Dict, List, Any
import logging

def parse_yaml(yaml_text: str) -> Dict[str, Any]:
    """Parse YAML text and return dictionary"""
    try:
        return yaml.safe_load(yaml_text)
    except yaml.YAMLError as e:
        logging.error(f"YAML parsing error: {str(e)}")
        raise e

def fuzzy_search(query: str, data: Dict[str, Any], threshold: int = 60) -> List[Dict[str, Any]]:
    """Perform fuzzy search on dictionary data"""
    matches = []
    
    if not isinstance(data, dict):
        return matches
    
    for key, value in data.items():
        if isinstance(value, (str, int, float)):
            value_str = str(value)
            
            # Check fuzzy match for key
            key_score = fuzz.partial_ratio(query.lower(), key.lower())
            if key_score >= threshold:
                matches.append({
                    'type': 'key',
                    'field': key,
                    'value': value_str,
                    'score': key_score
                })
            
            # Check fuzzy match for value
            value_score = fuzz.partial_ratio(query.lower(), value_str.lower())
            if value_score >= threshold:
                matches.append({
                    'type': 'value',
                    'field': key,
                    'value': value_str,
                    'score': value_score
                })
    
    # Sort by score descending
    matches.sort(key=lambda x: x['score'], reverse=True)
    return matches

def render_markdown(text: str) -> str:
    """Render markdown text to HTML with emoji support"""
    try:
        md = markdown.Markdown(extensions=['extra', 'codehilite'])
        html = md.convert(text)
        
        # Basic emoji support - convert common emoji codes
        emoji_map = {
            ':smile:': '😊',
            ':heart:': '❤️',
            ':thumbsup:': '👍',
            ':thumbsdown:': '👎',
            ':fire:': '🔥',
            ':rocket:': '🚀',
            ':star:': '⭐',
            ':check:': '✅',
            ':x:': '❌',
            ':warning:': '⚠️',
            ':info:': 'ℹ️',
            ':bulb:': '💡',
            ':tada:': '🎉'
        }
        
        for code, emoji in emoji_map.items():
            html = html.replace(code, emoji)
        
        return html
    except Exception as e:
        logging.error(f"Markdown rendering error: {str(e)}")
        return text

def create_dynamic_table(table_name: str, schema: Dict[str, Any]) -> bool:
    """Create a dynamic table based on schema (for future implementation)"""
    # This function can be expanded to create actual database tables
    # For now, we use the generic DataRecord model with JSON storage
    try:
        logging.info(f"Creating dynamic table: {table_name} with schema: {schema}")
        return True
    except Exception as e:
        logging.error(f"Error creating dynamic table: {str(e)}")
        return False

def validate_schema(schema: Dict[str, Any]) -> bool:
    """Validate table schema format"""
    if not isinstance(schema, dict):
        return False
    
    if 'fields' not in schema:
        return False
    
    if not isinstance(schema['fields'], list):
        return False
    
    for field in schema['fields']:
        if not isinstance(field, dict):
            return False
        if 'name' not in field or 'type' not in field:
            return False
    
    return True

def process_pipeline_data(pipeline_config: Dict[str, Any], source_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process data through a pipeline configuration"""
    processed_data = source_data.copy()
    
    try:
        # Apply transformations based on pipeline config
        transformations = pipeline_config.get('transformations', [])
        
        for transformation in transformations:
            transform_type = transformation.get('type')
            
            if transform_type == 'filter':
                condition = transformation.get('condition')
                processed_data = [item for item in processed_data if eval_condition(item, condition)]
            
            elif transform_type == 'map':
                mapping = transformation.get('mapping')
                for item in processed_data:
                    apply_mapping(item, mapping)
            
            elif transform_type == 'sort':
                field = transformation.get('field')
                reverse = transformation.get('reverse', False)
                processed_data.sort(key=lambda x: x.get(field, ''), reverse=reverse)
        
        return processed_data
    
    except Exception as e:
        logging.error(f"Pipeline processing error: {str(e)}")
        return source_data

def eval_condition(data: Dict[str, Any], condition: Dict[str, Any]) -> bool:
    """Evaluate a condition against data"""
    try:
        field = condition.get('field')
        operator = condition.get('operator')
        value = condition.get('value')
        
        if not field or not operator:
            return True
            
        data_value = data.get(field)
        
        if operator == 'equals':
            return data_value == value
        elif operator == 'contains':
            if data_value is None or value is None:
                return False
            return str(value).lower() in str(data_value).lower()
        elif operator == 'gt':
            try:
                return float(data_value or 0) > float(value or 0)
            except (ValueError, TypeError):
                return False
        elif operator == 'lt':
            try:
                return float(data_value or 0) < float(value or 0)
            except (ValueError, TypeError):
                return False
        
        return True
    except Exception:
        return True

def apply_mapping(data: Dict[str, Any], mapping: Dict[str, str]) -> None:
    """Apply field mapping to data"""
    try:
        for old_field, new_field in mapping.items():
            if old_field in data:
                data[new_field] = data.pop(old_field)
    except Exception as e:
        logging.error(f"Mapping error: {str(e)}")