feat: Initial implementation of Marketing Assistant AI for Adriana James
- Set up FastAPI backend with modular structure: - main.py for API routing - copywriter.py for AI-powered content generation using Cohere - embeddings.py for generating and reranking content embeddings - vector_store.py for FAISS-based similarity search - brand_style.py for managing brand tone, taboo words, and preferred terms - config.py for managing environment and application settings - Configured RESTful API endpoints: /generate-copy, /brand-style, /training-data, /improve-content, /analyze-content - Created frontend with vanilla HTML, CSS, and JS (index.html, styles.css, app.js) - Integrated brand style management for tone, voice, taboo words, and terminology - Implemented vector search for referencing similar historical content - Enabled training data input to improve future AI output - Added environment variable support for API keys and model configs - Structured data storage with local JSON and DB files - Added developer documentation, API reference, and project setup instructions This commit provides the foundation for a full-stack, AI-driven content creation platform that ensures brand consistency, speeds up marketing workflows, and supports iterative improvement over time.
This commit is contained in:
Michael Ikehi
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"""
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Brand style module for the Marketing Assistant AI.
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Ensures generated content aligns with Adriana James' brand voice and tone.
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"""
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import json
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import os
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from typing import Dict, List, Any, Optional
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from pathlib import Path
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from loguru import logger
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import config
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class BrandStyleManager:
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"""Manages brand style guidelines and ensures content consistency."""
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def __init__(self):
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"""Initialize the BrandStyleManager with default or stored style guidelines."""
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self.style_path = Path(config.DATA_DIR) / "style_guidelines" / "brand_style.json"
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self.style_guidelines = self._load_or_create_style()
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logger.info("BrandStyleManager initialized successfully")
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def _load_or_create_style(self) -> Dict[str, Any]:
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"""Load existing style guidelines or create new ones with defaults."""
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try:
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if self.style_path.exists():
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with open(self.style_path, 'r') as f:
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style = json.load(f)
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logger.info("Loaded existing brand style guidelines")
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return style
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else:
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# Create directory if it doesn't exist
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self.style_path.parent.mkdir(exist_ok=True)
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# Use default style guidelines
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style = config.DEFAULT_BRAND_STYLE
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# Save default style
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with open(self.style_path, 'w') as f:
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json.dump(style, f, indent=2)
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logger.info("Created default brand style guidelines")
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return style
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except Exception as e:
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logger.error(f"Error loading or creating style guidelines: {str(e)}")
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# Fall back to default style
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return config.DEFAULT_BRAND_STYLE
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def get_style_guidelines(self) -> Dict[str, Any]:
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"""
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Get current brand style guidelines.
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Returns:
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Dictionary of style guidelines
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"""
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return self.style_guidelines
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def update_style_guidelines(self, new_style: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Update brand style guidelines.
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Args:
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new_style: Dictionary with new style guidelines
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Returns:
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Updated style guidelines dictionary
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"""
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try:
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# Merge new style with existing
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for key, value in new_style.items():
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self.style_guidelines[key] = value
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# Ensure brand name is preserved
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self.style_guidelines['brand_name'] = config.BRAND_NAME
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# Save updated style
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with open(self.style_path, 'w') as f:
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json.dump(self.style_guidelines, f, indent=2)
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logger.info("Updated brand style guidelines")
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return self.style_guidelines
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except Exception as e:
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logger.error(f"Error updating style guidelines: {str(e)}")
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raise
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def format_prompt_with_brand_style(self, user_prompt: str, content_type: Optional[str] = None) -> str:
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"""
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Format user prompt with brand style guidelines for the LLM.
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Args:
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user_prompt: Original user prompt
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content_type: Type of content being generated
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Returns:
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Formatted prompt with brand style instructions
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"""
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style = self.style_guidelines
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# Create a formatted prompt with brand style instructions
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prompt_parts = [
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f"Generate marketing content for {style['brand_name']} based on the following request:",
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f"\"{user_prompt}\"",
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"\nFollow these brand style guidelines:",
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f"- Brand Name: {style['brand_name']}",
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f"- Tone: {', '.join(style.get('tone', []))}",
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f"- Voice Characteristics: {', '.join(style.get('voice_characteristics', []))}",
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]
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# Add taboo words if any
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if 'taboo_words' in style and style['taboo_words']:
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prompt_parts.append(f"- Avoid these words: {', '.join(style['taboo_words'])}")
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# Add preferred terms if any
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if 'preferred_terms' in style and style['preferred_terms']:
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terms = [f"use '{value}' instead of '{key}'" for key, value in style['preferred_terms'].items()]
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prompt_parts.append(f"- Preferred terminology: {'; '.join(terms)}")
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# Add content type specific instructions
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if content_type:
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if content_type == "email_campaign":
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prompt_parts.append("- Format as a professional email with subject line, greeting, body, and signature")
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elif content_type == "social_media":
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prompt_parts.append("- Format as a concise social media post with appropriate hashtags")
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elif content_type == "blog_post":
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prompt_parts.append("- Format as a blog post with title, introduction, body with subheadings, and conclusion")
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elif content_type == "website_copy":
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prompt_parts.append("- Format as website copy with clear headings and concise paragraphs")
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elif content_type == "ad_copy":
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prompt_parts.append("- Format as advertising copy with headline, body, and clear call to action")
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# Combine all parts
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formatted_prompt = "\n".join(prompt_parts)
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logger.debug("Created formatted prompt with brand style")
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return formatted_prompt
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def check_content_alignment(self, content: str) -> Dict[str, Any]:
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"""
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Check if generated content aligns with brand style guidelines.
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Args:
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content: Generated marketing content
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Returns:
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Dictionary with alignment metrics and suggestions
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"""
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style = self.style_guidelines
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taboo_words = style.get('taboo_words', [])
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preferred_terms = style.get('preferred_terms', {})
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# Check for taboo words
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found_taboo_words = []
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for word in taboo_words:
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if word.lower() in content.lower():
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found_taboo_words.append(word)
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# Check for preferred terminology
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terminology_issues = []
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for avoid, use in preferred_terms.items():
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if avoid.lower() in content.lower():
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terminology_issues.append(f"Found '{avoid}', should use '{use}' instead")
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# Calculate an overall alignment score (simple implementation)
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issues_count = len(found_taboo_words) + len(terminology_issues)
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alignment_score = max(0, 100 - (issues_count * 10)) # Reduce score for each issue
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return {
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'alignment_score': alignment_score,
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'taboo_words_found': found_taboo_words,
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'terminology_issues': terminology_issues,
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'aligned': alignment_score >= 80 # Consider aligned if score is 80% or higher
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}
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# Create a singleton instance
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brand_style_manager = BrandStyleManager()
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