Brand Voice Codifier

Overview

The Brand Voice Codifier is a tool designed to analyze, capture, and codify a client's brand tone of voice for integration with copywriting systems. This application demonstrates the core functionality through three input methods, parameter extraction, visualization, and export options.

Features

1. Multiple Input Methods

• Document Upload & Analysis: Upload brand documents (PDF, TXT) to extract voice parameters using intelligent AI analysis
• Conversational Brand Interview: Answer questions to define your brand voice with AI-enhanced interpretation
• Web Presence Scraper: Analyze website content for voice patterns using intelligent AI analysis

2. Parameter Extraction & Classification

• Brand Personality Profile
• Formality Spectrum
• Emotional Tone Framework
• Vocabulary Profile
• Communication Style Parameters
• Audience Adaptation Guidelines

3. Visualization & Results

• Interactive dashboard for reviewing brand voice parameters
• Visual representations of brand voice characteristics
• Real-time example generation of meta ad headlines and taglines

4. Export Options

• Well-formatted HTML report with print functionality
• JSON export for integration with copywriting systems
• API integration with external AI systems

Installation

1. Clone this repository

   git clone https://github.com/tom2tomtomtom/BrandVoice.git
   cd BrandVoice
   

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Download NLTK resources:

   python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords')"

Usage

1. Run the Flask application:

   python main.py
   

2. Open your web browser and navigate to http://localhost:5000

3. Choose one of the input methods:

   • Upload a document (PDF or TXT)
   • Complete the brand interview
   • Analyze a website

4. Review the results in the dashboard

5. Export your brand voice parameters as a formatted report or JSON

Project Structure

Brand Voice Codifier/
├── main.py                 # Main Flask application
├── requirements.txt        # Dependencies
├── README.md               # This file
├── static/                 # Static assets
│   ├── css/                # CSS stylesheets
│   └── js/                 # JavaScript files
├── templates/              # HTML templates
│   ├── base.html           # Base template
│   ├── index.html          # Home page
│   ├── document_upload.html # Document upload page
│   ├── brand_interview.html # Brand interview page
│   ├── web_scraper.html    # Web scraper page
│   ├── results.html        # Results dashboard
│   ├── report.html         # Formatted report
│   └── api_settings.html   # API integration settings
├── uploads/                # Temporary storage for uploads
└── venv/                   # Virtual environment (not tracked in git)

Key Features

Document Analysis

• Upload PDF or TXT files
• Extract text content
• Analyze for brand voice parameters
• Identify personality traits, emotional tone, and formality

Brand Interview

• Multi-step interview process
• Customizable options with "Other" fields
• Comprehensive coverage of brand voice aspects
• Real-time form validation

Web Scraper

• Analyze any public website
• Extract text content from paragraphs, headings, and lists
• Process content for brand voice parameters
• Identify patterns in online presence

Results Dashboard

• Visual representation of brand parameters
• Interactive charts
• Example ad copy generation
• Export options

Formatted Reports

• Professional HTML report
• Print-friendly design
• Comprehensive brand voice documentation
• Visual elements for easy understanding

Intelligent Text Analysis with API Integration

• Advanced AI-powered analysis using OpenAI, Anthropic, Cohere, or custom systems
• More accurate and nuanced brand voice parameter extraction
• Deeper insights into personality traits, emotional tone, and communication style
• Automatic fallback to basic analysis if API is unavailable
• Secure API key management
• Connection status monitoring

Future Enhancements

• PDF export functionality
• Enhanced AI model integration and customization
• Additional input methods (e.g., competitor analysis)
• Enhanced visualization options
• User accounts and saved brand profiles
• Collaborative editing features

License

This project is licensed under the MIT License - see the LICENSE file for details.