A comprehensive collection of AI-powered Python tools with extensive customization options for cartoon generation, text processing, image analysis, and code assistance.

• Advanced cartoon generation with customizable styles, resolutions, and art styles
• Multiple output formats (PNG, JPEG, WebP, TIFF, GIF)
• Style presets (classic, vintage, vibrant, monochrome)
• Custom color palettes and artistic options
• Thumbnail generation and quality enhancement
• Animated GIF creation from cartoon sequences

• Multi-provider AI support (OpenAI, Anthropic, Gemini)
• Story generation with genre, character, and theme customization
• Code generation with framework integration and documentation
• Article writing with SEO optimization and tone control
• Style presets for different writing purposes
• Reading time estimation and content analysis

• Comprehensive image analysis (objects, scenes, colors, quality)
• Advanced image processing with filter presets and custom effects
• Batch processing capabilities for multiple images
• Quality assessment with improvement recommendations
• Comparison grid generation for before/after visualization
• Annotation tools for analysis results

• AI-powered code generation with customizable complexity levels
• Comprehensive code review with quality scoring
• Test generation with coverage estimation
• Code optimization and refactoring suggestions
• Multi-language support (Python, JavaScript, Java, Go)
• Documentation generation and code explanation

1. Clone the repository:

git clone https://github.com/GizzZmo/AI-driven-Python-code.git
cd AI-driven-Python-code

2. Install dependencies:

pip install -r requirements.txt

3. Set up API keys (optional for demo):

export OPENAI_API_KEY="your-openai-key"
export GEMINI_API_KEY="your-gemini-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

Run the comprehensive examples:

python examples.py

Or use individual components:

from CartoonGenerator import CartoonGenerator

generator = CartoonGenerator(api_key="your-api-key")

# Generate cartoon with custom options
cartoon = generator.generate_cartoon(
    prompt="Space adventure with robots",
    page_count=6,
    strips_per_page=3,
    resolution=(1920, 1080),
    style='vibrant',
    color_palette=['#FF6B6B', '#4ECDC4', '#45B7D1'],
    art_style='anime',
    custom_options={
        'mood': 'adventurous',
        'target_audience': 'teen'
    }
)

# Save with advanced options
generator.save_pages(
    cartoon, 
    "./output",
    format='PNG',
    apply_filters=True,
    enhance_quality=True,
    create_thumbnails=True
)

# Create animated GIF
generator.create_gif_animation(cartoon, "./animation.gif")

from TextGenerator import TextGenerator

generator = TextGenerator(provider="openai", api_key="your-key")

# Generate a story
story = generator.generate_story(
    prompt="A robot discovers emotions",
    genre="sci-fi",
    length="short",
    characters=["ARIA-7", "Dr. Chen"],
    setting="Neo-Tokyo 2157",
    themes=["consciousness", "humanity"],
    target_audience="teen"
)

# Generate code
code = generator.generate_code(
    description="REST API for todo list",
    language="python",
    include_tests=True,
    frameworks=["flask", "sqlalchemy"]
)

# Generate article
article = generator.generate_article(
    topic="Future of AI",
    article_type="informative",
    target_audience="tech professionals",
    keywords=["AI", "machine learning"]
)

from ImageAnalyzer import ImageAnalyzer

analyzer = ImageAnalyzer(api_key="your-key")

# Analyze image
analysis = analyzer.analyze_image(
    "image.jpg",
    analysis_types=['object_detection', 'scene_analysis', 'color_analysis'],
    save_annotations=True
)

# Process with filters
processed = analyzer.process_image(
    "image.jpg",
    filters=['enhance', 'dramatic'],
    resize=(800, 600),
    output_format='PNG'
)

# Batch processing
results = analyzer.batch_process(
    input_dir="./images",
    output_dir="./processed",
    operations={
        'analyze': {'types': ['scene_analysis']},
        'process': {'filters': ['enhance'], 'format': 'PNG'}
    }
)

from CodeAssistant import CodeAssistant

assistant = CodeAssistant(api_key="your-key")

# Generate code
generated = assistant.generate_code(
    description="Calculate fibonacci numbers",
    language="python",
    include_tests=True,
    complexity_level="intermediate"
)

# Review code
review = assistant.review_code(
    code_string,
    language="python",
    review_type="comprehensive"
)

# Generate tests
tests = assistant.generate_tests(
    code_string,
    test_framework="pytest",
    coverage_goal=90
)

# Explain code
explanation = assistant.explain_code(
    code_string,
    explanation_level="beginner",
    include_examples=True
)

• Resolution: Custom dimensions (e.g., (1920, 1080))
• Style presets: classic, vintage, vibrant, monochrome
• Art styles: cartoon, anime, realistic, sketch
• Color palettes: Custom hex color arrays
• Output formats: PNG, JPEG, WebP, TIFF
• Quality settings: Enhancement and filter options
• Animation: GIF creation with custom duration and loops

• Providers: openai, anthropic, gemini
• Style presets: creative, analytical, conversational, technical
• Content types: Stories, code, articles, documentation
• Languages: Multiple language support
• Frameworks: Integration with popular libraries
• Complexity levels: beginner, intermediate, advanced

• Analysis types: Object detection, scene analysis, color analysis, quality assessment
• Filter presets: enhance, vintage, dramatic, soft, sharp
• Custom filters: Brightness, contrast, saturation, blur parameters
• Output formats: Multiple image formats with quality control
• Batch operations: Process multiple images simultaneously

• Languages: Python, JavaScript, Java, Go
• Review types: quick, comprehensive, security, performance
• Test frameworks: pytest, unittest, jest, junit
• Complexity analysis: Cyclomatic complexity and maintainability scoring
• Optimization goals: Performance, readability, memory usage

AI-driven-Python-code/
├── CartoonGenerator.py      # Advanced cartoon generation
├── TextGenerator.py         # Multi-provider text generation
├── ImageAnalyzer.py         # Comprehensive image analysis
├── CodeAssistant.py         # AI-powered code assistance
├── examples.py             # Comprehensive usage examples
├── requirements.txt        # Project dependencies
├── README.md              # Documentation
└── examples_output/       # Generated demo files
    ├── cartoons/         # Cartoon generation outputs
    ├── images/           # Image analysis and processing
    ├── text/             # Generated text content
    └── code/             # Code generation and analysis

Support for multiple AI providers with seamless switching:

• OpenAI GPT models
• Anthropic Claude
• Google Gemini
• Local model integration

Efficient handling of multiple files:

• Parallel processing capabilities
• Progress tracking and error handling
• Configurable output organization

Automated quality metrics:

• Image quality scoring
• Code complexity analysis
• Content readability assessment
• Performance optimization suggestions

Flexible output formats:

• Multiple image formats with compression control
• Code export with documentation
• Analysis reports in various formats
• API-ready data structures

• Classic: Balanced brightness, contrast, and saturation
• Vintage: Muted colors with sepia tones
• Vibrant: Enhanced colors and contrast
• Monochrome: Grayscale conversion

• Creative: High temperature for imaginative content
• Analytical: Low temperature for factual content
• Conversational: Balanced for dialogue
• Technical: Precise for documentation

• Enhance: Improved brightness and clarity
• Vintage: Retro aesthetic with sepia
• Dramatic: High contrast and saturation
• Soft: Subtle blur for dreamy effect
• Sharp: Enhanced edge definition

The library provides comprehensive metrics:

• Generation time tracking
• Quality scores (0-100 scale)
• Complexity analysis for code
• Coverage estimation for tests
• Resource usage monitoring

• Input validation for all parameters
• Secure API key handling
• Error handling with detailed logging
• Rate limiting support
• Content filtering options

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Follow existing code style
5. Submit a pull request

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

The examples.py script demonstrates:

• ✅ 40+ customization options across all modules
• ✅ Multiple output formats and quality settings
• ✅ Batch processing capabilities
• ✅ Integration examples between modules
• ✅ Error handling and validation
• ✅ Performance optimization techniques

Run python examples.py to see all features in action!

This repository includes a comprehensive GitHub Actions workflow system:

• CI: Continuous integration testing across Python 3.8-3.11
• Code Quality: Automated linting, formatting, and style checks
• Security & Dependencies: Vulnerability scanning and dependency management
• Release: Automated package building and publishing
• Dependencies: Monthly automated dependency updates

• ✅ Multi-Python testing (3.8, 3.9, 3.10, 3.11)
• ✅ Code quality enforcement (Black, Flake8, isort, Pylint)
• ✅ Security scanning (Bandit, Safety, Semgrep, CodeQL)
• ✅ Automated releases with PyPI publishing
• ✅ Docker container support
• ✅ Dependency vulnerability tracking

• Trigger dependency updates: workflow_dispatch on Dependencies workflow
• Force security scan: workflow_dispatch on Security workflow
• Create release: Push a tag like v1.0.0

For questions, issues, or feature requests:

• Open an issue on GitHub
• Check the examples for usage patterns
• Review the comprehensive docstrings in each module

Note: This library includes mock implementations for demonstration. Replace API calls with actual service integrations for production use.