traini-ai 2.0.3

Traini AI SDK - Dog emotion analysis and human-dog communication platform

Traini AI - Dog Emotion Analysis SDK

Advanced AI-powered dog emotion detection and image analysis SDK combining deep learning emotion recognition with multi-model visual intelligence.

🌟 Features

• 🎯 High Accuracy: 76-88% emotion detection accuracy using 4-model ensemble
• 🤖 Multi-Model Analysis: Integrates GPT-4, Gemini 2.5, and Claude 3 for comprehensive visual understanding
• 📝 Dual Output: Professional third-person descriptions + playful first-person narratives
• ⚡ Fast & Reliable: Optimized inference pipeline with automatic image format detection
• 🔧 Easy to Use: Simple API with just 3 lines of code

📦 Installation

pip install traini-ai

🚀 Quick Start

from traini_ai import IntegratedImageAnalysisWithEmotion

# Initialize analyzer
analyzer = IntegratedImageAnalysisWithEmotion()

# Analyze dog image
result = analyzer.analyze_image('path/to/dog_image.jpg')

# Get results
print(result['third_person_description'])  # Professional analysis
print(result['first_person_description'])  # Fun dog perspective

📖 API Reference

IntegratedImageAnalysisWithEmotion

Main class for dog image analysis.

Parameters

• openai_api_key (str, optional): OpenAI API key. Uses default if not provided.
• google_api_key (str, optional): Google API key. Uses default if not provided.
• anthropic_api_key (str, optional): Anthropic API key. Uses default if not provided.
• use_ensemble (bool, default=True): Whether to use 4-model ensemble for emotion detection.

Methods

analyze_image(image_path: str) -> dict

Analyzes a dog image and returns emotional and behavioral insights.

Parameters:

• image_path (str): Path to the dog image file. Supports JPEG, PNG, GIF, WebP.

Returns:

{
    'third_person_description': str,  # Objective third-person analysis
    'first_person_description': str   # Playful first-person narrative
}

💡 Usage Examples

Basic Usage

from traini_ai import IntegratedImageAnalysisWithEmotion

analyzer = IntegratedImageAnalysisWithEmotion()
result = analyzer.analyze_image('happy_dog.jpg')

print("Third-Person Analysis:")
print(result['third_person_description'])
print("\nFirst-Person Narrative:")
print(result['first_person_description'])

Custom API Keys

analyzer = IntegratedImageAnalysisWithEmotion(
    openai_api_key='your-openai-key',
    google_api_key='your-google-key',
    anthropic_api_key='your-anthropic-key'
)

Single Model Mode (Faster)

# Use single model for faster inference
analyzer = IntegratedImageAnalysisWithEmotion(use_ensemble=False)
result = analyzer.analyze_image('dog.jpg')

📊 Performance

Mode	Accuracy	Speed	Memory	Use Case
Ensemble (4 models)	76-88%	~3-5s	~1.2GB	High accuracy needs
Single Model	74-76%	~1-2s	~300MB	Real-time applications

🎨 Output Examples

Third-Person Description (Professional)

The image features a fluffy golden Pomeranian dog situated indoors on a
textured gray rug within a cozy living room setting. The dog's voluminous
fur and distinctively perked ears emphasize its breed traits, while its
dark, expressive eyes convey a sense of curiosity and attentiveness. The
dog is facing the camera with its head held high, displaying a slightly
open mouth that suggests a playful or relaxed demeanor...

First-Person Description (Playful)

Omg, hi there! 😍 I'm just chillin' here on my favorite rug, feelin' all
cozy and stuff! My floofy fur is like, 10/10 fluffy for snuggles. 🐾 My
ears are perked 'cause I'm super curious about what you're up to! Maybe
you wanna throw me a toy or give me some treats? I promise I won't get
mad – just playful wags and happy barks! 🐶💖

🔬 Technology Stack

Emotion Detection

• AttentionResNet50: Advanced attention mechanism
• ResNet50: Robust feature extraction
• ResNet101: Deep architecture for complex patterns
• EfficientNet: Optimized efficiency and accuracy

Visual Analysis

• GPT-4o-mini: General visual understanding
• Gemini 2.5 Flash Lite: Fast image processing
• Claude 3 Haiku: Detailed emotion interpretation

🎯 Supported Emotions

The SDK can detect 13 different dog emotions:

• Happy
• Sad
• Angry
• Alert
• Relaxed
• Fear
• Anxiety
• Anticipation
• Appeasement
• Caution
• Confident
• Curiosity
• Sleepy

🖼️ Supported Image Formats

• JPEG/JPG
• PNG
• GIF
• WebP

⚙️ Requirements

• Python 3.8+
• PyTorch 1.9+
• OpenAI SDK
• Google Generative AI
• Anthropic SDK

📄 License

MIT License - see LICENSE file for details

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📞 Support

• Documentation: https://docs.traini.ai
• Issues: https://github.com/traini-ai/traini-sdk/issues
• Email: [e-mail]

🔄 Changelog

v2.0.0 (2024-12-14)

• ✨ Added multi-model integration (GPT, Gemini, Claude)
• ✨ Implemented 4-model ensemble emotion detection
• ✨ Dual output: third-person + first-person descriptions
• 🔧 Automatic image format detection
• 📝 Simplified API with clean output
• 🚀 Production-ready with error handling

v1.0.0 (2024-12-01)

• 🎉 Initial release
• 🤖 Single model emotion detection
• 📊 Basic image analysis

🙏 Acknowledgments

Built with advanced AI models from OpenAI, Google, and Anthropic.

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Made with ❤️ by the Traini AI Team