Animals Classifier 🐾

This project combines computer vision and natural language processing to identify animals from images, then enriches the identification with detailed information from Wikipedia. It's a practical exploration of modern AI capabilities wrapped in a simple, user-friendly interface.

Architecture Overview

The system operates through three core components:

• Vision Recognition: Uses OpenAI's multimodal capabilities to identify animals in uploaded images
• Knowledge Retrieval: Leverages LlamaIndex to fetch and process relevant Wikipedia articles
• Interactive Interface: Presents findings through a clean Gradio UI

Getting Started

Prerequisites

You'll need the following on your development machine:

• Python 3.12+ (earlier versions might work but weren't tested)
• pip (for package management)
• virtualenv (for isolated environments)

Installation & Setup

# Clone this repository
git clone 

# Navigate to project directory
cd animals-classifier

# Set up virtual environment
python -m venv venv

# Activate environment
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set your OpenAI API key
export OPENAI_API_KEY="sk-..."  # On Windows: set OPENAI_API_KEY=sk-...

# Launch the application
python main.py

Once running, open your browser and navigate to http://127.0.0.1:7860 to interact with the classifier.

Technical Implementation

The project is structured around three main Python modules:

• agent.py: Implements the Wikipedia interaction layer using LlamaIndex
• classifier.py: Handles image processing and OpenAI API integration
• main.py: Provides the Gradio interface and application entry point

Testing Resources

The images directory contains sample animal images from Unsplash (free license) for testing the classifier's capabilities.

Tech Stack

The project leverages several powerful technologies:

• OpenAI: For multimodal vision and language capabilities
• LlamaIndex: For structured retrieval from Wikipedia
• Gradio: For rapid interface development
• Pillow: For image processing

Future Improvements

Potential enhancements to consider:

• Caching previously processed animals to reduce API costs
• Expanding beyond Wikipedia to other knowledge sources
• Adding capability to compare multiple animals
• Implementing offline recognition for common species

Documentation References

• LlamaIndex VectorStoreIndex
• OpenAI Embeddings
• WikipediaReader

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This project represents a practical intersection of multiple AI capabilities - image recognition, knowledge retrieval, and natural language processing. While relatively simple in scope, it demonstrates how powerful AI interfaces can be created with surprisingly little code.