christofy 1.0.2

Christofy is a lightweight, modular PyTorch-based AI toolkit that unifies essential deep learning capabilities. It supports Image Classification, Segmentation, and advanced Vision Analysis with clean, easy-to-use APIs. With built-in Retrieval-Augmented Generation (RAG) powered by ChromaDB and Ollama, it seamlessly blends vision and language intelligence. Designed for flexibility and speed, Christofy is your all-in-one arsenal for building modern AI solutions.

Christofy 🧠📦

Christofy is a lightweight and modular PyTorch-based image classification package designed for both training and prediction. Built on top of ResNet18, it supports both binary and multi-class classification with clean abstractions and an easy-to-use API. Now featuring a powerful RAG (Retrieval-Augmented Generation) system and Vision Analysis capabilities with Ollama integration!

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🚀 Features

• ✅ Train image classifiers using CNN (ResNet18)
• ✅ Binary and multi-class classification support
• ✅ Predict the class of a single image using saved model
• ✅ Modular structure: clean separation between training and prediction
• ✅ RAG pipeline with Ollama integration for text analysis
• ✅ Support for multiple file formats (PDF, DOCX, CSV, Excel, TXT, JSON)
• ✅ Vision Analysis with multimodal AI models
• ✅ Conversation mode for interactive image analysis
• ✅ Batch processing for multiple images
• ✅ Graph-based image segmentation
• ✅ AutoML for tabular data with multiple algorithms
• ✅ Ready to be installed as a Python package

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📚 Usage

Image Classification

import christofy

# Train a model
christofy.train_model(
    data_dir="path/to/dataset",
    learning_rate=0.001,
    epochs=10,
    batch_size=32,
    output_dir="output"
)

# Make predictions
predicted_class = christofy.predict_image(
    model_path="output/model.pth",
    image_path="path/to/image.jpg"
)

RAG Pipeline

from christofy import run_rag_pipeline

# Example usage
response = run_rag_pipeline(
    data="path/to/documents",  # Can be file, directory, or text
    query="Your question here?",
    model="llama2",              # Your local Ollama model
    embedding_model="nomic-embed-text",  # For embeddings
    temperature=0.7
)

print(response)

Supported File Formats for RAG

• PDF files (requires PyPDF2 or pdfplumber)
• Word documents (DOCX) (requires python-docx)
• CSV and Excel files (requires pandas)
• Text files (TXT)
• JSON files

Vision Analysis

from christofy import vision_spell

# Basic usage
vision_spell("image.jpg", "What do you see?")

# With model selection
vision_spell("image.jpg", "What do you see?", model="llava:13b")

# Full parameters
vision_spell(
    image_path="image.jpg",
    question="What do you see?",
    model="llava",
    temperature=0.7,
    max_tokens=None,
    system_prompt=None,
    conversation_mode=False
)

# Conversation mode
chat = vision_spell("image.jpg", "What do you see?", conversation_mode=True)
chat.ask("What colors are present?")
chat.ask("Is this indoors or outdoors?")

# Export conversation
chat.export_conversation("json")
chat.export_conversation("txt")

# Batch processing
from christofy import batch_vision_analysis

batch_vision_analysis(
    image_paths=["img1.jpg", "img2.png"],
    questions="What do you see?",
    model="llava"
)

# Utility functions
from christofy import list_vision_models, get_system_info, show_module_info

# Alternative function name
from christofy import vision
vision("image.jpg", "What do you see?")

Image Segmentation

from christofy import segmenter

# Basic usage with default parameters
segmenter("image.jpg")

# With custom parameters
segmenter(
    image_path="image.jpg",
    sigma=0.5,           # Standard deviation for Gaussian smoothing
    merge_threshold=500,  # Threshold for merging segments
    min_size=50          # Minimum segment size
)

Machine Learning for Tabular Data

from christofy.ml import ml_trainer, ml_predictor

# Example 1: AutoML (automatic algorithm selection)
result1 = ml_trainer(
    data="data.csv",  # Replace with your CSV file
    target="target",  # Replace with your target column
    hyperparams={
        "test_size": 0.3,
        "cv": 5
    },
    save_path="automl_model.pkl"
)

# Example 2: User-specified algorithm
result2 = ml_trainer(
    data="data.csv",
    target="target",
    algorithm="random_forest",
    hyperparams={
        "test_size": 0.25,
        "cv": 10,
        "n_estimators": 200,
        "max_depth": 8
    },
    save_path="rf_model.pkl"
)

# Example 3: Make predictions
predictions = ml_predictor(
    model_path="automl_model.pkl",
    values=[[5.1, 3.5, 1.4, 0.2], [6.2, 2.8, 4.8, 1.8]]
)

Requirements

• Python 3.9+
• PyTorch
• Ollama (for RAG and Vision functionality)
• Pillow (for Vision functionality)
• scikit-image (for Segmentation functionality)
• matplotlib (for Segmentation visualization)
• scikit-learn (for ML functionality)
• pandas (for ML and CSV/Excel support)
• Additional dependencies based on file formats

Installation

pip install christofy

For PDF support:

pip install PyPDF2  # or pdfplumber

For DOCX support:

pip install python-docx

For CSV/Excel support:

pip install pandas openpyxl

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📝 License

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

🤝 Contributing

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