dehug 0.2.1

Decentralized Hugging Face - AI models and datasets from IPFS/Filecoin

DeHug - Decentralized Hugging Face

DeHug is a decentralized platform for AI models and datasets built on IPFS/Filecoin, providing a decentralized alternative to traditional centralized AI model repositories.

Features

• 🌐 Decentralized Storage: Models and datasets stored on IPFS/Filecoin
• 🔒 Censorship Resistant: No single point of failure or control
• 🚀 Easy Integration: Simple Python SDK compatible with existing workflows
• 📊 Data Loading: Support for JSON, CSV, text, and binary formats
• 🔍 Search: Search and discover models and datasets
• 📋 Metadata: Rich metadata support for models and datasets

Installation

pip install dehug

Quick Start

Loading Datasets

from dehug import DeHug

client = DeHug()

# Load a CSV dataset
dataset = client.load_dataset("QmYourDatasetCID", format_hint="csv")
print(dataset.head())

# Load a JSON dataset
data = client.load_dataset("QmYourJSONDataCID", format_hint="json")
print(data)

# Load by name (if registered in DeHug registry)
dataset = client.load_dataset("my-dataset-name", format_hint="csv")

Working with Models

# Load model metadata
model_info = client.load_model("QmYourModelCID")
print(f"Model: {model_info['name']}")
print(f"Task: {model_info['task']}")
print(f"Description: {model_info['description']}")

# Download model files locally
model_path = client.download_model("QmYourModelCID", "./my_models")
print(f"Model downloaded to: {model_path}")

Listing and Searching

# List all available datasets
datasets = client.list_datasets()
for dataset in datasets:
    print(f"{dataset['name']}: {dataset['description']}")

# List all available models
models = client.list_models()
for model in models:
    print(f"{model['name']}: {model['task']}")

# Search for datasets
results = client.search_datasets("sentiment analysis")
print(f"Found {len(results)} datasets")

# Search for models
results = client.search_models("text classification")
print(f"Found {len(results)} models")

CLI Usage

DeHug includes a command-line interface for easy access:

# Load and display a dataset
dehug dataset QmYourDatasetCID --format csv

# Save dataset to file
dehug dataset QmYourDatasetCID --format csv --output dataset.csv

# Load model metadata
dehug model QmYourModelCID

# Download model files
dehug model QmYourModelCID --download ./models

# List available datasets
dehug list datasets

# List available models
dehug list models

# Search for datasets
dehug search datasets "machine learning"

# Search for models
dehug search models "nlp"

Configuration

You can customize the IPFS gateway and API endpoints by passing a config dictionary:

config = {
    'ipfs_gateway': 'https://your-ipfs-gateway.com/ipfs',
    'contract_api': 'https://your-api.com',
    'request_timeout': 60
}

client = DeHug(config=config)

Default configuration:

• IPFS Gateway: https://ipfs.io/ipfs
• Contract API: https://api.dehug.io
• Request Timeout: 30 seconds

Supported Formats

DeHug automatically detects and handles various data formats:

• JSON: Parsed into Python dictionaries
• CSV: Loaded as pandas DataFrames
• Text: Returned as strings
• Binary: Returned as bytes

You can also specify format hints for better performance:

# Explicitly specify format
csv_data = client.load_dataset("QmDataCID", format_hint="csv")
json_data = client.load_dataset("QmDataCID", format_hint="json")
text_data = client.load_dataset("QmDataCID", format_hint="text")
binary_data = client.load_dataset("QmDataCID", format_hint="binary")

Error Handling

DeHug provides specific exceptions for better error handling:

from dehug import DeHug, DeHugError, NetworkError, DatasetNotFoundError

client = DeHug()

try:
    dataset = client.load_dataset("nonexistent-dataset")
except DatasetNotFoundError:
    print("Dataset not found")
except NetworkError:
    print("Network connection failed")
except DeHugError as e:
    print(f"DeHug error: {e}")

API Reference

DeHug Client

• load_dataset(name_or_cid, format_hint=None): Load dataset from IPFS
• load_model(name_or_cid): Load model metadata
• download_model(name_or_cid, download_dir="./models"): Download model files
• list_datasets(): List available datasets
• list_models(): List available models
• search_datasets(query): Search datasets by query
• search_models(query): Search models by query

Utility Functions

• load_dataset_from_cid(cid, format_hint=None): Direct dataset loading from CID
• load_content_from_cid(cid, format_hint=None): Load any content from CID

Architecture

DeHug consists of two main components:

1. Contract API: Metadata registry for models and datasets
2. IPFS Storage: Decentralized storage for actual model/data files

The SDK provides a seamless interface to both layers, allowing you to work with decentralized AI assets as easily as with traditional centralized repositories.

Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature