Unlock the Power of AI-Optimized Decentralized Storage.
Empower developers to build smarter, secure, and efficient decentralized apps (DApps) with our cutting-edge AI Storage SDK.
The AI Storage SDK is an open-source, developer-friendly toolkit designed to revolutionize decentralized storage solutions. By combining AI-driven optimization with the scalability of blockchain, it enables developers to build advanced DApps with seamless file management, security, and storage cost efficiency.
Key Features:
AI-Powered File Compression
Optimize Storage Allocation: Reduce file sizes intelligently using AI-based predictions for optimal compression techniques.
Support for Multiple File Types: Includes dedicated algorithms for image files and general data files.
Advanced Security
Anomaly Detection: Protect your storage systems with AI-powered monitoring, flagging unusual upload behaviors to ensure secure data management.
Malware Prevention: Detect and block suspicious file uploads before they impact your system.
Cost Prediction
AI-Driven Insights: Predict future storage costs based on file size, redundancy, and historical data.
Plan and Budget Effectively: Enable cost optimization with predictive modeling.
Workflow Diagram:
File Input: Upload raw files (e.g., images, documents).
Compression: The SDK reduces file sizes intelligently with AI-powered algorithms.
Security Check: Files pass through anomaly detection to identify suspicious behaviors.
Cost Prediction: Estimate costs based on file parameters and storage requirements.
Storage: Files are stored securely, ready for retrieval or further processing.
Ready to start building with StorX's AI storage SDK?
Why Choose Our SDK?
Ease of Use: Works out of the box with a simple setup process.
Customizable: Modify AI models, compression techniques, and storage logic to suit your app’s needs.
Open Source: Join the community, contribute, and adapt the SDK for your specific use case.stored securely, ready for retrieval or further processing.
D
For developers building on Solana
Integrate the SDK to optimize storage and retrieval for decentralized applications, enhancing performance while reducing costs
Companies and Enterprises.
Leverage AI-driven cost prediction and security to manage large-scale file storage securely and efficiently
Research and experimentation.
Experiment with AI-powered file management and anomaly detection for academic or innovation purposes.
What is the AI storage SDK designed for?
Integrate the SDK to optimize storage and retrieval for decentralized applications, enhancing performance while reducing costs
Can I integrate it with Solana smart contracts?
Yes! The SDK is designed to be extended with Solana smart contracts for managing decentralized storage payments and operations
Do I need prior AI experience to use this SDK?
No! The SDK provides pre-trained models and simple APIs. Advanced users can retrain models for their specific use cases
Is the SDK free to use?
Yes! It’s open-source and freely available on GitHub. If you would like more expansive options and additional SDKs to build native Dapps utilizing StorX AI.
Project Structure
plaintext
solana_ai_storage_sdk/
├── src/
│ ├── ai_compression.py # AI-powered file compression using advanced models
│ ├── ai_security.py # Anomaly detection for file security using advanced models
│ ├── ai_cost_prediction.py # AI-based cost prediction using advanced models
│ ├── solana_smart_contracts/ # Pre-written Rust contracts for Solana
│ │ ├── storage_program/ # Solana smart contract (Rust)
│ │ └── instructions.md # Deployment guide for Solana program
│ ├── solana_integration/ # TypeScript scripts for Solana
│ │ ├── client.ts # Solana wallet and contract connection
│ │ └── setup_env.md # Solana environment setup guide
│ └── requirements.txt # Python dependencies
├── models/ # Directory for pre-trained models
│ ├── ai_compression_advanced.pkl
│ ├── ai_security_advanced.pkl
│ ├── ai_cost_prediction_advanced.pkl
├── README.md # High-level SDK instructions
├── features_and_best_practices.txt # Key features and troubleshooting
└── LICENSE # Open-source license
2. AI Modules
ai_compression.py (AI-Powered File Compression)
python
import joblib
import numpy as np
from PIL import Image
class AICompression:
def __init__(self):
# Load the pre-trained compression model
self.model = joblib.load('models/ai_compression_advanced.pkl')
def compress_file(self, input_file: str, file_type: str, output_file: str) -> str:
if file_type == "image":
# Compress image file using the pre-trained model
image = Image.open(input_file)
image_data = np.array(image).flatten().reshape(1, -1)
compressed_data = self.model.transform(image_data)
compressed_image = compressed_data.reshape(image.size[0], image.size[1])
Image.fromarray(compressed_image).save(output_file)
return output_file
else:
raise ValueError("Unsupported file type for compression.")
Ai compression example:
python
from src.ai_compression import AICompression
compression = AICompression()
compressed_file = compression.compress_file("example.png", "image", "compressed_example.jpg")
print(f"Compressed file saved to: {compressed_file}")
ai_security.py (Anomaly Detection for File Security)
python
import joblib
import numpy as np
class AISecurity:
def __init__(self):
# Load the pre-trained security model (anomaly detection)
self.model = joblib.load('models/ai_security_advanced.pkl')
def analyze_file(self, file_path: str) -> bool:
# Example: Analyze file by checking its characteristics (hashes, sizes, etc.)
file_data = np.random.rand(10) # Placeholder for actual file feature extraction
prediction = self.model.predict([file_data])
return prediction[0] == -1 # Return True if anomaly is detected
Ai Security example:
python
from src.ai_security import AISecurity
security = AISecurity()
is_anomaly = security.analyze_file("example.png")
print(f"File Anomaly Detected: {is_anomaly}")
3. Storage Cost Prediction Module (ai_cost_prediction.py)
This module predicts storage costs based on historical data and redundancy needs.
python
import joblib
import numpy as np
class AICostPrediction:
def __init__(self):
# Load the pre-trained cost prediction model
self.model = joblib.load('models/ai_cost_prediction_advanced.pkl')
def predict_cost(self, storage_size: float) -> float:
# Predict cost based on storage size using the pre-trained model
X = np.array([[10], [100], [1000]]) # Example data: storage size in GB
self.model.fit(X, [1, 10, 100]) # Fit with some example data for prediction
predicted_cost = self.model.predict([[storage_size]])
return predicted_cost[0]
Ai Security example:
python
from src.ai_security import AISecurity
security = AISecurity()
is_anomaly = security.analyze_file("example.png")
print(f"File Anomaly Detected: {is_anomaly}")
4. Solana Smart contracts
storage_program/src/lib.rs (Solana Smart Contract in Rust)
This is a simple example of a Rust smart contract for storing file metadata on Solana.
rust
use solana_program::{
account_info::AccountInfo, entrypoint, entrypoint::ProgramResult, msg, pubkey::Pubkey,
};
entrypoint!(process_instruction);
fn process_instruction(
_program_id: &Pubkey,
_accounts: &[AccountInfo],
_instruction_data: &[u8],
) -> ProgramResult {
msg!("Processing file metadata...");
// Example: Store file metadata logic here
Ok(())
}
Instructions.md (Solana smart contract deployment guide)
Install Solana CLI tools
Follow the guide here: Solana CLI Setup
Build the smart contract
From the storage_program directory:
bash
cargo build-bpf
Deploy the contract
deploy it to Solana's devnet (or mainnet if ready)
bash
solana program deploy ./target/deploy/storage_program.so
Program ID
After deployment you'll receive a Program ID. Use this in the client.ts script for the integration.
5. Solana integration with typescript
client.ts (Solana Client Script for Metadata Storage)
This TypeScript script allows interaction with the deployed Solana program to store file metadata.
typescript
import { Connection, PublicKey, Transaction, SystemProgram } from "@solana/web3.js";
const connection = new Connection("https://api.devnet.solana.com");
const programId = new PublicKey("YOUR_PROGRAM_ID"); // Replace with your deployed program ID
async function storeFileMetadata(fileHash: string) {
const transaction = new Transaction().add(
SystemProgram.transfer({
fromPubkey: YOUR_WALLET_PUBLIC_KEY,
toPubkey: programId,
lamports: 1000, // Example fee for file registration
})
);
const signature = await connection.sendTransaction(transaction, [YOUR_WALLET_KEYPAIR]);
console.log("Transaction sent:", signature);
}
setup_env.md (Solana environment setup guide)
Install Node.js
Download and install Node.js from:
Node.js Official Website.
Install Solana Web3.js library
Install the Solana Web3.js library:
bash
npm install @solana/web3.js
Solana CLI Setup
Install Solana CLI tools:
bash
curl -sSf https://release.solana.com/stable/install > solana_install.sh && sh solana_install.sh
Set up Solana Devnet (or mainnet) for testing:
bash
solana config set --url https://api.devnet.solana.com
Deploy Solana program
Follow the
storage_program/instructions.md to deploy your Solana program.
bash
pip install -r requirements.txt
6. Requirements.txt (python dependencies)
client.ts (Solana Client Script for Metadata Storage)
This TypeScript script allows interaction with the deployed Solana program to store file metadata.
Setup instructions for developers
Clone the SDK repository
bash
git clone https://github.com/your-repo/solana-ai-storage-sdk.git
cd solana-ai-storage-sdk
Install python dependencies
bash
pip install -r requirements.txt
This will ensure that the necessary packages for AI compression, anomaly detection, cost prediction, and handling model files are installed.
Follow Solana integration setup:
refer to solana_integration/setup_env.md for setting up Solana and deploying the smart contract.
*** IMPORTANT***
pre trained models are not offered as downloads in .pkl format. Instead, we provide a starting point for your AI models in which you can customize and adjust as needed. AI model scripts will generate a .pkl once executed based on data points they are trained on. Although our pre trained models can be used as is, it is highly recommended that you tailor these models and train them on data directly based on your specific needs for the Dapp you are creating utilizing our SDK.
Ai compression model (.pkl)
python
import joblib
import numpy as np
from sklearn.decomposition import PCA
from PIL import Image
class AICompression:
def __init__(self):
# Example: Train PCA model (you can replace this with autoencoder or more complex models)
self.pca = PCA(n_components=10)
def train(self, image_data: np.ndarray):
# Train the PCA model on image data
self.pca.fit(image_data)
def compress(self, image_data: np.ndarray):
# Compress the image using the trained model
return self.pca.transform(image_data)
def save_model(self, filename: str):
# Save the trained model to a .pkl file
joblib.dump(self.pca, filename)
# Example usage
image_data = np.random.rand(100, 100) # Dummy data, replace with actual image data
compression_model = AICompression()
compression_model.train(image_data)
compression_model.save_model('models/ai_compression_advanced.pkl')
In this example, replace image_data with real data. (e.g, flatten images to a 2D array). This will create a
ai_compression_advanced.pkl
file in the models/ directory.
Ai security model (.pkl)
python
import joblib
import numpy as np
from sklearn.ensemble import IsolationForest
class AISecurity:
def __init__(self):
# Example: Train Isolation Forest model (you can replace with more advanced models)
self.model = IsolationForest()
def train(self, data: np.ndarray):
# Train the anomaly detection model
self.model.fit(data)
def predict(self, data: np.ndarray):
# Predict anomalies using the trained model
return self.model.predict(data)
def save_model(self, filename: str):
# Save the trained model to a .pkl file
joblib.dump(self.model, filename)
# Example usage
file_data = np.random.rand(100, 10) # Dummy data, replace with actual file features
security_model = AISecurity()
security_model.train(file_data)
security_model.save_model('models/ai_security_advanced.pkl')
In this case, file_data represents features extracted from files, such as hashes or byte-level statistics. This will create an
ai_security_advanced.pkl
file in the models/ directory.
AI cost prediction model (.pkl)
python
import joblib
import numpy as np
from sklearn.ensemble import IsolationForest
class AISecurity:
def __init__(self):
# Example: Train Isolation Forest model (you can replace with more advanced models)
self.model = IsolationForest()
def train(self, data: np.ndarray):
# Train the anomaly detection model
self.model.fit(data)
def predict(self, data: np.ndarray):
# Predict anomalies using the trained model
return self.model.predict(data)
def save_model(self, filename: str):
# Save the trained model to a .pkl file
joblib.dump(self.model, filename)
# Example usage
file_data = np.random.rand(100, 10) # Dummy data, replace with actual file features
security_model = AISecurity()
security_model.train(file_data)
security_model.save_model('models/ai_security_advanced.pkl')
This will save an
ai_security_advanced.pkl
file in the models/ directory.
Once you've trained / saved the models using the code above, your models/ directory will contain:
plaintext
models/
├── ai_compression_advanced.pkl
├── ai_security_advanced.pkl
├── ai_cost_prediction_advanced.pkl
If there are any issues with the file directory path saving correctly, simply import the .pkl files into the directory as shown.
Dapp Example
This Solana-powered decentralized application (DApp) integrates blockchain, AI, and decentralized storage to offer a seamless solution for file storage, ownership, and payment. Users can upload files, pay storage fees via Solana wallets, and receive a unique NFT representing ownership of their uploaded files. The platform leverages advanced AI for file optimization and IPFS for decentralized storage, ensuring security, transparency, and scalability.
Key Features
AI-Driven File Optimization
The platform uses pre-trained AI models to compress files, reduce storage requirements, and enhance upload efficiency.
Decentralized Storage on IPFS
Files are stored securely on IPFS, providing a robust and censorship-resistant storage solution.
Solana Wallet Integration
Users pay storage fees in SOL through their Solana wallets, creating a transparent and seamless payment experience.
NFT-Based Ownership
Each uploaded file is linked to an NFT minted on the Solana blockchain, granting the user verifiable and immutable ownership.
How It Works
File Upload: Users upload their files through the platform. The files are compressed and analyzed using AI models before being uploaded to IPFS.
Payment: Storage costs are calculated, and users make payments directly from their Solana wallets.
NFT Minting: After the file is uploaded, an NFT is minted to represent ownership of the file and its metadata.
Access and Management: Users can view their stored files and corresponding NFTs through a dashboard.
Benefits
Decentralization: Files are stored on IPFS, ensuring no central authority controls user data.
Ownership and Security: NFTs provide cryptographic proof of ownership, and Solana ensures tamper-proof transactions.
Cost Efficiency: AI compression minimizes storage requirements, reducing overall costs for users.
This DApp is ideal for individuals and businesses looking for a secure, decentralized, and AI-optimized solution for file storage and ownership management.
Project Structure
plaintext
solana_file_storage_dapp/
├── frontend/
│ ├── index.html # User interface
│ ├── app.js # Frontend logic
│ ├── style.css # Styling
├── backend/
│ ├── app.py # Backend API for SDK and Solana
│ ├── requirements.txt # Python dependencies
│ └── sdk/ # Integrated SDK (copy SDK here)
├── solana_integration/
│ ├── mint_nft.ts # NFT minting script
│ └── wallet.ts # Wallet integration logic
├── README.md # DApp documentation
└── LICENSE # Open-source license
Frontend
index.html
html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Decentralized File Storage</title>
<link rel="stylesheet" href="style.css">
</head>
<body>
<h1>Decentralized File Storage</h1>
<form id="file-upload-form">
<label for="wallet">Wallet Address:</label>
<input type="text" id="wallet" required>
<label for="file">Upload File:</label>
<input type="file" id="file" required>
<button type="submit">Upload</button>
</form>
<div id="output"></div>
<script src="app.js"></script>
</body>
</html>
app.js
javascript
document.getElementById("file-upload-form").addEventListener("submit", async (e) => {
e.preventDefault();
const walletAddress = document.getElementById("wallet").value;
const file = document.getElementById("file").files[0];
const formData = new FormData();
formData.append("file", file);
try {
// Upload file to backend
const response = await fetch("http://localhost:5000/upload", {
method: "POST",
body: formData,
});
const result = await response.json();
// Mint NFT for the uploaded file
const nftResponse = await fetch("http://localhost:5000/mint_nft", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
fileHash: result.ipfs_hash,
walletAddress: walletAddress,
}),
});
const nftResult = await nftResponse.json();
document.getElementById("output").innerText = JSON.stringify(nftResult, null, 2);
} catch (error) {
console.error("Error:", error);
}
});
style.css
css
body {
font-family: Arial, sans-serif;
padding: 20px;
background-color: #f5f5f5;
}
form {
margin-bottom: 20px;
}
label {
display: block;
margin-bottom: 5px;
}
input, button {
margin-bottom: 10px;
}
button {
padding: 5px 10px;
cursor: pointer;
}
#output {
background: #fff;
padding: 10px;
border: 1px solid #ccc;
}
Backend
app.py
python
from flask import Flask, request, jsonify
import ipfshttpclient
app = Flask(__name__)
# Upload file to IPFS
def upload_to_ipfs(file_path):
client = ipfshttpclient.connect()
result = client.add(file_path)
return result["Hash"]
# Endpoint to handle file uploads
@app.route("/upload", methods=["POST"])
def upload_file():
file = request.files.get("file")
if not file:
return jsonify({"error": "No file provided"}), 400
file_path = f"./uploads/{file.filename}"
file.save(file_path)
ipfs_hash = upload_to_ipfs(file_path)
return jsonify({"message": "File uploaded successfully!", "ipfs_hash": ipfs_hash})
# Endpoint to mint NFT
@app.route("/mint_nft", methods=["POST"])
def mint_nft():
data = request.json
file_hash = data.get("fileHash")
wallet_address = data.get("walletAddress")
if not file_hash or not wallet_address:
return jsonify({"error": "Missing fileHash or walletAddress"}), 400
nft_address = "SimulatedNFTAddress123456" # Replace with actual minting logic
return jsonify({"message": "NFT minted successfully!", "nft_address": nft_address})
if __name__ == "__main__":
app.run(debug=True)
requirements.txt
plaintext
Flask==2.1.1
flask-cors==3.0.10
ipfshttpclient==0.8.0a2
joblib==1.3.0
numpy==1.24.0
Pillow==9.5.0
scikit-learn==1.3.0
SDK
SDK
StorX software development kit.
Solana Integration
Use the Solana smart contract provided in the SDK. Refer to the SDK’s solana_smart_contracts/instructions.md for deploying the smart contract. Update the store_file_metadata function in the backend to include your deployed program ID
mint_nft.ts
typescript
import { Connection, Keypair } from "@solana/web3.js";
import { Metaplex, keypairIdentity, bundlrStorage } from "@metaplex-foundation/js";
const connection = new Connection("https://api.devnet.solana.com");
const metaplex = Metaplex.make(connection)
.use(keypairIdentity(Keypair.generate()))
.use(bundlrStorage());
export async function mintFileOwnershipNFT(fileHash: string, ownerPublicKey: string) {
const metadata = {
name: "File Ownership NFT",
symbol: "FILE",
description: `Ownership of the file with hash: ${fileHash}`,
image: "https://via.placeholder.com/150",
properties: {
files: [{ uri: `https://ipfs.io/ipfs/${fileHash}`, type: "application/octet-stream" }],
},
};
const nft = await metaplex.nfts().create({
uri: `https://ipfs.io/ipfs/${fileHash}`,
name: metadata.name,
symbol: metadata.symbol,
sellerFeeBasisPoints: 0,
creators: [{ address: ownerPublicKey, verified: true, share: 100 }],
});
console.log("NFT minted:", nft.mintAddress.toString());
return nft.mintAddress.toString();
}
wallet.ts
typescript
import { Connection, PublicKey, Transaction, SystemProgram } from "@solana/web3.js";
const connection = new Connection("https://api.devnet.solana.com");
export async function chargeForStorage(userPublicKey: string, cost: number) {
const userKey = new PublicKey(userPublicKey);
const transaction = new Transaction().add(
SystemProgram.transfer({
fromPubkey: userKey,
toPubkey: new PublicKey("RECIPIENT_WALLET_ADDRESS"), // Replace with your wallet address
lamports: cost * 1_000_000_000,
})
);
const signature = await connection.sendTransaction(transaction, []);
console.log("Transaction signature:", signature);
return signature;
}
Additional Notes.
If you'd like to test build this example please do the following:
Replace placeholders like
"RECIPIENT_WALLET_ADDRESS"and"SimulatedNFTAddress123456"with actual implementations for your project.Ensure the environment is correctly set up (IPFS, Solana wallet, Metaplex SDK, etc.).
StorX AI
copyright 2024 - all rights reserved.