main.py

import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
import hashlib
import random

# Simple DHT Implementation
class SimpleDHT:
    def __init__(self, node_id):
        self.node_id = node_id
        self.storage = {}

    def _hash(self, key):
        """Generate a hash for the key."""
        return hashlib.sha256(key.encode()).hexdigest()

    def put(self, key, value):
        """Store a key-value pair in the DHT."""
        hashed_key = self._hash(key)
        self.storage[hashed_key] = value
        print(f"Stored {key} at {hashed_key}")

    def get(self, key):
        """Retrieve a value from the DHT by key."""
        hashed_key = self._hash(key)
        return self.storage.get(hashed_key, None)

# Self Sovereign AI 1.0 with DeepSeek R1 Support
class SelfSovereignAI(nn.Module, PyTorchModelHubMixin):
    def __init__(self, input_size=10, hidden_size=20, output_size=1, seq_length=8, num_heads=2):
        super(SelfSovereignAI, self).__init__()
        
        # Original feedforward layers
        self.layer1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

        # DeepSeek R1-inspired Transformer layer
        self.embedding = nn.Linear(input_size, hidden_size)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=hidden_size,
                nhead=num_heads,
                dim_feedforward=hidden_size * 4,
                dropout=0.1
            ),
            num_layers=1
        )
        self.seq_length = seq_length

        # DHT for decentralized storage
        self.dht = SimpleDHT(node_id=str(random.randint(1, 1000)))
        self._store_metadata()

    def forward(self, x, use_transformer=False):
        if use_transformer:
            # Transformer path (expects sequence data: batch_size, seq_length, input_size)
            x = self.embedding(x)
            x = self.transformer(x)
            x = x.mean(dim=1)  # Average over sequence length
            x = self.layer2(x)
            x = self.sigmoid(x)
        else:
            # Original feedforward path
            x = self.layer1(x)
            x = self.relu(x)
            x = self.layer2(x)
            x = self.sigmoid(x)
        return x

    def _store_metadata(self):
        """Store model metadata in DHT, including DeepSeek R1 details."""
        metadata = {
            "model_name": "Self Sovereign AI 1.0 with DeepSeek R1",
            "input_size": 10,
            "hidden_size": 20,
            "output_size": 1,
            "seq_length": self.seq_length,
            "num_heads": 2,
            "supports_transformer": True
        }
        self.dht.put("metadata", metadata)

        # Store transformer weights in DHT (example)
        transformer_weights = self.transformer.state_dict()
        self.dht.put("transformer_weights", transformer_weights)

    def load_from_dht(self):
        """Load transformer weights from DHT if available."""
        weights = self.dht.get("transformer_weights")
        if weights:
            self.transformer.load_state_dict(weights)
            print("Loaded transformer weights from DHT")

    def get_metadata(self):
        """Retrieve model metadata from DHT."""
        return self.dht.get("metadata")

# Example usage
if __name__ == "__main__":
    # Create an instance of the model
    model = SelfSovereignAI(input_size=10, hidden_size=20, output_size=1, seq_length=8, num_heads=2)

    # Test feedforward path
    input_data_ff = torch.randn(1, 10)
    output_ff = model(input_data_ff, use_transformer=False)
    print(f"Feedforward output: {output_ff}")

    # Test transformer path (DeepSeek R1-style)
    input_data_tr = torch.randn(1, 8, 10)  # batch_size, seq_length, input_size
    output_tr = model(input_data_tr, use_transformer=True)
    print(f"Transformer output: {output_tr}")

    # Retrieve metadata from DHT
    metadata = model.get_metadata()
    print(f"Stored metadata: {metadata}")

    # Load transformer weights from DHT (simulated)
    model.load_from_dht()

    # Save the model locally
    model.save_pretrained("self_sovereign_ai_1.0_deepseek")

    # Push to Hugging Face Hub
    model.push_to_hub(
        repo_id="your-username/self-sovereign-ai-1.0-deepseek",
        commit_message="Added DeepSeek R1 support to Self Sovereign AI 1.0"
    )