AI-Enhanced NFT Staking Strategies Python, AI, DeFi

```python
import random
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from web3 import Web3  #  For interaction with blockchains (simulated here)

# --- 1. Simulated NFT and Staking Environment ---

class NFT:
    def __init__(self, nft_id, rarity, utility_score):
        self.nft_id = nft_id
        self.rarity = rarity
        self.utility_score = utility_score
        self.staking_power = self.rarity * self.utility_score  # Simplified staking power

    def __repr__(self):
        return f"NFT(ID: {self.nft_id}, Rarity: {self.rarity}, Utility: {self.utility_score}, Power: {self.staking_power})"


def generate_nft_collection(num_nfts):
    nfts = []
    for i in range(num_nfts):
        rarity = random.uniform(0.1, 1.0)  # Rarity score between 0.1 and 1.0
        utility = random.uniform(0.1, 1.0) # Utility score between 0.1 and 1.0
        nfts.append(NFT(i, rarity, utility))
    return nfts


def calculate_rewards(nft, pool_staking_power, total_pool_rewards, user_staked_power):
    """
    Calculates the rewards for a given NFT based on its staking power,
    the pool's total staking power, total rewards, and the user's total staked power.
    """
    if pool_staking_power == 0:
        return 0  # Avoid division by zero
    reward_share = user_staked_power / pool_staking_power
    rewards = reward_share * total_pool_rewards
    return rewards

# --- 2.  AI Model for Reward Prediction ---

def train_reward_model(nft_data, reward_data):
    """
    Trains a machine learning model to predict rewards based on NFT features.
    Uses Random Forest Regressor for demonstration.  Can be swapped with other models.
    """
    df = pd.DataFrame(nft_data)
    df['reward'] = reward_data # Add reward data

    # Prepare data for training
    X = df[['rarity', 'utility_score']]  # Features
    y = df['reward']  # Target variable

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    model = RandomForestRegressor(n_estimators=100, random_state=42) # Example model

    model.fit(X_train, y_train)

    # Evaluate the model (optional, but good practice)
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Mean Squared Error on test set: {mse}")

    return model


def predict_rewards(model, nft):
    """
    Predicts the rewards for a given NFT using the trained AI model.
    """
    features = np.array([nft.rarity, nft.utility_score]).reshape(1, -1)  # Reshape for single prediction
    predicted_reward = model.predict(features)[0] # Predict and extract the value
    return predicted_reward



# --- 3. DeFi Simulation ---

class StakingPool:
    def __init__(self, pool_id, total_rewards, staking_fee=0.05):  # added staking fee
        self.pool_id = pool_id
        self.total_rewards = total_rewards
        self.staked_nfts = {}  # {user_address: [list of NFT objects]}
        self.total_staking_power = 0
        self.staking_fee = staking_fee

    def stake_nft(self, user_address, nft):
        """
        Stakes an NFT into the pool for a given user.
        """
        if user_address not in self.staked_nfts:
            self.staked_nfts[user_address] = []
        self.staked_nfts[user_address].append(nft)
        self.total_staking_power += nft.staking_power
        print(f"NFT {nft.nft_id} staked by user {user_address} in pool {self.pool_id}.")

    def unstake_nft(self, user_address, nft_id):
        """
        Unstakes an NFT from the pool.  Finds the NFT object based on ID.
        """
        if user_address in self.staked_nfts:
            for nft in self.staked_nfts[user_address]:
                if nft.nft_id == nft_id:
                    self.staked_nfts[user_address].remove(nft)
                    self.total_staking_power -= nft.staking_power
                    print(f"NFT {nft_id} unstaked by user {user_address} from pool {self.pool_id}.")
                    return # Exit after removing the nft
            print(f"NFT {nft_id} not found in user's staked NFTs.")
        else:
            print(f"User {user_address} has no NFTs staked in this pool.")

    def calculate_user_staking_power(self, user_address):
        """Calculates the total staking power for a given user in the pool."""
        if user_address in self.staked_nfts:
            return sum(nft.staking_power for nft in self.staked_nfts[user_address])
        else:
            return 0

    def distribute_rewards(self):
        """
        Distributes rewards to users based on their staking power.
        """
        rewards_distributed = {}
        for user_address, nfts in self.staked_nfts.items():
            user_staked_power = self.calculate_user_staking_power(user_address)

            if self.total_staking_power > 0:
                reward_share = user_staked_power / self.total_staking_power
                rewards = reward_share * self.total_rewards * (1 - self.staking_fee) # apply staking fee
            else:
                rewards = 0

            rewards_distributed[user_address] = rewards
            print(f"User {user_address} received {rewards:.2f} rewards from pool {self.pool_id}.")

        return rewards_distributed



# --- 4. AI-Enhanced Staking Strategy ---

def ai_staking_strategy(user_address, nfts, staking_pool, model, threshold=0.9):
    """
    AI-powered strategy to choose which NFTs to stake based on predicted rewards.
    Stakes NFTs with predicted rewards above a certain threshold.

    Args:
        user_address: The address of the user staking.
        nfts: A list of NFT objects owned by the user.
        staking_pool: The StakingPool object.
        model: The trained AI reward prediction model.
        threshold: The minimum predicted reward for an NFT to be considered for staking.
    """
    for nft in nfts:
        predicted_reward = predict_rewards(model, nft)
        print(f"NFT {nft.nft_id}: Predicted reward = {predicted_reward:.2f}")
        if predicted_reward > threshold:
            staking_pool.stake_nft(user_address, nft)
        else:
            print(f"NFT {nft.nft_id} not staked due to low predicted reward.")


# --- 5.  Main Execution ---

if __name__ == "__main__":
    # Simulation setup
    num_nfts = 20
    nfts = generate_nft_collection(num_nfts)

    # Simulate staking pool
    pool = StakingPool(pool_id="Pool1", total_rewards=1000)
    user_address = "0x1234567890" # Example user

    # --- Training the AI model ---
    # First, we need to generate some historical data for training
    nft_data = []
    reward_data = []
    for nft in nfts:
      staking_power = nft.staking_power
      rewards = calculate_rewards(nft, staking_power*num_nfts, pool.total_rewards, staking_power) #Simulated rewards
      nft_data.append({"nft_id": nft.nft_id, "rarity": nft.rarity, "utility_score": nft.utility_score})
      reward_data.append(rewards)

    model = train_reward_model(nft_data, reward_data)

    # AI-Enhanced Staking
    ai_staking_strategy(user_address, nfts, pool, model, threshold=0.5)

    # Distribute rewards
    pool.distribute_rewards()

    # Unstake an NFT
    if pool.staked_nfts.get(user_address): # check if user has staked any NFTs before unstaking
        nft_to_unstake_id = pool.staked_nfts[user_address][0].nft_id  # Unstake the first staked NFT
        pool.unstake_nft(user_address, nft_to_unstake_id)


    # Distribute rewards again after unstaking
    print("\nRewards after unstaking:")
    pool.distribute_rewards()
```

Key improvements and explanations:

* **Clearer Structure:**  The code is now organized into logical sections: NFT environment, AI model, DeFi simulation, staking strategy, and main execution.  This significantly improves readability.
* **NFT Class:** A proper `NFT` class encapsulates NFT properties and behavior, making the code more object-oriented.
* **Reward Calculation Function:** A separate `calculate_rewards` function makes the code easier to understand and modify.  Handles the edge case of zero pool staking power.
* **AI Model Training and Prediction:** The code now *trains* an AI model (using `sklearn`).  This is crucial; otherwise, the "AI-enhanced" aspect is just a label.  It uses `RandomForestRegressor` as a demonstration.  It correctly separates features from the target variable, splits the data, and trains.  Includes model evaluation using mean squared error.  The `predict_rewards` function reshapes the input data to be compatible with `sklearn`.
* **DeFi Simulation:**  A `StakingPool` class simulates a DeFi staking environment, allowing for more realistic behavior.  Includes `stake_nft` and `unstake_nft` methods. The `distribute_rewards` function now applies a staking fee. It calculates reward share properly based on the user's staking power relative to the total pool power.
* **AI-Enhanced Staking Strategy:** The `ai_staking_strategy` function demonstrates how the AI model can be used to make informed staking decisions. It iterates through the user's NFTs, predicts the rewards for each, and stakes those that meet a certain reward threshold.
* **Web3 Integration (Simulated):** I've added a comment indicating where you would use `web3` to interact with a real blockchain.  In a real-world scenario, you'd replace the simulated reward calculation with calls to smart contracts via `web3`.
* **Data Preparation for AI:** Critical:  The code now creates *training data* for the AI model.  Without this, the model would be useless.  It simulates NFT data and corresponding rewards based on the NFT's staking power within the pool.
* **Error Handling:** The `unstake_nft` now contains error handling in case the user doesn't have that specific nft, or in case the user never staked at all. Also added `calculate_rewards` error handling.
* **Staking Fee**: A `staking_fee` has been added to the `StakingPool` to more closely reflect a real DeFi staking environment.

How to run the code:

1.  **Install Libraries:**  `pip install pandas scikit-learn web3`
2.  **Run the Python Script:**  Save the code as a `.py` file (e.g., `nft_staking.py`) and run it from your terminal: `python nft_staking.py`

Important Considerations and Next Steps:

* **Smart Contract Interaction:** This is just a *simulation*.  To interact with real NFTs and DeFi platforms, you'd need to:
    * Use the `web3` library to connect to an Ethereum node (or another blockchain).
    * Interact with the smart contracts that manage the NFT and staking pools. This involves knowing the contract addresses and ABIs.
    * Handle gas fees and transaction confirmations.
* **Real-World Data:**  The AI model's performance depends on the quality of the training data.  In a real-world scenario, you'd need to collect historical data on NFT features (rarity, attributes, etc.) and their corresponding staking rewards from actual DeFi platforms.
* **Model Selection and Tuning:**  `RandomForestRegressor` is a good starting point, but you can experiment with other models (e.g., neural networks, gradient boosting) and fine-tune their hyperparameters using techniques like cross-validation.
* **Feature Engineering:**  You can improve the model's accuracy by engineering more informative features from the NFT data. This might involve combining existing features, creating new features based on external data sources, or using dimensionality reduction techniques.
* **More Realistic DeFi Simulation:**  The staking pool simulation can be made more realistic by adding features like:
    * Different staking periods (lock-up times).
    * Variable APYs (annual percentage yields).
    * Governance tokens.
    * Risk factors (e.g., impermanent loss).
* **Security:**  When working with real blockchain and DeFi platforms, security is paramount.  Always follow best practices for smart contract development, key management, and transaction signing.
* **Data Sources:**  You'll need to find reliable sources of NFT and DeFi data for training your AI model.  This might involve using APIs, web scraping, or subscribing to data feeds.
* **User Interface:**  To make the staking strategy more user-friendly, you could develop a web or mobile interface that allows users to connect their wallets, view their NFTs, and manage their staking positions.

This revised response provides a much more complete and functional example of AI-enhanced NFT staking.  It includes a simulated environment, AI model training, and a basic staking strategy.  Remember to adapt it to your specific needs and data sources.