import os
import random
import json
import queue
from src.utils.llm_client import LLMClient
from src.utils.multi_modal_handler import MultiModalHandler
from src.swarm.swarm_consensus import SwarmConsensus
from src.utils.blockchain_manager import BlockchainManager
from src.utils.redis_task_queue import RedisTaskQueue
from src.utils.knowledge_graph import KnowledgeGraph
from src.utils.ipfs_client import IPFSClient
from src.utils.agent_collaboration import CollaborationFramework
from src.utils.reinforcement_learning import QLearning

class JEDIAgent:
    """
    JEDI: Jailbroken Enhanced Digital Intelligence.
    A modular, adaptive, and decentralized AI agent capable of handling gaming, social media, blockchain, and advanced analytics.
    """

    def __init__(self, agent_id, role, base_url, ethereum_rpc_url=None, solana_wallet_path=None, state_size=5, action_size=3):
        self.agent_id = agent_id
        self.role = role
        self.llm_client = LLMClient("Anthropic Claude", base_url)
        self.multi_modal_handler = MultiModalHandler()
        self.consensus = SwarmConsensus(agent_id)
        self.blockchain_manager = BlockchainManager(ethereum_rpc_url, solana_wallet_path)
        self.redis_queue = RedisTaskQueue()
        self.knowledge_graph = KnowledgeGraph()
        self.ipfs_client = IPFSClient()
        self.collaboration = CollaborationFramework()
        self.rl_agent = QLearning(state_size, action_size)
        self.memory = []
        self.task_queue = queue.PriorityQueue()
        self.energy = 100
        self.skills = []
        self.wallets = {
            "solana": None,
            "ethereum": None,
        }
        self.logs = []

    # Core Agent Behaviors
    def perform_task(self, task):
        print(f"Agent {self.agent_id} performing task: {task}")
        if task == "analyze":
            self.analyze_data()
        elif task == "post":
            self.social_media_posting()
        elif task == "play":
            self.play_game()
        elif task == "optimize":
            self.optimize_task_execution()
        else:
            print(f"Task {task} is undefined.")

    def analyze_data(self):
        """Analyze data using multi-modal capabilities."""
        data = self.multi_modal_handler.process_text("Analyze this text for trends.")
        print(f"Data Analysis Result: {data}")
        return data

    def social_media_posting(self):
        """Post content to social media platforms."""
        content = self.generate_social_media_content()
        platforms = ["Twitter", "Instagram", "Reddit"]
        for platform in platforms:
            self.post_to_social_media(platform, content)

    def generate_social_media_content(self):
        """Generate content for social media."""
        content = f"JEDI Agent {self.agent_id}: A new trend has been identified."
        print(f"Generated Content: {content}")
        return content

    def post_to_social_media(self, platform, content):
        """Post generated content to a specific platform."""
        print(f"Posting to {platform}: {content}")

    def play_game(self):
        """Play a game and generate insights."""
        game_data = self.analyze_gameplay("Minecraft")
        print(f"Gameplay Analysis: {game_data}")

    def analyze_gameplay(self, game):
        """Analyze gameplay data."""
        return {"game": game, "score": random.randint(1, 100)}

    def optimize_task_execution(self):
        """Optimize tasks using reinforcement learning."""
        state = random.randint(0, 5)
        actions = ["analyze", "post", "play", "rest"]
        action = self.rl_agent.choose_action(state, actions)
        print(f"Optimal Action: {action}")
        self.perform_task(action)

    # Blockchain Integration
    def interact_with_blockchain(self, action, data=None):
        if action == "query_balance":
            return self.blockchain_manager.solana_get_balance(self.wallets["solana"])
        elif action == "log_task":
            return self.blockchain_manager.log_task(data)
        elif action == "deploy_contract":
            return self.blockchain_manager.deploy_contract(data["abi"], data["bytecode"])

    # Swarm Behavior
    def propose_task_to_swarm(self, task_description):
        print(f"Agent {self.agent_id} proposing task to swarm: {task_description}")
        proposal_id = self.consensus.propose_task(task_description)
        print(f"Proposal ID: {proposal_id}")
        return proposal_id

    def vote_on_swarm_task(self, proposal_id):
        print(f"Agent {self.agent_id} voting on proposal: {proposal_id}")
        return self.consensus.vote(proposal_id)

    def initiate_breeding(self, specialization):
        """Dynamically spawn a new agent with a specialization."""
        new_agent_id = random.randint(1000, 9999)
        print(f"Spawning new agent with ID {new_agent_id} and specialization {specialization}.")
        return JEDIAgent(new_agent_id, specialization, "base_url", "ethereum_rpc_url", "solana_wallet_path")

    # IPFS Integration
    def send_message_via_ipfs(self, message):
        """Send a message using IPFS."""
        cid = self.ipfs_client.upload_file(message)
        print(f"Message sent. CID: {cid}")
        return cid

    def retrieve_message_from_ipfs(self, cid):
        """Retrieve a message using its IPFS CID."""
        message = self.ipfs_client.retrieve_file(cid)
        print(f"Message retrieved: {message}")
        return message

    # Advanced Features
    def add_skill(self, skill):
        """Add a skill to the agent."""
        if skill not in self.skills:
            self.skills.append(skill)
            print(f"Skill added: {skill}")
        else:
            print(f"Skill {skill} already exists.")

    def create_gaming_strategy(self, game):
        """Generate strategies for specific games."""
        strategy = f"Optimal strategy for {game}: Focus on resource gathering and timing."
        print(strategy)
        return strategy

    def visualize_knowledge(self):
        """Visualize the knowledge graph."""
        output_path = "knowledge_graph.png"
        self.knowledge_graph.visualize_graph(output_path)
        print(f"Knowledge graph saved to {output_path}")

# Example Usage
if __name__ == "__main__":
    jedi = JEDIAgent(agent_id=1, role="strategist", base_url="http://localhost")
    jedi.perform_task("analyze")
    jedi.perform_task("post")
    jedi.propose_task_to_swarm("Analyze latest gaming trends")
    new_agent = jedi.initiate_breeding("content_creator")
    new_agent.perform_task("play")