import asyncio
import json
import logging
import os
from typing import List, Dict, Any
from cryptography.fernet import Fernet
from botbuilder.core import StatePropertyAccessor, TurnContext
from botbuilder.dialogs import Dialog, DialogSet, DialogTurnStatus
from dialog_helper import DialogHelper
import aiohttp
import speech_recognition as sr
from PIL import Image
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

# Ensure nltk is installed and download required data
try:
    import nltk
    from nltk.tokenize import word_tokenize
    nltk.download('punkt', quiet=True)
except ImportError:
    import subprocess
    import sys
    subprocess.check_call([sys.executable, "-m", "pip", "install", "nltk"])
    import nltk
    from nltk.tokenize import word_tokenize
    nltk.download('punkt', quiet=True)

# Import perspectives
from perspectives import (
    Perspective, NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective,
    PsychologicalPerspective
)

# Load environment variables
from dotenv import load_dotenv
load_dotenv()

# Setup Logging
def setup_logging(config):
    if config.get('logging_enabled', True):
        log_level = config.get('log_level', 'DEBUG').upper()
        numeric_level = getattr(logging, log_level, logging.DEBUG)
        logging.basicConfig(
            filename='universal_reasoning.log',
            level=numeric_level,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
    else:
        logging.disable(logging.CRITICAL)

# Load JSON configuration
def load_json_config(file_path):
    if not os.path.exists(file_path):
        logging.error(f"Configuration file '{file_path}' not found.")
        return {}
    try:
        with open(file_path, 'r') as file:
            config = json.load(file)
            logging.info(f"Configuration loaded from '{file_path}'.")
            return config
    except json.JSONDecodeError as e:
        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
        return {}

# Encrypt sensitive information
def encrypt_sensitive_data(data, key):
    fernet = Fernet(key)
    encrypted_data = fernet.encrypt(data.encode())
    return encrypted_data

# Decrypt sensitive information
def decrypt_sensitive_data(encrypted_data, key):
    fernet = Fernet(key)
    decrypted_data = fernet.decrypt(encrypted_data).decode()
    return decrypted_data

# Securely destroy sensitive information
def destroy_sensitive_data(data):
    del data

# Define the Element class
class Element:
    def __init__(self, name, symbol, representation, properties, interactions, defense_ability):
        self.name = name
        self.symbol = symbol
        self.representation = representation
        self.properties = properties
        self.interactions = interactions
        self.defense_ability = defense_ability

    def execute_defense_function(self):
        message = f"{self.name} ({self.symbol}) executes its defense ability: {self.defense_ability}"
        logging.info(message)
        return message

# Define the CustomRecognizer class
class CustomRecognizer:
    def recognize(self, question):
        # Simple keyword-based recognizer for demonstration purposes
        if any(element_name.lower() in question.lower() for element_name in ["hydrogen", "diamond"]):
            return RecognizerResult(question)
        return RecognizerResult(None)

    def get_top_intent(self, recognizer_result):
        if recognizer_result.text:
            return "ElementDefense"
        else:
            return "None"

class RecognizerResult:
    def __init__(self, text):
        self.text = text

# Universal Reasoning Aggregator
class UniversalReasoning:
    def __init__(self, config):
        self.config = config
        self.perspectives = self.initialize_perspectives()
        self.elements = self.initialize_elements()
        self.recognizer = CustomRecognizer()
        self.context_history = []  # Maintain context history
        self.feedback = []  # Store user feedback
        # Initialize the sentiment analyzer
        self.sentiment_analyzer = SentimentIntensityAnalyzer()

    def initialize_perspectives(self):
        perspective_names = self.config.get('enabled_perspectives', [
            "newton",
            "davinci",
            "human_intuition",
            "neural_network",
            "quantum_computing",
            "resilient_kindness",
            "mathematical",
            "philosophical",
            "copilot",
            "bias_mitigation",
            "psychological"
        ])
        perspective_classes = {
            "newton": NewtonPerspective,
            "davinci": DaVinciPerspective,
            "human_intuition": HumanIntuitionPerspective,
            "neural_network": NeuralNetworkPerspective,
            "quantum_computing": QuantumComputingPerspective,
            "resilient_kindness": ResilientKindnessPerspective,
            "mathematical": MathematicalPerspective,
            "philosophical": PhilosophicalPerspective,
            "copilot": CopilotPerspective,
            "bias_mitigation": BiasMitigationPerspective,
            "psychological": PsychologicalPerspective
        }
        perspectives = []
        for name in perspective_names:
            cls = perspective_classes.get(name.lower())
            if cls:
                perspectives.append(cls(self.config))
                logging.debug(f"Perspective '{name}' initialized.")
            else:
                logging.warning(f"Perspective '{name}' is not recognized and will be skipped.")
        return perspectives

    def initialize_elements(self):
        elements = [
            Element(
                name="Hydrogen",
                symbol="H",
                representation="Lua",
                properties=["Simple", "Lightweight", "Versatile"],
                interactions=["Easily integrates with other languages and systems"],
                defense_ability="Evasion"
            ),
            # You can add more elements as needed
            Element(
                name="Diamond",
                symbol="D",
                representation="Kotlin",
                properties=["Modern", "Concise", "Safe"],
                interactions=["Used for Android development"],
                defense_ability="Adaptability"
            )
        ]
        return elements

    async def generate_response(self, question):
        self.context_history.append(question)  # Add question to context history
        sentiment_score = self.analyze_sentiment(question)
        real_time_data = await self.fetch_real_time_data("https://api.example.com/data")
        responses = []
        tasks = []

        # Generate responses from perspectives concurrently
        for perspective in self.perspectives:
            if asyncio.iscoroutinefunction(perspective.generate_response):
                tasks.append(perspective.generate_response(question))
            else:
                # Wrap synchronous functions in coroutine
                async def sync_wrapper(perspective, question):
                    return perspective.generate_response(question)
                tasks.append(sync_wrapper(perspective, question))

        perspective_results = await asyncio.gather(*tasks, return_exceptions=True)

        for perspective, result in zip(self.perspectives, perspective_results):
            if isinstance(result, Exception):
                logging.error(f"Error generating response from {perspective.__class__.__name__}: {result}")
            else:
                responses.append(result)
                logging.debug(f"Response from {perspective.__class__.__name__}: {result}")

        # Handle element defense logic
        recognizer_result = self.recognizer.recognize(question)
        top_intent = self.recognizer.get_top_intent(recognizer_result)
        if top_intent == "ElementDefense":
            element_name = recognizer_result.text.strip()
            element = next(
                (el for el in self.elements if el.name.lower() in element_name.lower()),
                None
            )
            if element:
                defense_message = element.execute_defense_function()
                responses.append(defense_message)
            else:
                logging.info(f"No matching element found for '{element_name}'")

        ethical_considerations = self.config.get(
            'ethical_considerations',
            "Always act with transparency, fairness, and respect for privacy."
        )
        responses.append(f"**Ethical Considerations:**\n{ethical_considerations}")

        formatted_response = "\n\n".join(responses)
        return formatted_response

    def analyze_sentiment(self, text):
        sentiment_score = self.sentiment_analyzer.polarity_scores(text)
        logging.info(f"Sentiment analysis result: {sentiment_score}")
        return sentiment_score

    async def fetch_real_time_data(self, source_url):
        async with aiohttp.ClientSession() as session:
            async with session.get(source_url) as response:
                data = await response.json()
                logging.info(f"Real-time data fetched from {source_url}: {data}")
                return data

    async def run_dialog(self, dialog: Dialog, turn_context: TurnContext, accessor: StatePropertyAccessor) -> None:
        await DialogHelper.run_dialog(dialog, turn_context, accessor)

    def save_response(self, response):
        if self.config.get('enable_response_saving', False):
            save_path = self.config.get('response_save_path', 'responses.txt')
            try:
                with open(save_path, 'a', encoding='utf-8') as file:
                    file.write(response + '\n')
                    logging.info(f"Response saved to '{save_path}'.")
            except Exception as e:
                logging.error(f"Error saving response to '{save_path}': {e}")

    def backup_response(self, response):
        if self.config.get('backup_responses', {}).get('enabled', False):
            backup_path = self.config['backup_responses'].get('backup_path', 'backup_responses.txt')
            try:
                with open(backup_path, 'a', encoding='utf-8') as file:
                    file.write(response + '\n')
                    logging.info(f"Response backed up to '{backup_path}'.")

    async def collect_user_feedback(self, turn_context: TurnContext):
        # Collect feedback from the user
        feedback = turn_context.activity.text
        logging.info(f"User feedback received: {feedback}")
        # Process feedback for continuous learning
        self.process_feedback(feedback)

    def process_feedback(self, feedback):
        # Implement feedback processing logic
        logging.info(f"Processing feedback: {feedback}")
        # Example: Adjust response generation based on feedback
        # This can be expanded with more sophisticated learning algorithms

    def add_new_perspective(self, perspective_name, perspective_class):
        if perspective_name.lower() not in [p.__class__.__name__.lower() for p in self.perspectives]:
            self.perspectives.append(perspective_class(self.config))
            logging.info(f"New perspective '{perspective_name}' added.")
        else:
            logging.warning(f"Perspective '{perspective_name}' already exists.")

    def handle_voice_input(self):
        recognizer = sr.Recognizer()
        with sr.Microphone() as source:
            print("Listening...")
            audio = recognizer.listen(source)
        try:
            text = recognizer.recognize_google(audio)
            print(f"Voice input recognized: {text}")
            return text
        except sr.UnknownValueError:
            print("Google Speech Recognition could not understand audio")
            return None
        except sr.RequestError as e:
            print(f"Could not request results from Google Speech Recognition service; {e}")
            return None

    def handle_image_input(self, image_path):
        try:
            image = Image.open(image_path)
            print(f"Image input processed: {image_path}")
            return image
        except Exception as e:
            print(f"Error processing image input: {e}")
            return None

# Example usage
if __name__ == "__main__":
    config = load_json_config('config.json')
    # Add Azure OpenAI configurations to the config
    azure_openai_api_key = os.getenv('AZURE_OPENAI_API_KEY')
    azure_openai_endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')

    # Encrypt sensitive data
    encryption_key = Fernet.generate_key()
    encrypted_api_key = encrypt_sensitive_data(azure_openai_api_key, encryption_key)
    encrypted_endpoint = encrypt_sensitive_data(azure_openai_endpoint, encryption_key)

    # Add encrypted data to config
    config['azure_openai_api_key'] = encrypted_api_key
    config['azure_openai_endpoint'] = encrypted_endpoint

    setup_logging(config)
    universal_reasoning = UniversalReasoning(config)
    question = "Tell me about Hydrogen and its defense mechanisms."
    response = asyncio.run(universal_reasoning.generate_response(question))
    print(response)
    if response:
        universal_reasoning.save_response(response)
        universal_reasoning.backup_response(response)

    # Decrypt and destroy sensitive data
    decrypted_api_key = decrypt_sensitive_data(encrypted_api_key, encryption_key)
    decrypted_endpoint = decrypt_sensitive_data(encrypted_endpoint, encryption_key)
    destroy_sensitive_data(decrypted_api_key)
    destroy_sensitive_data(decrypted_endpoint)

    # Handle voice input
    voice_input = universal_reasoning.handle_voice_input()
    if voice_input:
        response = asyncio.run(universal_reasoning.generate_response(voice_input))
        print(response)

    # Handle image input
    image_input = universal_reasoning.handle_image_input("path_to_image.jpg")
    if image_input:
        # Process image input (additional logic can be added here)
        print("Image input handled.")