import os
import torch
from typing import Dict, List, Any, Union, Optional, Tuple
import numpy as np
from pathlib import Path
import requests
import json
import time

from utils.config import AI_MODELS
from utils.logging import get_logger, log_performance
from utils.error_handling import handle_ai_model_exceptions, AIModelError, ValidationError
from utils.semantic_search import search_content, find_similar_items, detect_duplicates, cluster_content, build_knowledge_graph, identify_trends, identify_information_gaps

# Initialize logger
logger = get_logger(__name__)

# Set environment variable to use CPU if no GPU available
os.environ["CUDA_VISIBLE_DEVICES"] = "" if not torch.cuda.is_available() else "0"

# Global cache for loaded models
MODEL_CACHE = {}

@handle_ai_model_exceptions
def get_model(task: str, model_name: Optional[str] = None):
    """
    Load and cache AI models
    
    Args:
        task: Task type (text_generation, question_answering, image_captioning, etc.)
        model_name: Name of the model on HuggingFace (optional, uses default from config if None)
        
    Returns:
        Loaded model and tokenizer/processor
        
    Raises:
        AIModelError: If there's an error loading the model
        ValidationError: If the task is not supported
    """
    # Get model name from config if not provided
    if model_name is None:
        if task not in AI_MODELS:
            logger.error(f"Unsupported task: {task}")
            raise ValidationError(f"Unsupported task: {task}")
        model_name = AI_MODELS[task]["name"]
    
    cache_key = f"{model_name}_{task}"
    
    # Return cached model if available
    if cache_key in MODEL_CACHE:
        logger.debug(f"Using cached model for {task}: {model_name}")
        return MODEL_CACHE[cache_key]
    
    logger.info(f"Loading model for {task}: {model_name}")
    start_time = time.time()
    
    try:
        if task == "text_generation":
            from transformers import AutoModelForCausalLM, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModelForCausalLM.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        elif task == "question_answering":
            from transformers import AutoModelForQuestionAnswering, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModelForQuestionAnswering.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        elif task == "image_captioning":
            from transformers import BlipProcessor, BlipForConditionalGeneration
            processor = BlipProcessor.from_pretrained(model_name)
            model = BlipForConditionalGeneration.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, processor)
            
        elif task == "speech_to_text":
            from transformers import WhisperProcessor, WhisperForConditionalGeneration
            processor = WhisperProcessor.from_pretrained(model_name)
            model = WhisperForConditionalGeneration.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, processor)
            
        elif task == "translation":
            from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        elif task == "sentiment":
            from transformers import AutoModelForSequenceClassification, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModelForSequenceClassification.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        elif task == "summarization":
            from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        elif task == "code_generation":
            from transformers import AutoModel, AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(model_name)
            model = AutoModel.from_pretrained(model_name)
            MODEL_CACHE[cache_key] = (model, tokenizer)
            
        else:
            logger.error(f"Unsupported task: {task}")
            raise ValidationError(f"Unsupported task: {task}")
        
        # Log performance
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance(f"load_model_{task}", elapsed_time)
        logger.info(f"Model loaded successfully for {task}: {model_name} in {elapsed_time:.2f}ms")
        
        return MODEL_CACHE[cache_key]
        
    except Exception as e:
        logger.error(f"Error loading model {model_name} for task {task}: {str(e)}")
        raise AIModelError(f"Error loading model {model_name} for task {task}", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def generate_text(prompt: str, max_length: Optional[int] = None, temperature: Optional[float] = None) -> str:
    """
    Generate text using DialoGPT-medium
    
    Args:
        prompt: Input prompt
        max_length: Maximum length of generated text (uses config default if None)
        temperature: Temperature for sampling (uses config default if None)
        
    Returns:
        Generated text
        
    Raises:
        AIModelError: If there's an error generating text
    """
    task = "text_generation"
    model_config = AI_MODELS[task]
    model_name = model_config["name"]
    
    # Use config defaults if not provided
    if max_length is None:
        max_length = model_config.get("max_length", 100)
    if temperature is None:
        temperature = model_config.get("temperature", 0.7)
    
    logger.debug(f"Generating text with prompt: {prompt[:50]}...")
    start_time = time.time()
    
    model, tokenizer = get_model(task)
    
    try:
        # Encode the input and generate response
        inputs = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors="pt")
        with torch.no_grad():
            outputs = model.generate(
                inputs, 
                max_length=max_length,
                pad_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=3,
                do_sample=True,
                top_k=50,
                top_p=0.95,
                temperature=temperature
            )
        
        # Decode and return the response
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Log performance and usage
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance("generate_text", elapsed_time)
        log_ai_model_usage(model_name, "text_generation", len(inputs[0]) + len(outputs[0]))
        
        logger.debug(f"Text generated successfully in {elapsed_time:.2f}ms")
        return response
    except Exception as e:
        logger.error(f"Error generating text: {str(e)}")
        raise AIModelError(f"Error generating text", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def answer_question(question: str, context: str) -> str:
    """
    Answer a question based on the given context
    
    Args:
        question: Question to answer
        context: Context for the question
        
    Returns:
        Answer to the question
        
    Raises:
        AIModelError: If there's an error answering the question
    """
    task = "question_answering"
    model_name = AI_MODELS[task]["name"]
    
    logger.debug(f"Answering question: {question}")
    start_time = time.time()
    
    model, tokenizer = get_model(task)
    
    try:
        # Encode the input
        inputs = tokenizer(question, context, return_tensors="pt")
        
        # Get model output
        with torch.no_grad():
            outputs = model(**inputs)
        
        # Get answer span
        answer_start = torch.argmax(outputs.start_logits)
        answer_end = torch.argmax(outputs.end_logits) + 1
        
        # Convert to answer text
        answer = tokenizer.convert_tokens_to_string(
            tokenizer.convert_ids_to_tokens(inputs.input_ids[0][answer_start:answer_end])
        )
        
        # Log performance and usage
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance("answer_question", elapsed_time)
        log_ai_model_usage(model_name, "question_answering", len(inputs.input_ids[0]))
        
        logger.debug(f"Question answered successfully in {elapsed_time:.2f}ms")
        return answer if answer else "No answer found"
    except Exception as e:
        logger.error(f"Error answering question: {str(e)}")
        raise AIModelError(f"Error answering question", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def analyze_sentiment(text: str) -> Dict[str, float]:
    """
    Analyze sentiment of text
    
    Args:
        text: Text to analyze
        
    Returns:
        Dictionary with sentiment scores
        
    Raises:
        AIModelError: If there's an error analyzing sentiment
    """
    task = "sentiment"
    model_name = AI_MODELS[task]["name"]
    
    logger.debug(f"Analyzing sentiment of text: {text[:50]}...")
    start_time = time.time()
    
    model, tokenizer = get_model(task)
    
    try:
        # Encode the input
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
        
        # Get model output
        with torch.no_grad():
            outputs = model(**inputs)
        
        # Get sentiment scores
        scores = torch.nn.functional.softmax(outputs.logits, dim=1)
        scores = scores.detach().numpy()[0]
        
        # Map scores to labels
        labels = ["negative", "neutral", "positive"]
        results = {label: float(score) for label, score in zip(labels, scores)}
        
        # Log performance and usage
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance("analyze_sentiment", elapsed_time)
        log_ai_model_usage(model_name, "sentiment_analysis", len(inputs.input_ids[0]))
        
        logger.debug(f"Sentiment analysis completed successfully in {elapsed_time:.2f}ms")
        return results
    except Exception as e:
        logger.error(f"Error analyzing sentiment: {str(e)}")
        raise AIModelError(f"Error analyzing sentiment", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def summarize_text(text: str, max_length: Optional[int] = None, min_length: Optional[int] = None) -> str:
    """
    Summarize text using BART
    
    Args:
        text: Text to summarize
        max_length: Maximum length of summary (uses config default if None)
        min_length: Minimum length of summary (uses config default if None)
        
    Returns:
        Summarized text
        
    Raises:
        AIModelError: If there's an error summarizing text
    """
    task = "summarization"
    model_config = AI_MODELS[task]
    model_name = model_config["name"]
    
    # Use config defaults if not provided
    if max_length is None:
        max_length = model_config.get("max_length", 150)
    if min_length is None:
        min_length = model_config.get("min_length", 40)
    
    logger.debug(f"Summarizing text: {text[:50]}...")
    start_time = time.time()
    
    model, tokenizer = get_model(task)
    
    try:
        # Encode the input
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
        
        # Generate summary
        with torch.no_grad():
            summary_ids = model.generate(
                inputs.input_ids,
                max_length=max_length,
                min_length=min_length,
                num_beams=4,
                early_stopping=True
            )
        
        # Decode summary
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        
        # Log performance and usage
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance("summarize_text", elapsed_time)
        log_ai_model_usage(model_name, "summarization", len(inputs.input_ids[0]) + len(summary_ids[0]))
        
        logger.debug(f"Text summarization completed successfully in {elapsed_time:.2f}ms")
        return summary
    except Exception as e:
        logger.error(f"Error summarizing text: {str(e)}")
        raise AIModelError(f"Error summarizing text", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def get_weather(city: str) -> Dict[str, Any]:
    """
    Get weather information for a city using a free weather API
    
    Args:
        city: City name
        
    Returns:
        Weather information
        
    Raises:
        AIModelError: If there's an error getting weather information
    """
    logger.debug(f"Getting weather for city: {city}")
    start_time = time.time()
    
    try:
        # Using OpenWeatherMap API with a free tier (requires signup but free)
        # In a real app, you would store this in an environment variable
        # For demo purposes, we'll use a mock response
        
        # Mock response for demo
        weather_data = {
            "location": city,
            "temperature": 22,  # Celsius
            "condition": "Partly Cloudy",
            "humidity": 65,
            "wind_speed": 10,
            "forecast": [
                {"day": "Today", "high": 24, "low": 18, "condition": "Partly Cloudy"},
                {"day": "Tomorrow", "high": 26, "low": 19, "condition": "Sunny"},
                {"day": "Day After", "high": 23, "low": 17, "condition": "Rain"}
            ]
        }
        
        # Log performance
        elapsed_time = (time.time() - start_time) * 1000  # Convert to ms
        log_performance("get_weather", elapsed_time)
        
        logger.debug(f"Weather data retrieved successfully in {elapsed_time:.2f}ms")
        return weather_data
    except Exception as e:
        logger.error(f"Error getting weather: {str(e)}")
        raise AIModelError(f"Error getting weather information", {"original_error": str(e)}) from e

@handle_ai_model_exceptions
def generate_motivation_quote() -> str:
    """
    Generate a motivational quote using DialoGPT
    
    Returns:
        Motivational quote
        
    Raises:
        AIModelError: If there's an error generating the quote
    """
    logger.debug("Generating motivational quote")
    
    prompts = [
        "Share an inspiring quote about productivity.",
        "What's a motivational quote for success?",
        "Give me a quote about achieving goals.",
        "Share wisdom about staying focused.",
        "What's a good quote about perseverance?"
    ]
    
    import random
    prompt = random.choice(prompts)
    
    return generate_text(prompt, max_length=50)

@handle_ai_model_exceptions
def generate_daily_plan(tasks: List[Dict[str, Any]], goals: List[Dict[str, Any]]) -> str:
    """
    Generate a daily plan based on tasks and goals
    
    Args:
        tasks: List of tasks
        goals: List of goals
        
    Returns:
        Generated daily plan
        
    Raises:
        AIModelError: If there's an error generating the plan
    """
    logger.debug("Generating daily plan")
    
    # Create a prompt based on tasks and goals
    active_tasks = [task for task in tasks if not task.get("completed", False)][:5]
    active_goals = [goal for goal in goals if not goal.get("completed", False)][:3]
    
    task_list = "\n".join([f"- {task.get('title', 'Untitled Task')}" for task in active_tasks])
    goal_list = "\n".join([f"- {goal.get('title', 'Untitled Goal')}" for goal in active_goals])
    
    prompt = f"""Create a productive daily plan based on these tasks and goals:
    
Tasks:
{task_list}

Goals:
{goal_list}

Daily Plan:"""
    
    return generate_text(prompt, max_length=300)

@handle_ai_model_exceptions
def break_down_task(task_title: str, task_description: str) -> List[str]:
    """
    Break down a task into subtasks using AI
    
    Args:
        task_title: Title of the task
        task_description: Description of the task
        
    Returns:
        List of subtasks
        
    Raises:
        AIModelError: If there's an error breaking down the task
    """
    logger.debug(f"Breaking down task: {task_title}")
    
    prompt = f"""Break down this task into 3-5 actionable subtasks:
    
Task: {task_title}
Description: {task_description}

Subtasks:"""
    
    response = generate_text(prompt, max_length=200)
    
    # Parse the response into a list of subtasks
    subtasks = []
    for line in response.split("\n"):
        line = line.strip()
        if line and (line.startswith("-") or line.startswith("*") or 
                    (len(line) > 2 and line[0].isdigit() and line[1] == '.')):
            # Remove leading dash, asterisk, or number
            subtask = line[2:].strip() if line[1] == ' ' else line[1:].strip()
            subtasks.append(subtask)
    
    # If parsing failed, create some generic subtasks
    if not subtasks:
        logger.warning(f"Failed to parse subtasks for {task_title}, using generic subtasks")
        subtasks = [
            f"Research for {task_title}",
            f"Create draft for {task_title}",
            f"Review and finalize {task_title}"
        ]
    
    return subtasks

@handle_ai_model_exceptions
def estimate_task_time(task_title: str, task_description: str) -> int:
    """
    Estimate time needed for a task in minutes
    
    Args:
        task_title: Title of the task
        task_description: Description of the task
        
    Returns:
        Estimated time in minutes
        
    Raises:
        AIModelError: If there's an error estimating the time
    """
    logger.debug(f"Estimating time for task: {task_title}")
    
    prompt = f"""Estimate how many minutes this task will take:
    
Task: {task_title}
Description: {task_description}

Estimated minutes:"""
    
    response = generate_text(prompt, max_length=10)
    
    # Try to extract a number from the response
    import re
    numbers = re.findall(r'\d+', response)
    
    if numbers:
        # Use the first number found
        try:
            minutes = int(numbers[0])
            # Cap at reasonable limits
            return min(max(minutes, 5), 480)  # Between 5 minutes and 8 hours
        except ValueError:
            logger.warning(f"Failed to parse time estimate from response: {response}")
    
    # Default estimate based on task title length as a fallback
    logger.warning(f"Using fallback time estimate for {task_title}")
    return min(len(task_title) * 5, 120)  # Default between 5 and 120 minutes