# Utils.py
#########################################
# General Utilities Library
# This library is used to hold random utilities used by various other libraries.
#
####
####################
# Function List
#
# 1. extract_text_from_segments(segments: List[Dict]) -> str
# 2. download_file(url, dest_path, expected_checksum=None, max_retries=3, delay=5)
# 3. verify_checksum(file_path, expected_checksum)
# 4. create_download_directory(title)
# 5. sanitize_filename(filename)
# 6. normalize_title(title)
# 7.
#
#
#
####################
# Import necessary libraries
import configparser
import hashlib
import json
import logging
import os
import re
import time
from datetime import timedelta
from urllib.parse import urlparse, parse_qs, urlencode, urlunparse

import requests
import unicodedata
from tqdm import tqdm

from App_Function_Libraries.Video_DL_Ingestion_Lib import get_youtube


#######################################################################################################################
# Function Definitions
#

def extract_text_from_segments(segments):
    logging.debug(f"Segments received: {segments}")
    logging.debug(f"Type of segments: {type(segments)}")

    def extract_text_recursive(data):
        if isinstance(data, dict):
            for key, value in data.items():
                if key == 'Text':
                    return value
                elif isinstance(value, (dict, list)):
                    result = extract_text_recursive(value)
                    if result:
                        return result
        elif isinstance(data, list):
            return ' '.join(filter(None, [extract_text_recursive(item) for item in data]))
        return None

    text = extract_text_recursive(segments)

    if text:
        return text.strip()
    else:
        logging.error(f"Unable to extract text from segments: {segments}")
        return "Error: Unable to extract transcription"


def download_file(url, dest_path, expected_checksum=None, max_retries=3, delay=5):
    temp_path = dest_path + '.tmp'

    for attempt in range(max_retries):
        try:
            # Check if a partial download exists and get its size
            resume_header = {}
            if os.path.exists(temp_path):
                resume_header = {'Range': f'bytes={os.path.getsize(temp_path)}-'}

            response = requests.get(url, stream=True, headers=resume_header)
            response.raise_for_status()

            # Get the total file size from headers
            total_size = int(response.headers.get('content-length', 0))
            initial_pos = os.path.getsize(temp_path) if os.path.exists(temp_path) else 0

            mode = 'ab' if 'Range' in response.headers else 'wb'
            with open(temp_path, mode) as temp_file, tqdm(
                total=total_size, unit='B', unit_scale=True, desc=dest_path, initial=initial_pos, ascii=True
            ) as pbar:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:  # filter out keep-alive new chunks
                        temp_file.write(chunk)
                        pbar.update(len(chunk))

            # Verify the checksum if provided
            if expected_checksum:
                if not verify_checksum(temp_path, expected_checksum):
                    os.remove(temp_path)
                    raise ValueError("Downloaded file's checksum does not match the expected checksum")

            # Move the file to the final destination
            os.rename(temp_path, dest_path)
            print("Download complete and verified!")
            return dest_path

        except Exception as e:
            print(f"Attempt {attempt + 1} failed: {e}")
            if attempt < max_retries - 1:
                print(f"Retrying in {delay} seconds...")
                time.sleep(delay)
            else:
                print("Max retries reached. Download failed.")
                raise


def verify_checksum(file_path, expected_checksum):
    sha256_hash = hashlib.sha256()
    with open(file_path, 'rb') as f:
        for byte_block in iter(lambda: f.read(4096), b''):
            sha256_hash.update(byte_block)
    return sha256_hash.hexdigest() == expected_checksum


def create_download_directory(title):
    base_dir = "Results"
    # Remove characters that are illegal in Windows filenames and normalize
    safe_title = normalize_title(title)
    logging.debug(f"{title} successfully normalized")
    session_path = os.path.join(base_dir, safe_title)
    if not os.path.exists(session_path):
        os.makedirs(session_path, exist_ok=True)
        logging.debug(f"Created directory for downloaded video: {session_path}")
    else:
        logging.debug(f"Directory already exists for downloaded video: {session_path}")
    return session_path


def sanitize_filename(filename):
    # Remove invalid characters and replace spaces with underscores
    sanitized = re.sub(r'[<>:"/\\|?*]', '', filename)
    sanitized = re.sub(r'\s+', ' ', sanitized).strip()
    return sanitized


def normalize_title(title):
    # Normalize the string to 'NFKD' form and encode to 'ascii' ignoring non-ascii characters
    title = unicodedata.normalize('NFKD', title).encode('ascii', 'ignore').decode('ascii')
    title = title.replace('/', '_').replace('\\', '_').replace(':', '_').replace('"', '').replace('*', '').replace('?',
                                                                                                                   '').replace(
        '<', '').replace('>', '').replace('|', '')
    return title


def clean_youtube_url(url):
    parsed_url = urlparse(url)
    query_params = parse_qs(parsed_url.query)
    if 'list' in query_params:
        query_params.pop('list')
    cleaned_query = urlencode(query_params, doseq=True)
    cleaned_url = urlunparse(parsed_url._replace(query=cleaned_query))
    return cleaned_url


def extract_video_info(url):
    info_dict = get_youtube(url)
    title = info_dict.get('title', 'Untitled')
    return info_dict, title


def import_data(file):
    # Implement this function to import data from a file
    pass


def safe_read_file(file_path):
    encodings = ['utf-8', 'utf-16', 'ascii', 'latin-1', 'iso-8859-1', 'cp1252']
    for encoding in encodings:
        try:
            with open(file_path, 'r', encoding=encoding) as file:
                return file.read()
        except UnicodeDecodeError:
            continue
        except FileNotFoundError:
            return f"File not found: {file_path}"
        except Exception as e:
            return f"An error occurred: {e}"
    return f"Unable to decode the file {file_path} with any of the attempted encodings: {encodings}"

#
#
#######################
# Temp file cleanup
#
# Global list to keep track of downloaded files
downloaded_files = []

def cleanup_downloads():
    """Function to clean up downloaded files when the server exits."""
    for file_path in downloaded_files:
        try:
            if os.path.exists(file_path):
                os.remove(file_path)
                print(f"Cleaned up file: {file_path}")
        except Exception as e:
            print(f"Error cleaning up file {file_path}: {e}")

#
#
#######################
# Config loading
#

def load_comprehensive_config():
    # Get the directory of the current script
    current_dir = os.path.dirname(os.path.abspath(__file__))
    # Go up one level to the project root directory
    project_root = os.path.dirname(current_dir)
    # Construct the path to the config file in the project root directory
    config_path = os.path.join(project_root, 'config.txt')
    # Create a ConfigParser object
    config = configparser.ConfigParser()
    # Read the configuration file
    files_read = config.read(config_path)
    if not files_read:
        raise FileNotFoundError(f"Config file not found at {config_path}")
    return config


# FIXME - update to include prompt path in return statement
def load_and_log_configs():
    try:
        config = load_comprehensive_config()
        if config is None:
            logging.error("Config is None, cannot proceed")
            return None
        # API Keys
        anthropic_api_key = config.get('API', 'anthropic_api_key', fallback=None)
        logging.debug(
            f"Loaded Anthropic API Key: {anthropic_api_key[:5]}...{anthropic_api_key[-5:] if anthropic_api_key else None}")

        cohere_api_key = config.get('API', 'cohere_api_key', fallback=None)
        logging.debug(
            f"Loaded Cohere API Key: {cohere_api_key[:5]}...{cohere_api_key[-5:] if cohere_api_key else None}")

        groq_api_key = config.get('API', 'groq_api_key', fallback=None)
        logging.debug(f"Loaded Groq API Key: {groq_api_key[:5]}...{groq_api_key[-5:] if groq_api_key else None}")

        openai_api_key = config.get('API', 'openai_api_key', fallback=None)
        logging.debug(
            f"Loaded OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")

        huggingface_api_key = config.get('API', 'huggingface_api_key', fallback=None)
        logging.debug(
            f"Loaded HuggingFace API Key: {huggingface_api_key[:5]}...{huggingface_api_key[-5:] if huggingface_api_key else None}")

        openrouter_api_key = config.get('API', 'openrouter_api_key', fallback=None)
        logging.debug(
            f"Loaded OpenRouter API Key: {openrouter_api_key[:5]}...{openrouter_api_key[-5:] if openrouter_api_key else None}")

        deepseek_api_key = config.get('API', 'deepseek_api_key', fallback=None)
        logging.debug(
            f"Loaded DeepSeek API Key: {deepseek_api_key[:5]}...{deepseek_api_key[-5:] if deepseek_api_key else None}")

        # Models
        anthropic_model = config.get('API', 'anthropic_model', fallback='claude-3-sonnet-20240229')
        cohere_model = config.get('API', 'cohere_model', fallback='command-r-plus')
        groq_model = config.get('API', 'groq_model', fallback='llama3-70b-8192')
        openai_model = config.get('API', 'openai_model', fallback='gpt-4-turbo')
        huggingface_model = config.get('API', 'huggingface_model', fallback='CohereForAI/c4ai-command-r-plus')
        openrouter_model = config.get('API', 'openrouter_model', fallback='microsoft/wizardlm-2-8x22b')
        deepseek_model = config.get('API', 'deepseek_model', fallback='deepseek-chat')

        logging.debug(f"Loaded Anthropic Model: {anthropic_model}")
        logging.debug(f"Loaded Cohere Model: {cohere_model}")
        logging.debug(f"Loaded Groq Model: {groq_model}")
        logging.debug(f"Loaded OpenAI Model: {openai_model}")
        logging.debug(f"Loaded HuggingFace Model: {huggingface_model}")
        logging.debug(f"Loaded OpenRouter Model: {openrouter_model}")

        # Local-Models
        kobold_api_ip = config.get('Local-API', 'kobold_api_IP', fallback='http://127.0.0.1:5000/api/v1/generate')
        kobold_api_key = config.get('Local-API', 'kobold_api_key', fallback='')

        llama_api_IP = config.get('Local-API', 'llama_api_IP', fallback='http://127.0.0.1:8080/v1/chat/completions')
        llama_api_key = config.get('Local-API', 'llama_api_key', fallback='')

        ooba_api_IP = config.get('Local-API', 'ooba_api_IP', fallback='http://127.0.0.1:5000/v1/chat/completions')
        ooba_api_key = config.get('Local-API', 'ooba_api_key', fallback='')

        tabby_api_IP = config.get('Local-API', 'tabby_api_IP', fallback='http://127.0.0.1:5000/api/v1/generate')
        tabby_api_key = config.get('Local-API', 'tabby_api_key', fallback=None)
        tabby_model = config.get('models', 'tabby_model', fallback=None)

        vllm_api_url = config.get('Local-API', 'vllm_api_IP', fallback='http://127.0.0.1:500/api/v1/chat/completions')
        vllm_api_key = config.get('Local-API', 'vllm_api_key', fallback=None)
        vllm_model = config.get('Local-API', 'vllm_model', fallback=None)

        ollama_api_url = config.get('Local-API', 'ollama_api_IP', fallback='http://127.0.0.1:11434/api/generate')
        ollama_api_key = config.get('Local-API', 'ollama_api_key', fallback=None)
        ollama_model = config.get('Local-API', 'ollama_model', fallback=None)

        logging.debug(f"Loaded Kobold API IP: {kobold_api_ip}")
        logging.debug(f"Loaded Llama API IP: {llama_api_IP}")
        logging.debug(f"Loaded Ooba API IP: {ooba_api_IP}")
        logging.debug(f"Loaded Tabby API IP: {tabby_api_IP}")
        logging.debug(f"Loaded VLLM API URL: {vllm_api_url}")

        # Retrieve output paths from the configuration file
        output_path = config.get('Paths', 'output_path', fallback='results')
        logging.debug(f"Output path set to: {output_path}")

        # Retrieve processing choice from the configuration file
        processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
        logging.debug(f"Processing choice set to: {processing_choice}")

        # Prompts - FIXME
        prompt_path = config.get('Prompts', 'prompt_path', fallback='prompts.db')

        return {
            'api_keys': {
                'anthropic': anthropic_api_key,
                'cohere': cohere_api_key,
                'groq': groq_api_key,
                'openai': openai_api_key,
                'huggingface': huggingface_api_key,
                'openrouter': openrouter_api_key,
                'deepseek': deepseek_api_key,
                'kobold': kobold_api_key,
                'llama': llama_api_key,
                'ooba': ooba_api_key,
                'tabby': tabby_api_key,
                'vllm': vllm_api_key,
                'ollama': ollama_api_key
            },
            'models': {
                'anthropic': anthropic_model,
                'cohere': cohere_model,
                'groq': groq_model,
                'openai': openai_model,
                'huggingface': huggingface_model,
                'openrouter': openrouter_model,
                'deepseek': deepseek_model,
                'vllm': vllm_model,
                'tabby': tabby_model,
                'ollama': ollama_model

            },
            'local_api_ip': {
                'kobold': kobold_api_ip,
                'llama': llama_api_IP,
                'ooba': ooba_api_IP,
                'tabby': tabby_api_IP,
                'vllm': vllm_api_url,
                'ollama': ollama_api_url
            },
            'output_path': output_path,
            'processing_choice': processing_choice
        }

    except Exception as e:
        logging.error(f"Error loading config: {str(e)}")
        return None


# Log file
# logging.basicConfig(filename='debug-runtime.log', encoding='utf-8', level=logging.DEBUG)


def format_metadata_as_text(metadata):
    if not metadata:
        return "No metadata available"

    formatted_text = "Video Metadata:\n"
    for key, value in metadata.items():
        if value is not None:
            if isinstance(value, list):
                # Join list items with commas
                formatted_value = ", ".join(str(item) for item in value)
            elif key == 'upload_date' and len(str(value)) == 8:
                # Format date as YYYY-MM-DD
                formatted_value = f"{value[:4]}-{value[4:6]}-{value[6:]}"
            elif key in ['view_count', 'like_count']:
                # Format large numbers with commas
                formatted_value = f"{value:,}"
            elif key == 'duration':
                # Convert seconds to HH:MM:SS format
                hours, remainder = divmod(value, 3600)
                minutes, seconds = divmod(remainder, 60)
                formatted_value = f"{hours:02d}:{minutes:02d}:{seconds:02d}"
            else:
                formatted_value = str(value)

            formatted_text += f"{key.capitalize()}: {formatted_value}\n"
    return formatted_text.strip()

# # Example usage:
# example_metadata = {
#     'title': 'Sample Video Title',
#     'uploader': 'Channel Name',
#     'upload_date': '20230615',
#     'view_count': 1000000,
#     'like_count': 50000,
#     'duration': 3725,  # 1 hour, 2 minutes, 5 seconds
#     'tags': ['tag1', 'tag2', 'tag3'],
#     'description': 'This is a sample video description.'
# }
#
# print(format_metadata_as_text(example_metadata))


def convert_to_seconds(time_str):
    if not time_str:
        return 0

    # If it's already a number, assume it's in seconds
    if time_str.isdigit():
        return int(time_str)

    # Parse time string in format HH:MM:SS, MM:SS, or SS
    time_parts = time_str.split(':')
    if len(time_parts) == 3:
        return int(timedelta(hours=int(time_parts[0]),
                             minutes=int(time_parts[1]),
                             seconds=int(time_parts[2])).total_seconds())
    elif len(time_parts) == 2:
        return int(timedelta(minutes=int(time_parts[0]),
                             seconds=int(time_parts[1])).total_seconds())
    elif len(time_parts) == 1:
        return int(time_parts[0])
    else:
        raise ValueError(f"Invalid time format: {time_str}")


def save_to_file(video_urls, filename):
    with open(filename, 'w') as file:
        file.write('\n'.join(video_urls))
    print(f"Video URLs saved to {filename}")


def save_segments_to_json(segments, file_name="transcription_segments.json"):
    """
    Save transcription segments to a JSON file.

    Parameters:
    segments (list): List of transcription segments
    file_name (str): Name of the JSON file to save (default: "transcription_segments.json")

    Returns:
    str: Path to the saved JSON file
    """
    # Ensure the Results directory exists
    os.makedirs("Results", exist_ok=True)

    # Full path for the JSON file
    json_file_path = os.path.join("Results", file_name)

    # Save segments to JSON file
    with open(json_file_path, 'w', encoding='utf-8') as json_file:
        json.dump(segments, json_file, ensure_ascii=False, indent=4)

    return json_file_path

def generate_unique_filename(base_path, base_filename):
    """Generate a unique filename by appending a counter if necessary."""
    filename = base_filename
    counter = 1
    while os.path.exists(os.path.join(base_path, filename)):
        name, ext = os.path.splitext(base_filename)
        filename = f"{name}_{counter}{ext}"
        counter += 1
    return filename


def generate_unique_identifier(file_path):
    filename = os.path.basename(file_path)
    timestamp = int(time.time())

    # Generate a hash of the file content
    hasher = hashlib.md5()
    with open(file_path, 'rb') as f:
        buf = f.read()
        hasher.update(buf)
    content_hash = hasher.hexdigest()[:8]  # Use first 8 characters of the hash

    return f"local:{timestamp}:{content_hash}:{filename}"

#
#
#######################################################################################################################
#
# Backup code

#
# End of backup code
#######################################################################################################################