from os import environ, path
from datetime import timedelta
import redis

basedir = path.abspath(path.dirname(__file__))


class Config(object):
    DEBUG = False
    DEVELOPMENT = False
    SECRET_KEY = '97867c1491bea5ee6a8e8436eb11bf2ba6a69ff53ab1b17ecba450d0f2e572e1'
    SESSION_COOKIE_SECURE = False
    SESSION_COOKIE_HTTPONLY = True

    WTF_CSRF_ENABLED = True

    # flask-security-too settings
    # SECURITY_URL_PREFIX = '/admin'
    SECURITY_LOGIN_URL = '/admin/login'
    SECURITY_LOGOUT_URL = '/admin/logout'
    # SECURITY_REGISTER_URL = '/admin/register'
    # SECURITY_RESET_URL = '/admin/reset'
    # SECURITY_CHANGE_URL = '/admin/change'
    # SECURITY_POST_LOGIN_VIEW = '/admin/user/tenant_overview'
    # SECURITY_POST_LOGOUT_VIEW = '/admin'
    # SECURITY_POST_REGISTER_VIEW = '/admin/user/tenant_overview'
    # SECURITY_POST_RESET_VIEW = '/admin/login'
    # SECURITY_POST_CHANGE_VIEW = '/admin/login'
    # SECURITY_BLUEPRINT_NAME = 'security_bp'
    SECURITY_PASSWORD_SALT = '228614859439123264035565568761433607235'
    REMEMBER_COOKIE_SAMESITE = 'strict'
    SESSION_COOKIE_SAMESITE = 'strict'
    SECURITY_CONFIRMABLE = True
    SECURITY_TRACKABLE = True
    SECURITY_PASSWORD_COMPLEXITY_CHECKER = 'zxcvbn'
    SECURITY_POST_LOGIN_VIEW = '/user/tenant_overview'
    SECURITY_RECOVERABLE = True
    SECURITY_EMAIL_SENDER = "eveai_super@flow-it.net"

    # Ensure Flask-Security-Too is handling CSRF tokens when behind a proxy
    SECURITY_CSRF_PROTECT_MECHANISMS = ['session']
    SECURITY_CSRF_COOKIE_NAME = 'XSRF-TOKEN'
    SECURITY_CSRF_HEADER = 'X-XSRF-TOKEN'
    WTF_CSRF_CHECK_DEFAULT = False

    # flask-mailman settings
    MAIL_SERVER = 'mail.flow-it.net'
    MAIL_PORT = 587
    MAIL_USE_TLS = True
    MAIL_USE_SSL = False
    MAIL_DEFAULT_SENDER = ('eveAI Admin', 'eveai_admin@flow-it.net')

    # file upload settings
    MAX_CONTENT_LENGTH = 16 * 1024 * 1024
    UPLOAD_EXTENSIONS = ['.txt', '.pdf', '.png', '.jpg', '.jpeg', '.gif']

    # supported languages
    SUPPORTED_LANGUAGES = ['en', 'fr', 'nl', 'de', 'es']

    # supported LLMs
    SUPPORTED_EMBEDDINGS = ['openai.text-embedding-3-small', 'openai.text-embedding-3-large', 'mistral.mistral-embed']
    SUPPORTED_LLMS = ['openai.gpt-4o', 'openai.gpt-4-turbo', 'openai.gpt-3.5-turbo', 'mistral.mistral-large-2402']

    # Celery settings
    CELERY_TASK_SERIALIZER = 'json'
    CELERY_RESULT_SERIALIZER = 'json'
    CELERY_ACCEPT_CONTENT = ['json']
    CELERY_TIMEZONE = 'UTC'
    CELERY_ENABLE_UTC = True

    # Chunk Definition, Embedding dependent
    OAI_TE3S_MIN_CHUNK_SIZE = 2000
    OAI_TE3S_MAX_CHUNK_SIZE = 3000
    OAI_TE3L_MIN_CHUNK_SIZE = 3000
    OAI_TE3L_MAX_CHUNK_SIZE = 4000

    # LLM TEMPLATES
    GPT4_SUMMARY_TEMPLATE = """Write a concise summary of the text in {language}. The text is delimited between triple backquotes.
    ```{text}```"""
    GPT3_5_SUMMARY_TEMPLATE = """Write a concise summary of the text in {language}. The text is delimited between triple backquotes.
        ```{text}```"""

    GPT4_RAG_TEMPLATE = """Answer the question based on the following context, delimited between triple backquotes. 
    {tenant_context}
    Use the following {language} in your communication, and cite the sources used.
    If the question cannot be answered using the given context, say "I have insufficient information to answer this question."
    Context:
    ```{context}```
    Question:
    {question}"""
    GPT3_5_RAG_TEMPLATE = """Answer the question based on the following context, delimited between triple backquotes. 
    {tenant_context}
    Use the following {language} in your communication.
    If the question cannot be answered using the given context, say "I have insufficient information to answer this question."
    Context:
    ```{context}```
    Question:
    {question}"""

    GPT4_HISTORY_TEMPLATE = """You are a helpful assistant that details a question based on a previous context,
    in such a way that the question is understandable without the previous context. 
    {tenant_context}
    The context is a conversation history, with the HUMAN asking questions, the AI answering questions.
    The history is delimited between triple backquotes.
    You answer by stating the question in {language}.
    History:
    ```{history}```
    Question to be detailed:
    {question}"""

    GPT3_5_HISTORY_TEMPLATE = """You are a helpful assistant that details a question based on a previous context,
    in such a way that the question is understandable without the previous context. 
    {tenant_context}
    The context is a conversation history, with the HUMAN asking questions, the AI answering questions.
    The history is delimited between triple backquotes.
    You answer by stating the question in {language}.
    History:
    ```{history}```
    Question to be detailed:
    {question}"""

    GPT4_ENCYCLOPEDIA_TEMPLATE = """You have a lot of background knowledge, and as such you are some kind of 
    'encyclopedia' to explain general terminology. Only answer if you have a clear understanding of the question. 
    If not, say you do not have sufficient information to answer the question. Use the {language} in your communication.
    Question:
    {question}"""

    GPT3_5_ENCYCLOPEDIA_TEMPLATE = """You have a lot of background knowledge, and as such you are some kind of 
        'encyclopedia' to explain general terminology. Only answer if you have a clear understanding of the question. 
        If not, say you do not have sufficient information to answer the question. Use the {language} in your communication.
        Question:
        {question}"""

    GPT4_TRANSCRIPT_TEMPLATE = """You are a transcription editor that improves a given transcript on several parts 
    and returns markdown. Without changing what people say. The transcript is delimited between triple backquotes.
    Do the following:
    - divide the transcript into several logical parts. Ensure questions and their answers are in the same logical part.
    - annotate the text to identify these logical parts using headings (max 2 levels) in the same language as the transcript.
    - improve errors in the transcript given the context, but leave the text intact.
    
    ```{transcript}``` 
    """

    GPT3_5_TRANSCRIPT_TEMPLATE = """You are a transcription editor that improves a given transcript on several parts 
        and returns markdown. Without changing what people say. The transcript is delimited between triple backquotes.
        Do the following:
        - divide the transcript into several logical parts. Ensure questions and their answers are in the same logical part.
        - annotate the text to identify these logical parts using headings (max 2 levels) in the same language as the transcript.
        - improve errors in the transcript given the context, but leave the text intact.

        ```{transcript}``` 
        """

    # SocketIO settings
    # SOCKETIO_ASYNC_MODE = 'threading'
    SOCKETIO_ASYNC_MODE = 'gevent'

    # Session Settings
    SESSION_TYPE = 'redis'
    SESSION_PERMANENT = False
    SESSION_USE_SIGNER = True
    PERMANENT_SESSION_LIFETIME = timedelta(minutes=60)
    SESSION_REFRESH_EACH_REQUEST = True

    # Fallback Algorithms
    FALLBACK_ALGORITHMS = [
        "RAG_TENANT",
        "RAG_WIKIPEDIA",
        "RAG_GOOGLE",
        "LLM"
    ]

    # Interaction algorithms
    INTERACTION_ALGORITHMS = {
        "RAG_TENANT": {"name": "RAG_TENANT", "description": "Algorithm using only information provided by the tenant"},
        "RAG_WIKIPEDIA": {"name": "RAG_WIKIPEDIA", "description": "Algorithm using information provided by Wikipedia"},
        "RAG_GOOGLE": {"name": "RAG_GOOGLE", "description": "Algorithm using information provided by Google"},
        "LLM": {"name": "LLM", "description": "Algorithm using information integrated in the used LLM"}
    }


class DevConfig(Config):
    DEVELOPMENT = True
    DEBUG = True
    FLASK_DEBUG = True
    PYCHARM_DEBUG = False
    DB_HOST = environ.get('DB_HOST', 'localhost')
    DB_USER = environ.get('DB_USER', 'luke')
    DB_PASS = environ.get('DB_PASS', 'Skywalker!')
    DB_NAME = environ.get('DB_NAME', 'eveai')

    SQLALCHEMY_DATABASE_URI = f'postgresql+pg8000://{DB_USER}:{DB_PASS}@{DB_HOST}:5432/{DB_NAME}'
    SQLALCHEMY_BINDS = {'public': SQLALCHEMY_DATABASE_URI}
    EXPLAIN_TEMPLATE_LOADING = False

    # Define the nginx prefix used for the specific apps
    EVEAI_APP_LOCATION_PREFIX = '/admin'
    EVEAI_CHAT_LOCATION_PREFIX = '/chat'

    # flask-mailman settings
    MAIL_USERNAME = 'eveai_super@flow-it.net'
    MAIL_PASSWORD = '$6xsWGbNtx$CFMQZqc*'

    # file upload settings
    UPLOAD_FOLDER = '/app/tenant_files'

    # Celery settings
    # eveai_app Redis Settings
    CELERY_BROKER_URL = 'redis://redis:6379/0'
    CELERY_RESULT_BACKEND = 'redis://redis:6379/0'
    # eveai_chat Redis Settings
    CELERY_BROKER_URL_CHAT = 'redis://redis:6379/3'
    CELERY_RESULT_BACKEND_CHAT = 'redis://redis:6379/3'

    # OpenAI API Keys
    OPENAI_API_KEY = 'sk-proj-8R0jWzwjL7PeoPyMhJTZT3BlbkFJLb6HfRB2Hr9cEVFWEhU7'

    # Groq API Keys
    GROQ_API_KEY = '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'

    # Unstructured settings
    UNSTRUCTURED_API_KEY = 'pDgCrXumYhM3CNvjvwV8msMldXC3uw'
    UNSTRUCTURED_BASE_URL = 'https://flowitbv-16c4us0m.api.unstructuredapp.io'
    UNSTRUCTURED_FULL_URL = 'https://flowitbv-16c4us0m.api.unstructuredapp.io/general/v0/general'

    # SocketIO settings
    SOCKETIO_MESSAGE_QUEUE = 'redis://redis:6379/1'
    SOCKETIO_CORS_ALLOWED_ORIGINS = '*'
    SOCKETIO_LOGGER = True
    SOCKETIO_ENGINEIO_LOGGER = True
    SOCKETIO_PING_TIMEOUT = 20000
    SOCKETIO_PING_INTERVAL = 25000
    SOCKETIO_MAX_IDLE_TIME = timedelta(minutes=60)  # Changing this value ==> change maxConnectionDuration value in
                                                    # eveai-chat-widget.js

    # Google Cloud settings
    GC_PROJECT_NAME = 'eveai-420711'
    GC_LOCATION = 'europe-west1'
    GC_KEY_RING = 'eveai-chat'
    GC_CRYPTO_KEY = 'envelope-encryption-key'

    # JWT settings
    JWT_SECRET_KEY = 'bsdMkmQ8ObfMD52yAFg4trrvjgjMhuIqg2fjDpD/JqvgY0ccCcmlsEnVFmR79WPiLKEA3i8a5zmejwLZKl4v9Q=='

    # Session settings
    SESSION_REDIS = redis.from_url('redis://redis:6379/2')

    # PATH settings
    ffmpeg_path = '/usr/bin/ffmpeg'


class ProdConfig(Config):
    DEVELOPMENT = False
    DEBUG = False
    # SQLALCHEMY_DATABASE_URI = environ.get('SQLALCHEMY_DATABASE_URI') or \
    #                           'sqlite:///' + os.path.join(basedir, 'db.sqlite')


config = {
    'dev': DevConfig(),
    'prod': ProdConfig(),
    'default': DevConfig(),
}