import json
from typing import Any, Dict, Generator, Optional, Union, cast
from uuid import uuid4

from curl_cffi import requests

from webscout import exceptions
from webscout.AIbase import Provider, Response
from webscout.AIutel import AwesomePrompts, Conversation, Optimizers, sanitize_stream
from webscout.litagent import LitAgent


class ExaAI(Provider):
    """
    A class to interact with the o3minichat.exa.ai API.

    Attributes:
        system_prompt (str): The system prompt to define the assistant's role.

    Examples:
        >>> from webscout.Provider.ExaAI import ExaAI
        >>> ai = ExaAI()
        >>> response = ai.chat("What's the weather today?")
        >>> print(response)
        'The weather today depends on your location...'
    """

    AVAILABLE_MODELS = ["O3-Mini"]
    required_auth = False

    def __init__(
        self,
        is_conversation: bool = True,
        max_tokens: int = 600,
        timeout: int = 30,
        intro: Optional[str] = None,
        filepath: Optional[str] = None,
        update_file: bool = True,
        proxies: dict = {},
        history_offset: int = 10250,
        act: Optional[str] = None,
        # system_prompt: str = "You are a helpful assistant.",
        model: str = "O3-Mini",  # >>> THIS FLAG IS NOT USED <<<
    ):
        """
        Initializes the ExaAI API with given parameters.

        Args:
            is_conversation (bool): Whether the provider is in conversation mode.
            max_tokens (int): Maximum number of tokens to sample.
            timeout (int): Timeout for API requests.
            intro (str): Introduction message for the conversation.
            filepath (str): Filepath for storing conversation history.
            update_file (bool): Whether to update the conversation history file.
            proxies (dict): Proxies for the API requests.
            history_offset (int): Offset for conversation history.
            act (str): Act for the conversation.
            system_prompt (str): The system prompt to define the assistant's role.

        Examples:
            >>> ai = ExaAI(system_prompt="You are a friendly assistant.")
            >>> print(ai.system_prompt)
            'You are a friendly assistant.'
        """
        self.session = requests.Session()
        self.is_conversation = is_conversation
        self.max_tokens_to_sample = max_tokens
        self.api_endpoint = "https://o3minichat.exa.ai/api/chat"
        self.timeout = timeout
        self.last_response = {}
        # self.system_prompt = system_prompt

        # Initialize LitAgent for user agent generation
        self.agent = LitAgent()

        self.headers = {
            "authority": "o3minichat.exa.ai",
            "accept": "*/*",
            "accept-encoding": "gzip, deflate, br, zstd",
            "accept-language": "en-US,en;q=0.9,en-IN;q=0.8",
            "content-type": "application/json",
            "dnt": "1",
            "origin": "https://o3minichat.exa.ai",
            "priority": "u=1, i",
            "referer": "https://o3minichat.exa.ai/",
            "sec-ch-ua": '"Microsoft Edge";v="135", "Not-A.Brand";v="8", "Chromium";v="135"',
            "sec-ch-ua-mobile": "?0",
            "sec-ch-ua-platform": '"Windows"',
            "sec-fetch-dest": "empty",
            "sec-fetch-mode": "cors",
            "sec-fetch-site": "same-origin",
            "sec-gpc": "1",
            "user-agent": self.agent.random(),  # Use LitAgent to generate a random user agent
        }

        self.__available_optimizers = (
            method
            for method in dir(Optimizers)
            if callable(getattr(Optimizers, method)) and not method.startswith("__")
        )
        self.session.headers.update(self.headers)
        self.conversation = Conversation(
            is_conversation, self.max_tokens_to_sample, filepath, update_file
        )
        self.conversation.history_offset = history_offset

        if act:
            self.conversation.intro = (
                AwesomePrompts().get_act(
                    cast(Union[str, int], act),
                    default=self.conversation.intro,
                    case_insensitive=True,
                )
                or self.conversation.intro
            )
        elif intro:
            self.conversation.intro = intro
        if proxies:
            self.session.proxies.update(proxies)

    def ask(
        self,
        prompt: str,
        stream: bool = False,
        raw: bool = False,
        optimizer: Optional[str] = None,
        conversationally: bool = False,
        **kwargs: Any,
    ) -> Response:
        """
        Sends a prompt to the o3minichat.exa.ai API and returns the response.

        Args:
            prompt (str): The prompt to send to the API.
            stream (bool): Whether to stream the response.
            raw (bool): Whether to return the raw response.
            optimizer (str): Optimizer to use for the prompt.
            conversationally (bool): Whether to generate the prompt conversationally.

        Returns:
            Dict[str, Any]: The API response.

        Examples:
            >>> ai = ExaAI()
            >>> response = ai.ask("Tell me a joke!")
            >>> print(response)
            {'text': 'Why did the scarecrow win an award? Because he was outstanding in his field!'}
        """
        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
        if optimizer:
            if optimizer in self.__available_optimizers:
                conversation_prompt = getattr(Optimizers, optimizer)(
                    conversation_prompt if conversationally else prompt
                )
            else:
                raise Exception(f"Optimizer is not one of {self.__available_optimizers}")

        # Generate a unique ID for the conversation
        conversation_id = uuid4().hex[:16]

        payload = {
            "id": conversation_id,
            "messages": [
                # {"role": "system", "content": self.system_prompt}, # system role not supported by this provider
                {"role": "user", "content": conversation_prompt}
            ],
        }

        def for_stream():
            response = self.session.post(
                self.api_endpoint,
                headers=self.headers,
                json=payload,
                stream=True,
                timeout=self.timeout,
            )
            if not response.ok:
                raise exceptions.FailedToGenerateResponseError(
                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
                )

            streaming_response = ""
            # Use sanitize_stream with extract_regexes for Exa AI format
            processed_stream = sanitize_stream(
                data=response.iter_lines(decode_unicode=True),
                intro_value=None,
                to_json=False,
                extract_regexes=[r'0:"(.*?)"'],
                yield_raw_on_error=False,
                raw=raw,
            )

            for content in processed_stream:
                if content:
                    if raw:
                        yield content
                    else:
                        if isinstance(content, str):
                            streaming_response += content
                            yield dict(text=content)

            self.last_response = {"text": streaming_response}
            self.conversation.update_chat_history(prompt, streaming_response)

        def for_non_stream():
            for _ in for_stream():
                pass
            return self.last_response if not raw else json.dumps(self.last_response)

        return for_stream() if stream else for_non_stream()

    def chat(
        self,
        prompt: str,
        stream: bool = False,
        optimizer: Optional[str] = None,
        conversationally: bool = False,
        **kwargs: Any,
    ) -> Union[str, Generator[str, None, None]]:
        """
        Generates a response from the ExaAI API.

        Args:
            prompt (str): The prompt to send to the API.
            stream (bool): Whether to stream the response.
            optimizer (str): Optimizer to use for the prompt.
            conversationally (bool): Whether to generate the prompt conversationally.
            **kwargs: Additional parameters including raw.

        Returns:
            Union[str, Generator[str, None, None]]: The API response as a string or a generator of string chunks.

        Examples:
            >>> ai = ExaAI()
            >>> response = ai.chat("What's the weather today?")
            >>> print(response)
            'The weather today depends on your location...'
        """
        raw = kwargs.get("raw", False)

        def for_stream():
            for response in self.ask(
                prompt, True, raw=raw, optimizer=optimizer, conversationally=conversationally
            ):
                if raw:
                    yield response
                else:
                    yield self.get_message(response)

        def for_non_stream():
            result = self.ask(
                prompt,
                False,
                raw=raw,
                optimizer=optimizer,
                conversationally=conversationally,
            )
            if raw:
                return cast(str, result)
            return self.get_message(result)

        return for_stream() if stream else for_non_stream()

    def get_message(self, response: Response) -> str:
        """
        Extracts the message from the API response.

        Args:
            response (Response): The API response.

        Returns:
            str: The message content.
        """
        if not isinstance(response, dict):
            return str(response)
        resp_dict = cast(Dict[str, Any], response)
        formatted_text = cast(str, resp_dict["text"]).replace("\\n", "\n").replace("\\n\\n", "\n\n")
        return formatted_text


if __name__ == "__main__":
    from rich import print

    ai = ExaAI(timeout=5000)
    response = ai.chat("Tell me about HelpingAI", stream=True)
    if hasattr(response, "__iter__") and not isinstance(response, (str, bytes)):
        for chunk in response:
            print(chunk, end="", flush=True)
    else:
        print(response)
