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

from curl_cffi import requests

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


class Julius(Provider):
    required_auth = True
    AVAILABLE_MODELS = [
        "Llama 3",
        "GPT-4o",
        "GPT-3.5",
        "Command R",
        "Gemini Flash",
        "Gemini 1.5",
        "Claude Sonnet",
        "Claude Opus",
        "Claude Haiku",
        "GPT-4",
        "GPT-4o mini",
        "Command R+",
        "o1-mini",
        "o1-preview",
    ]

    def __init__(
        self,
        api_key: str,
        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,
        model: str = "Gemini Flash",
    ):
        """Instantiates Julius

        Args:
            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
            timeout (int, optional): Http request timeout. Defaults to 30.
            intro (str, optional): Conversation introductory prompt. Defaults to None.
            filepath (str, optional): Path to file containing conversation history. Defaults to None.
            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
            proxies (dict, optional): Http request proxies. Defaults to {}.
            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
            model (str, optional): Model to use for generating text. Defaults to "Gemini Flash".
                                   Options: "Llama 3", "GPT-4o", "GPT-3.5", "Command R", "Gemini Flash", "Gemini 1.5".
        """
        if model not in self.AVAILABLE_MODELS:
            raise ValueError(f"Invalid model: {model}. Choose from: {self.AVAILABLE_MODELS}")

        self.session = requests.Session()
        self.is_conversation = is_conversation
        self.max_tokens_to_sample = max_tokens
        self.chat_endpoint = "https://api.julius.ai/api/chat/message"
        self.stream_chunk_size = 64
        self.timeout = timeout
        self.last_response = {}
        self.model = model
        self.api_key = api_key
        self.headers = {
            "authorization": f"Bearer {self.api_key}",
            "content-type": "application/json",
            "conversation-id": str(uuid.uuid4()),
            "interactive-charts": "true",
            "is-native": "false",
            "orient-split": "true",
            "request-id": str(uuid.uuid4()),
            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0",
            "visitor-id": str(uuid.uuid4()),
        }

        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
        )
        act_prompt = (
            AwesomePrompts().get_act(
                cast(Union[str, int], act), default=None, case_insensitive=True
            )
            if act
            else intro
        )
        if act_prompt:
            self.conversation.intro = act_prompt
        self.conversation.history_offset = history_offset
        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:
        """Chat with AI

        Args:
            prompt (str): Prompt to be send.
            stream (bool, optional): Whether to stream the response. Defaults to False.
            raw (bool, optional): Whether to return the raw response. Defaults to False.
            optimizer (str, optional): The name of the optimizer to use. Defaults to None.
            conversationally (bool, optional): Whether to chat conversationally. Defaults to False.

        Returns:
            The response from the API.
        """
        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}")

        payload = {
            "message": {"content": conversation_prompt, "role": "user"},
            "provider": "default",
            "chat_mode": "auto",
            "client_version": "20240130",
            "theme": "dark",
            "new_images": None,
            "new_attachments": None,
            "dataframe_format": "json",
            "selectedModels": [self.model],  # Choose the model here
        }

        def for_stream():
            response = self.session.post(
                self.chat_endpoint,
                json=payload,
                headers=self.headers,
                stream=True,
                timeout=self.timeout,
            )

            if not response.ok:
                raise exceptions.FailedToGenerateResponseError(
                    f"Failed to generate response - ({response.status_code}, {response.reason})"
                )

            streaming_response = ""
            # Use sanitize_stream with content_extractor for Julius JSON format
            processed_stream = sanitize_stream(
                data=response.iter_lines(decode_unicode=True),
                intro_value=None,
                to_json=True,
                content_extractor=lambda chunk: chunk.get("content")
                if isinstance(chunk, dict)
                else None,
                yield_raw_on_error=False,
                raw=raw,
            )

            for content in processed_stream:
                if content:
                    if raw:
                        yield content if isinstance(content, str) else json.dumps(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]]:
        """Generate response `str`
        Args:
            prompt (str): Prompt to be send.
            stream (bool, optional): Flag for streaming response. Defaults to False.
            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
            **kwargs: Additional parameters including raw.
        Returns:
            str: Response generated
        """
        raw = kwargs.get("raw", False)

        def for_stream():
            for response in self.ask(
                prompt, True, raw=raw, optimizer=optimizer, conversationally=conversationally
            ):
                if raw:
                    yield cast(str, response)
                else:
                    yield self.get_message(cast(Dict[str, Any], 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(cast(Dict[str, Any], result))

        return for_stream() if stream else for_non_stream()

    def get_message(self, response: Response) -> str:
        if not isinstance(response, dict):
            return str(response)
        resp_dict = cast(Dict[str, Any], response)
        return cast(str, resp_dict["text"])


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

    ai = Julius(api_key="", timeout=5000)
    response = ai.chat("write a poem about AI", stream=True)
    if hasattr(response, "__iter__") and not isinstance(response, (str, bytes)):
        for chunk in response:
            print(chunk, end="", flush=True)
    else:
        print(response)
