import random
from typing import Any, Dict, Generator, Optional, Union
from uuid import uuid4

from curl_cffi import CurlError
from curl_cffi.requests import Session  # Import Session

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


class Venice(Provider):
    """
    A class to interact with the Venice AI API.
    """

    required_auth = False
    AVAILABLE_MODELS = [
        "mistral-31-24b",
        "dolphin-3.0-mistral-24b",
        "dolphin-3.0-mistral-24b-1dot1"
    ]

    def __init__(
        self,
        is_conversation: bool = True,
        max_tokens: int = 2000,
        timeout: int = 30,
        temperature: float = 0.8, # Keep temperature, user might want to adjust
        top_p: float = 0.9, # Keep top_p
        intro: Optional[str] = None,
        filepath: Optional[str] = None,
        update_file: bool = True,
        proxies: dict = {},
        history_offset: int = 10250,
        act: Optional[str] = None,
        model: str = "mistral-31-24b",
        # System prompt is empty in the example, but keep it configurable
        system_prompt: str = ""
    ):
        """Initialize Venice AI client"""
        if model not in self.AVAILABLE_MODELS:
            raise ValueError(f"Invalid model: {model}. Choose from: {self.AVAILABLE_MODELS}")

        # Update API endpoint
        self.api_endpoint = "https://outerface.venice.ai/api/inference/chat"
        # Initialize curl_cffi Session
        self.session = Session()
        self.is_conversation = is_conversation
        self.max_tokens_to_sample = max_tokens
        self.temperature = temperature
        self.top_p = top_p
        self.timeout = timeout
        self.model = model
        self.system_prompt = system_prompt
        self.last_response = {}

        # Update Headers based on successful request
        self.headers = {
            "User-Agent": LitAgent().random(), # Keep using LitAgent
            "accept": "*/*",
            "accept-language": "en-US,en;q=0.9", # Keep existing
            "content-type": "application/json",
            "origin": "https://venice.ai",
            "referer": "https://venice.ai/", # Update referer
            # Update sec-ch-ua to match example
            "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",
            # Update sec-fetch-site to match example
            "sec-fetch-site": "same-site",
            # Add missing headers from example
            "priority": "u=1, i",
            "sec-gpc": "1",
            "x-venice-version": "interface@20250424.065523+50bac27" # Add version header
        }

        # Update curl_cffi session headers and proxies
        self.session.headers.update(self.headers)
        self.session.proxies.update(proxies)

        self.__available_optimizers = (
            method
            for method in dir(Optimizers)
            if callable(getattr(Optimizers, method)) and not method.startswith("__")
        )
        Conversation.intro = (
            AwesomePrompts().get_act(
                act, raise_not_found=True, default=None, case_insensitive=True
            )
            if act
            else intro or Conversation.intro
        )

        self.conversation = Conversation(
            is_conversation, self.max_tokens_to_sample, filepath, update_file
        )
        self.conversation.history_offset = history_offset

    @staticmethod
    def _venice_extractor(chunk: Union[str, Dict[str, Any]]) -> Optional[str]:
        """Extracts content from Venice stream JSON objects."""
        if isinstance(chunk, dict) and chunk.get("kind") == "content":
            return chunk.get("content")
        return None


    def ask(
        self,
        prompt: str,
        stream: bool = False,
        raw: bool = False,
        optimizer: Optional[str] = None,
        conversationally: bool = False,
        **kwargs: Any,
    ) -> Response:
        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 = {
            "requestId": str(uuid4())[:7],
            "modelId": self.model,
            "prompt": [{"content": conversation_prompt, "role": "user"}],
            "systemPrompt": self.system_prompt,
            "conversationType": "text",
            "temperature": self.temperature,
            "webEnabled": True,
            "topP": self.top_p,
            "includeVeniceSystemPrompt": True,
            "isCharacter": False,
            "userId": "user_anon_" + str(random.randint(1000000000, 9999999999)),
            "isDefault": True,
            "textToSpeech": {"voiceId": "af_sky", "speed": 1},
            "clientProcessingTime": random.randint(10, 50)
        }
        def for_stream():
            try:
                response = self.session.post(
                    self.api_endpoint,
                    json=payload,
                    stream=True,
                    timeout=self.timeout,
                    impersonate="edge101"
                )
                if response.status_code != 200:
                    raise exceptions.FailedToGenerateResponseError(
                        f"Request failed with status code {response.status_code} - {response.text}"
                    )
                streaming_text = ""
                processed_stream = sanitize_stream(
                    data=response.iter_content(chunk_size=None),
                    intro_value=None,
                    to_json=True,
                    content_extractor=self._venice_extractor,
                    yield_raw_on_error=False,
                    raw=raw
                )
                for content_chunk in processed_stream:
                    # Always yield as string, even in raw mode
                    if isinstance(content_chunk, bytes):
                        content_chunk = content_chunk.decode('utf-8', errors='ignore')
                    if raw:
                        yield content_chunk
                    else:
                        if content_chunk and isinstance(content_chunk, str):
                            streaming_text += content_chunk
                            yield dict(text=content_chunk)
                self.conversation.update_chat_history(prompt, streaming_text)
                self.last_response = {"text": streaming_text}
            except CurlError as e:
                raise exceptions.FailedToGenerateResponseError(f"Request failed (CurlError): {e}")
            except Exception as e:
                raise exceptions.FailedToGenerateResponseError(f"An unexpected error occurred ({type(e).__name__}): {e}")
        def for_non_stream():
            full_text = ""
            for chunk_data in for_stream():
                if isinstance(chunk_data, dict) and "text" in chunk_data:
                    full_text += chunk_data["text"]
                elif isinstance(chunk_data, str):
                     full_text += chunk_data
            self.last_response = {"text": full_text}
            return 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,
        raw: bool = False,  # Added raw parameter
    ) -> Union[str, Generator[str, None, None]]:
        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 result
            else:
                return self.get_message(result)
        return for_stream() if stream else for_non_stream()

    def get_message(self, response: dict) -> str:
        assert isinstance(response, dict), "Response should be of dict data-type only"
        return response["text"]

if __name__ == "__main__":
    # Ensure curl_cffi is installed
    print("-" * 80)
    print(f"{'Model':<50} {'Status':<10} {'Response'}")
    print("-" * 80)

    # Test all available models
    working = 0
    total = len(Venice.AVAILABLE_MODELS)

    for model in Venice.AVAILABLE_MODELS:
        try:
            test_ai = Venice(model=model, timeout=60)
            response = test_ai.chat("Say 'Hello' in one word", stream=True)
            response_text = ""
            if hasattr(response, "__iter__") and not isinstance(response, (str, bytes)):
                for chunk in response:
                    response_text += chunk
                    print(f"\r{model:<50} {'Testing...':<10}", end="", flush=True)
            else:
                response_text = str(response)

            if response_text and len(response_text.strip()) > 0:
                status = "✓"
                # Truncate response if too long
                display_text = response_text.strip()[:50] + "..." if len(response_text.strip()) > 50 else response_text.strip()
            else:
                status = "✗"
                display_text = "Empty or invalid response"
            print(f"\r{model:<50} {status:<10} {display_text}")
        except Exception as e:
            print(f"\r{model:<50} {'✗':<10} {str(e)}")
