from __future__ import annotations

import re
import random
import string
import uuid
import json
from aiohttp import ClientSession
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
from datetime import datetime
from fastapi.responses import StreamingResponse

# Custom exception for model not working
class ModelNotWorkingException(Exception):
    def __init__(self, model: str):
        self.model = model
        self.message = f"The model '{model}' is currently not working. Please wait for NiansuhAI to fix this. Thank you for your patience."
        super().__init__(self.message)

# Mock implementations for ImageResponse and to_data_uri
class ImageResponse:
    def __init__(self, url: str, alt: str):
        self.url = url
        self.alt = alt

def to_data_uri(image: Any) -> str:
    return "data:image/png;base64,..."  # Replace with actual base64 data

class AsyncGeneratorProvider:
    pass

class ProviderModelMixin:
    pass

class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
    url = "https://www.blackbox.ai"
    api_endpoint = "https://www.blackbox.ai/api/chat"
    working = True
    supports_stream = True
    supports_system_message = True
    supports_message_history = True
    
    default_model = 'blackbox'
    models = [
        'blackbox',
        'gemini-1.5-flash',
        "llama-3.1-8b",
        'llama-3.1-70b',  # Example of a non-working model
        'llama-3.1-405b',
        'ImageGenerationLV45LJp',
        'gpt-4o',
        'gemini-pro',
        'claude-sonnet-3.5',
    ]

    # Define the working status of models
    model_status = {
        'blackbox': True,
        'gemini-1.5-flash': True,
        'llama-3.1-8b': True,
        'llama-3.1-70b': False,  # Non-working model
        'llama-3.1-405b': True,
        'ImageGenerationLV45LJp': True,
        'gpt-4o': True,
        'gemini-pro': True,
        'claude-sonnet-3.5': True,
    }

    agentMode = {
        'ImageGenerationLV45LJp': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
    }

    trendingAgentMode = {
        "blackbox": {},
        "gemini-1.5-flash": {'mode': True, 'id': 'Gemini'},
        "llama-3.1-8b": {'mode': True, 'id': "llama-3.1-8b"},
        'llama-3.1-70b': {'mode': True, 'id': "llama-3.1-70b"},
        'llama-3.1-405b': {'mode': True, 'id': "llama-3.1-405b"},
    }
    
    userSelectedModel = {
        "gpt-4o": "gpt-4o",
        "gemini-pro": "gemini-pro",
        'claude-sonnet-3.5': "claude-sonnet-3.5",
    }
    
    model_aliases = {
        "gemini-flash": "gemini-1.5-flash",
        "flux": "ImageGenerationLV45LJp",
    }

    @classmethod
    def get_model(cls, model: str) -> str:
        if model in cls.models:
            return model
        elif model in cls.userSelectedModel:
            return model
        elif model in cls.model_aliases:
            return cls.model_aliases[model]
        else:
            return cls.default_model

    @classmethod
    async def create_async_generator(
        cls,
        model: str,
        messages: List[Dict[str, str]],
        proxy: Optional[str] = None,
        image: Optional[Any] = None,
        image_name: Optional[str] = None,
        **kwargs
    ) -> Any:
        model = cls.get_model(model)

        # Check if the model is working
        if not cls.model_status.get(model, False):
            raise ModelNotWorkingException(model)

        headers = {
            "accept": "*/*",
            "accept-language": "en-US,en;q=0.9",
            "cache-control": "no-cache",
            "content-type": "application/json",
            "origin": cls.url,
            "pragma": "no-cache",
            "referer": f"{cls.url}/",
            "sec-ch-ua": '"Not;A=Brand";v="24", "Chromium";v="128"',
            "sec-ch-ua-mobile": "?0",
            "sec-ch-ua-platform": '"Linux"',
            "sec-fetch-dest": "empty",
            "sec-fetch-mode": "cors",
            "sec-fetch-site": "same-origin",
            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36"
        }

        if model in cls.userSelectedModel:
            prefix = f"@{cls.userSelectedModel[model]}"
            if not messages[0]['content'].startswith(prefix):
                messages[0]['content'] = f"{prefix} {messages[0]['content']}"
        
        async with ClientSession(headers=headers) as session:
            if image is not None:
                messages[-1]["data"] = {
                    "fileText": image_name,
                    "imageBase64": to_data_uri(image)
                }
            
            random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7))

            data = {
                "messages": messages,
                "id": random_id,
                "previewToken": None,
                "userId": None,
                "codeModelMode": True,
                "agentMode": {},
                "trendingAgentMode": {},
                "userSelectedModel": None,
                "userSystemPrompt": None,
                "isMicMode": False,
                "maxTokens": 4096,
                "playgroundTopP": 0.9,
                "playgroundTemperature": 0.5,
                "isChromeExt": False,
                "githubToken": None,
                "clickedAnswer2": False,
                "clickedAnswer3": False,
                "clickedForceWebSearch": False,
                "visitFromDelta": False,
                "mobileClient": False,
                "webSearchMode": False,
            }

            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
                response.raise_for_status()
                if model == 'ImageGenerationLV45LJp':
                    response_text = await response.text()
                    url_match = re.search(r'https://storage\.googleapis\.com/[^\s\)]+', response_text)
                    if url_match:
                        image_url = url_match.group(0)
                        yield ImageResponse(image_url, alt=messages[-1]['content'])
                    else:
                        raise Exception("Image URL not found in the response")
                else:
                    response_content = ""
                    async for chunk in response.content.iter_any():
                        if chunk:
                            decoded_chunk = chunk.decode(errors='ignore')
                            decoded_chunk = re.sub(r'\$@\$v=[^$]+\$@\$', '', decoded_chunk)
                            if decoded_chunk.strip():
                                response_content += decoded_chunk

                    # Check if the response content is empty
                    if not response_content.strip():
                        raise ModelNotWorkingException(model)

                    yield response_content

# FastAPI app setup
app = FastAPI()

class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    model: str
    messages: List[Message]
    stream: Optional[bool] = False  # Add this for streaming

def create_response(content: str, model: str, finish_reason: Optional[str] = None) -> Dict[str, Any]:
    return {
        "id": f"chatcmpl-{uuid.uuid4()}",
        "object": "chat.completion.chunk",
        "created": int(datetime.now().timestamp()),
        "model": model,
        "choices": [
            {
                "index": 0,
                "delta": {"content": content, "role": "assistant"},
                "finish_reason": finish_reason,
            }
        ],
        "usage": None,
    }

@app.post("/niansuhai/v1/chat/completions")
async def chat_completions(request: ChatRequest):
    # Validate the model
    valid_models = Blackbox.models + list(Blackbox.userSelectedModel.keys()) + list(Blackbox.model_aliases.keys())
    if request.model not in valid_models:
        raise HTTPException(status_code=400, detail=f"Invalid model name: {request.model}. Valid models are: {valid_models}")

    messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]

    try:
        async_generator = Blackbox.create_async_generator(
            model=request.model,
            messages=messages,
            image=None,  # Pass the image if required
            image_name=None  # Pass image name if required
        )
    except ModelNotWorkingException as e:
        raise HTTPException(status_code=503, detail=str(e))

    if request.stream:
        async def generate():
            async for chunk in async_generator:
                if isinstance(chunk, ImageResponse):
                    image_markdown = f"![image]({chunk.url})"
                    yield f"data: {json.dumps(create_response(image_markdown, request.model))}\n\n"
                else:
                    yield f"data: {json.dumps(create_response(chunk + '\nNiansuhAI', request.model))}\n\n"
            yield "data: [DONE]\n\n"

        return StreamingResponse(generate(), media_type="text/event-stream")
    else:
        response_content = ""
        async for chunk in async_generator:
            if isinstance(chunk, ImageResponse):
                response_content += f"![image]({chunk.url})\n"
            else:
                response_content += chunk
        
        # Append "\nNiansuhAI" to the final response content
        response_content += "\n**NiansuhAI**"

        return {
            "id": f"chatcmpl-{uuid.uuid4()}",
            "object": "chat.completion",
            "created": int(datetime.now().timestamp()),
            "model": request.model,
            "choices": [
                {
                    "message": {
                        "role": "assistant",
                        "content": response_content
                    },
                    "finish_reason": "stop",
                    "index": 0
                }
            ],
            "usage": None,
        }


@app.get("/niansuhai/v1/models")
async def get_models():
    return {"models": Blackbox.models}