main.py

import os
import re
import random
import string
import uuid
import json
import logging
import asyncio
import time
from collections import defaultdict
from typing import List, Dict, Any, Optional, AsyncGenerator, Union

from datetime import datetime

from aiohttp import ClientSession, ClientTimeout, ClientError
from fastapi import FastAPI, HTTPException, Request, Depends, Header
from fastapi.responses import StreamingResponse, JSONResponse, RedirectResponse
from pydantic import BaseModel

from PIL import Image
import base64
from io import BytesIO

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

# Load environment variables
API_KEYS = os.getenv('API_KEYS', '').split(',')  # Comma-separated API keys
RATE_LIMIT = int(os.getenv('RATE_LIMIT', '60'))  # Requests per minute
AVAILABLE_MODELS = os.getenv('AVAILABLE_MODELS', '')  # Comma-separated available models

if not API_KEYS or API_KEYS == ['']:
    logger.error("No API keys found. Please set the API_KEYS environment variable.")
    raise Exception("API_KEYS environment variable not set.")

# Process available models
if AVAILABLE_MODELS:
    AVAILABLE_MODELS = [model.strip() for model in AVAILABLE_MODELS.split(',') if model.strip()]
else:
    AVAILABLE_MODELS = []  # If empty, all models are available

# Simple in-memory rate limiter based solely on IP addresses
rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})

# Define cleanup interval and window
CLEANUP_INTERVAL = 60  # seconds
RATE_LIMIT_WINDOW = 60  # seconds

async def cleanup_rate_limit_stores():
    """
    Periodically cleans up stale entries in the rate_limit_store to prevent memory bloat.
    """
    while True:
        current_time = time.time()
        ips_to_delete = [ip for ip, value in rate_limit_store.items() if current_time - value["timestamp"] > RATE_LIMIT_WINDOW * 2]
        for ip in ips_to_delete:
            del rate_limit_store[ip]
            logger.debug(f"Cleaned up rate_limit_store for IP: {ip}")
        await asyncio.sleep(CLEANUP_INTERVAL)

async def rate_limiter_per_ip(request: Request):
    """
    Rate limiter that enforces a limit based on the client's IP address.
    """
    client_ip = request.client.host
    current_time = time.time()

    # Initialize or update the count and timestamp
    if current_time - rate_limit_store[client_ip]["timestamp"] > RATE_LIMIT_WINDOW:
        rate_limit_store[client_ip] = {"count": 1, "timestamp": current_time}
    else:
        if rate_limit_store[client_ip]["count"] >= RATE_LIMIT:
            logger.warning(f"Rate limit exceeded for IP address: {client_ip}")
            raise HTTPException(status_code=429, detail='Rate limit exceeded for IP address | NiansuhAI')
        rate_limit_store[client_ip]["count"] += 1

async def get_api_key(request: Request, authorization: str = Header(None)) -> str:
    """
    Dependency to extract and validate the API key from the Authorization header.
    """
    client_ip = request.client.host
    if authorization is None or not authorization.startswith('Bearer '):
        logger.warning(f"Invalid or missing authorization header from IP: {client_ip}")
        raise HTTPException(status_code=401, detail='Invalid authorization header format')
    api_key = authorization[7:]
    if api_key not in API_KEYS:
        logger.warning(f"Invalid API key attempted: {api_key} from IP: {client_ip}")
        raise HTTPException(status_code=401, detail='Invalid API key')
    return api_key

# 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 try another model or wait for it to be fixed."
        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 Blackbox:
    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 = 'blackboxai'
    image_models = ['ImageGeneration']
    models = [
        default_model,
        'blackboxai-pro',
        "llama-3.1-8b",
        'llama-3.1-70b',
        'llama-3.1-405b',
        'gpt-4o',
        'gemini-pro',
        'gemini-1.5-flash',
        'claude-sonnet-3.5',
        'PythonAgent',
        'JavaAgent',
        'JavaScriptAgent',
        'HTMLAgent',
        'GoogleCloudAgent',
        'AndroidDeveloper',
        'SwiftDeveloper',
        'Next.jsAgent',
        'MongoDBAgent',
        'PyTorchAgent',
        'ReactAgent',
        'XcodeAgent',
        'AngularJSAgent',
        *image_models,
        'Niansuh',
    ]

    # Filter models based on AVAILABLE_MODELS
    if AVAILABLE_MODELS:
        models = [model for model in models if model in AVAILABLE_MODELS]

    agentMode = {
        'ImageGeneration': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
        'Niansuh': {'mode': True, 'id': "NiansuhAIk1HgESy", 'name': "Niansuh"},
    }
    trendingAgentMode = {
        "blackboxai": {},
        "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"},
        'blackboxai-pro': {'mode': True, 'id': "BLACKBOXAI-PRO"},
        'PythonAgent': {'mode': True, 'id': "Python Agent"},
        'JavaAgent': {'mode': True, 'id': "Java Agent"},
        'JavaScriptAgent': {'mode': True, 'id': "JavaScript Agent"},
        'HTMLAgent': {'mode': True, 'id': "HTML Agent"},
        'GoogleCloudAgent': {'mode': True, 'id': "Google Cloud Agent"},
        'AndroidDeveloper': {'mode': True, 'id': "Android Developer"},
        'SwiftDeveloper': {'mode': True, 'id': "Swift Developer"},
        'Next.jsAgent': {'mode': True, 'id': "Next.js Agent"},
        'MongoDBAgent': {'mode': True, 'id': "MongoDB Agent"},
        'PyTorchAgent': {'mode': True, 'id': "PyTorch Agent"},
        'ReactAgent': {'mode': True, 'id': "React Agent"},
        'XcodeAgent': {'mode': True, 'id': "Xcode Agent"},
        'AngularJSAgent': {'mode': True, 'id': "AngularJS Agent"},
    }

    userSelectedModel = {
        "gpt-4o": "gpt-4o",
        "gemini-pro": "gemini-pro",
        'claude-sonnet-3.5': "claude-sonnet-3.5",
    }

    model_prefixes = {
        'gpt-4o': '@GPT-4o',
        'gemini-pro': '@Gemini-PRO',
        'claude-sonnet-3.5': '@Claude-Sonnet-3.5',
        'PythonAgent': '@Python Agent',
        'JavaAgent': '@Java Agent',
        'JavaScriptAgent': '@JavaScript Agent',
        'HTMLAgent': '@HTML Agent',
        'GoogleCloudAgent': '@Google Cloud Agent',
        'AndroidDeveloper': '@Android Developer',
        'SwiftDeveloper': '@Swift Developer',
        'Next.jsAgent': '@Next.js Agent',
        'MongoDBAgent': '@MongoDB Agent',
        'PyTorchAgent': '@PyTorch Agent',
        'ReactAgent': '@React Agent',
        'XcodeAgent': '@Xcode Agent',
        'AngularJSAgent': '@AngularJS Agent',
        'blackboxai-pro': '@BLACKBOXAI-PRO',
        'ImageGeneration': '@Image Generation',
        'Niansuh': '@Niansuh',
    }

    model_referers = {
        "blackboxai": f"{url}/?model=blackboxai",
        "gpt-4o": f"{url}/?model=gpt-4o",
        "gemini-pro": f"{url}/?model=gemini-pro",
        "claude-sonnet-3.5": f"{url}/?model=claude-sonnet-3.5"
    }

    model_aliases = {
        "gemini-flash": "gemini-1.5-flash",
        "claude-3.5-sonnet": "claude-sonnet-3.5",
        "flux": "ImageGeneration",
        "niansuh": "Niansuh",
    }

    @classmethod
    def get_model(cls, model: str) -> Optional[str]:
        if model in cls.models:
            return model
        elif model in cls.userSelectedModel and cls.userSelectedModel[model] in cls.models:
            return cls.userSelectedModel[model]
        elif model in cls.model_aliases and cls.model_aliases[model] in cls.models:
            return cls.model_aliases[model]
        else:
            return cls.default_model if cls.default_model in cls.models else None

    @classmethod
    async def create_async_generator(
        cls,
        model: str,
        messages: List[Dict[str, str]],
        proxy: Optional[str] = None,
        image: Any = None,
        image_name: Optional[str] = None,
        webSearchMode: bool = False,
        **kwargs
    ) -> AsyncGenerator[Any, None]:
        model = cls.get_model(model)
        if model is None:
            logger.error(f"Model {model} is not available.")
            raise ModelNotWorkingException(model)

        logger.info(f"Selected model: {model}")

        if not cls.working or model not in cls.models:
            logger.error(f"Model {model} is not working or not supported.")
            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",
            "priority": "u=1, i",
            "referer": cls.model_referers.get(model, cls.url),
            "sec-ch-ua": '"Chromium";v="129", "Not=A?Brand";v="8"',
            "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/129.0.0.0 Safari/537.36",
        }

        if model in cls.model_prefixes:
            prefix = cls.model_prefixes[model]
            if not messages[0]['content'].startswith(prefix):
                logger.debug(f"Adding prefix '{prefix}' to the first message.")
                messages[0]['content'] = f"{prefix} {messages[0]['content']}"
        
        random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7))
        messages[-1]['id'] = random_id
        messages[-1]['role'] = 'user'

        # Don't log the full message content for privacy
        logger.debug(f"Generated message ID: {random_id} for model: {model}")

        if image is not None:
            messages[-1]['data'] = {
                'fileText': '',
                'imageBase64': to_data_uri(image),
                'title': image_name
            }
            messages[-1]['content'] = 'FILE:BB\n$#$\n\n$#$\n' + messages[-1]['content']
            logger.debug("Image data added to the message.")
        
        data = {
            "messages": messages,
            "id": random_id,
            "previewToken": None,
            "userId": None,
            "codeModelMode": True,
            "agentMode": {},
            "trendingAgentMode": {},
            "isMicMode": False,
            "userSystemPrompt": None,
            "maxTokens": 99999999,
            "playgroundTopP": 0.9,
            "playgroundTemperature": 0.5,
            "isChromeExt": False,
            "githubToken": None,
            "clickedAnswer2": False,
            "clickedAnswer3": False,
            "clickedForceWebSearch": False,
            "visitFromDelta": False,
            "mobileClient": False,
            "userSelectedModel": None,
            "webSearchMode": webSearchMode,
        }

        if model in cls.agentMode:
            data["agentMode"] = cls.agentMode[model]
        elif model in cls.trendingAgentMode:
            data["trendingAgentMode"] = cls.trendingAgentMode[model]
        elif model in cls.userSelectedModel:
            data["userSelectedModel"] = cls.userSelectedModel[model]
        logger.info(f"Sending request to {cls.api_endpoint} with data (excluding messages).")

        timeout = ClientTimeout(total=60)  # Set an appropriate timeout
        retry_attempts = 10  # Set the number of retry attempts

        for attempt in range(retry_attempts):
            try:
                async with ClientSession(headers=headers, timeout=timeout) as session:
                    async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
                        response.raise_for_status()
                        logger.info(f"Received response with status {response.status}")
                        if model == 'ImageGeneration':
                            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)
                                logger.info(f"Image URL found.")
                                yield ImageResponse(image_url, alt=messages[-1]['content'])
                            else:
                                logger.error("Image URL not found in the response.")
                                raise Exception("Image URL not found in the response")
                        else:
                            full_response = ""
                            search_results_json = ""
                            try:
                                async for chunk, _ in response.content.iter_chunks():
                                    if chunk:
                                        decoded_chunk = chunk.decode(errors='ignore')
                                        decoded_chunk = re.sub(r'\$@\$v=[^$]+\$@\$', '', decoded_chunk)
                                        if decoded_chunk.strip():
                                            if '$~~~$' in decoded_chunk:
                                                search_results_json += decoded_chunk
                                            else:
                                                full_response += decoded_chunk
                                                yield decoded_chunk
                                logger.info("Finished streaming response chunks.")
                            except Exception as e:
                                logger.exception("Error while iterating over response chunks.")
                                raise e
                            if data["webSearchMode"] and search_results_json:
                                match = re.search(r'\$~~~\$(.*?)\$~~~\$', search_results_json, re.DOTALL)
                                if match:
                                    try:
                                        search_results = json.loads(match.group(1))
                                        formatted_results = "\n\n**Sources:**\n"
                                        for i, result in enumerate(search_results[:5], 1):
                                            formatted_results += f"{i}. [{result['title']}]({result['link']})\n"
                                        logger.info("Formatted search results.")
                                        yield formatted_results
                                    except json.JSONDecodeError as je:
                                        logger.error("Failed to parse search results JSON.")
                                        raise je
            except ClientError as ce:
                logger.error(f"Client error occurred: {ce}. Retrying attempt {attempt + 1}/{retry_attempts}")
                if attempt == retry_attempts - 1:
                    raise HTTPException(status_code=502, detail="Error communicating with the external API.")
            except asyncio.TimeoutError:
                logger.error(f"Request timed out. Retrying attempt {attempt + 1}/{retry_attempts}")
                if attempt == retry_attempts - 1:
                    raise HTTPException(status_code=504, detail="External API request timed out.")
            except Exception as e:
                logger.error(f"Unexpected error: {e}. Retrying attempt {attempt + 1}/{retry_attempts}")
                if attempt == retry_attempts - 1:
                    raise HTTPException(status_code=500, detail=str(e))

# FastAPI app setup
app = FastAPI()

# Add the cleanup task when the app starts
@app.on_event("startup")
async def startup_event():
    asyncio.create_task(cleanup_rate_limit_stores())
    logger.info("Started rate limit store cleanup task.")

# Middleware to enhance security and enforce Content-Type for specific endpoints
@app.middleware("http")
async def security_middleware(request: Request, call_next):
    client_ip = request.client.host
    # Enforce that POST requests to /v1/chat/completions must have Content-Type: application/json
    if request.method == "POST" and request.url.path == "/v1/chat/completions":
        content_type = request.headers.get("Content-Type")
        if content_type != "application/json":
            logger.warning(f"Invalid Content-Type from IP: {client_ip} for path: {request.url.path}")
            return JSONResponse(
                status_code=400,
                content={
                    "error": {
                        "message": "Content-Type must be application/json",
                        "type": "invalid_request_error",
                        "param": None,
                        "code": None
                    }
                },
            )
    response = await call_next(request)
    return response

# Request Models
class Message(BaseModel):
    role: str
    content: Union[str, List[Any]]  # content can be a string or a list (for images)

class ChatRequest(BaseModel):
    model: str
    messages: List[Message]
    temperature: Optional[float] = 1.0
    top_p: Optional[float] = 1.0
    n: Optional[int] = 1
    stream: Optional[bool] = False
    stop: Optional[Union[str, List[str]]] = None
    max_tokens: Optional[int] = None
    presence_penalty: Optional[float] = 0.0
    frequency_penalty: Optional[float] = 0.0
    logit_bias: Optional[Dict[str, float]] = None
    user: Optional[str] = None
    webSearchMode: Optional[bool] = False  # Custom parameter
    image: Optional[str] = None  # Base64-encoded image

class TokenizerRequest(BaseModel):
    text: str

def calculate_estimated_cost(prompt_tokens: int, completion_tokens: int) -> float:
    """
    Calculate the estimated cost based on the number of tokens.
    Replace the pricing below with your actual pricing model.
    """
    # Example pricing: $0.00000268 per token
    cost_per_token = 0.00000268
    return round((prompt_tokens + completion_tokens) * cost_per_token, 8)

def create_response(content: str, model: str, finish_reason: Optional[str] = None) -> Dict[str, Any]:
    return {
        "id": f"chatcmpl-{uuid.uuid4()}",
        "object": "chat.completion",
        "created": int(datetime.now().timestamp()),
        "model": model,
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": content
                },
                "finish_reason": finish_reason
            }
        ],
        "usage": None,  # To be filled in non-streaming responses
    }

@app.post("/v1/chat/completions", dependencies=[Depends(rate_limiter_per_ip)])
async def chat_completions(request: ChatRequest, req: Request, api_key: str = Depends(get_api_key)):
    client_ip = req.client.host
    # Redact user messages only for logging purposes
    redacted_messages = [{"role": msg.role, "content": "[redacted]"} for msg in request.messages]

    logger.info(f"Received chat completions request from API key: {api_key} | IP: {client_ip} | Model: {request.model} | Messages: {redacted_messages}")

    analysis_result = None
    if request.image:
        try:
            image = decode_base64_image(request.image)
            analysis_result = analyze_image(image)
            logger.info("Image analysis completed successfully.")
        except HTTPException as he:
            logger.error(f"Image analysis failed: {he.detail}")
            raise he
        except Exception as e:
            logger.exception("Unexpected error during image analysis.")
            raise HTTPException(status_code=500, detail="Image analysis failed.") from e

    # Prepare messages to send to the external API, excluding image data
    processed_messages = []
    for msg in request.messages:
        if isinstance(msg.content, list) and len(msg.content) == 2:
            # Assume the second item is image data, skip it
            processed_messages.append({
                "role": msg.role,
                "content": msg.content[0]["text"]  # Only include the text part
            })
        else:
            processed_messages.append({
                "role": msg.role,
                "content": msg.content
            })

    # Create a modified ChatRequest without the image
    modified_request = ChatRequest(
        model=request.model,
        messages=[msg for msg in processed_messages],
        stream=request.stream,
        temperature=request.temperature,
        top_p=request.top_p,
        max_tokens=request.max_tokens,
        presence_penalty=request.presence_penalty,
        frequency_penalty=request.frequency_penalty,
        logit_bias=request.logit_bias,
        user=request.user,
        webSearchMode=request.webSearchMode,
        image=None  # Exclude image from external API
    )

    try:
        if request.stream:
            logger.info("Streaming response")
            # **Removed the 'await' keyword here**
            streaming_response = Blackbox.create_async_generator(
                model=modified_request.model,
                messages=[{"role": msg.role, "content": msg.content} for msg in modified_request.messages],
                proxy=None,
                image=None,
                image_name=None,
                webSearchMode=modified_request.webSearchMode
            )

            # Wrap the streaming generator to include image analysis at the end
            async def generate_with_analysis():
                assistant_content = ""
                try:
                    async for chunk in streaming_response:
                        if isinstance(chunk, ImageResponse):
                            # Handle image responses if necessary
                            image_markdown = f"![image]({chunk.url})\n"
                            assistant_content += image_markdown
                            response_chunk = create_response(image_markdown, modified_request.model, finish_reason=None)
                        else:
                            assistant_content += chunk
                            # Yield the chunk as a partial choice
                            response_chunk = {
                                "id": f"chatcmpl-{uuid.uuid4()}",
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp()),
                                "model": modified_request.model,
                                "choices": [
                                    {
                                        "index": 0,
                                        "delta": {"content": chunk, "role": "assistant"},
                                        "finish_reason": None,
                                    }
                                ],
                                "usage": None,  # Usage can be updated if you track tokens in real-time
                            }
                        yield f"data: {json.dumps(response_chunk)}\n\n"
                    
                    # After all chunks are sent, send the final message with finish_reason
                    prompt_tokens = sum(len(msg["content"].split()) for msg in modified_request.messages)
                    completion_tokens = len(assistant_content.split())
                    total_tokens = prompt_tokens + completion_tokens
                    estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens)

                    final_content = assistant_content
                    if analysis_result:
                        final_content += f"\n\n**Image Analysis:** {analysis_result}"

                    final_response = {
                        "id": f"chatcmpl-{uuid.uuid4()}",
                        "object": "chat.completion",
                        "created": int(datetime.now().timestamp()),
                        "model": modified_request.model,
                        "choices": [
                            {
                                "message": {
                                    "role": "assistant",
                                    "content": final_content
                                },
                                "finish_reason": "stop",
                                "index": 0
                            }
                        ],
                        "usage": {
                            "prompt_tokens": prompt_tokens,
                            "completion_tokens": completion_tokens,
                            "total_tokens": total_tokens,
                            "estimated_cost": estimated_cost
                        },
                    }

                    yield f"data: {json.dumps(final_response)}\n\n"
                    yield "data: [DONE]\n\n"
                except HTTPException as he:
                    error_response = {"error": he.detail}
                    yield f"data: {json.dumps(error_response)}\n\n"
                except Exception as e:
                    logger.exception(f"Error during streaming response generation from IP: {client_ip}.")
                    error_response = {"error": str(e)}
                    yield f"data: {json.dumps(error_response)}\n\n"

            return StreamingResponse(generate_with_analysis(), media_type="text/event-stream")
        else:
            logger.info("Non-streaming response")
            # **Removed the 'await' keyword here as well**
            streaming_response = Blackbox.create_async_generator(
                model=modified_request.model,
                messages=[{"role": msg.role, "content": msg.content} for msg in modified_request.messages],
                proxy=None,
                image=None,
                image_name=None,
                webSearchMode=modified_request.webSearchMode
            )

            response_content = ""
            async for chunk in streaming_response:
                if isinstance(chunk, ImageResponse):
                    response_content += f"![image]({chunk.url})\n"
                else:
                    response_content += chunk

            prompt_tokens = sum(len(msg["content"].split()) for msg in modified_request.messages)
            completion_tokens = len(response_content.split())
            total_tokens = prompt_tokens + completion_tokens
            estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens)

            if analysis_result:
                response_content += f"\n\n**Image Analysis:** {analysis_result}"

            logger.info(f"Completed non-streaming response generation for API key: {api_key} | IP: {client_ip}")

            response = {
                "id": f"chatcmpl-{uuid.uuid4()}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": modified_request.model,
                "choices": [
                    {
                        "message": {
                            "role": "assistant",
                            "content": response_content
                        },
                        "finish_reason": "stop",
                        "index": 0
                    }
                ],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": completion_tokens,
                    "total_tokens": total_tokens,
                    "estimated_cost": estimated_cost
                },
            }

            return response
    except ModelNotWorkingException as e:
        logger.warning(f"Model not working: {e} | IP: {client_ip}")
        raise HTTPException(status_code=503, detail=str(e))
    except HTTPException as he:
        logger.warning(f"HTTPException: {he.detail} | IP: {client_ip}")
        raise he
    except Exception as e:
        logger.exception(f"An unexpected error occurred while processing the chat completions request from IP: {client_ip}.")
        raise HTTPException(status_code=500, detail=str(e))

# Endpoint: POST /v1/tokenizer
@app.post("/v1/tokenizer", dependencies=[Depends(rate_limiter_per_ip)])
async def tokenizer(request: TokenizerRequest, req: Request):
    client_ip = req.client.host
    text = request.text
    token_count = len(text.split())
    logger.info(f"Tokenizer requested from IP: {client_ip} | Text length: {len(text)}")
    return {"text": text, "tokens": token_count}

# Endpoint: GET /v1/models
@app.get("/v1/models", dependencies=[Depends(rate_limiter_per_ip)])
async def get_models(req: Request):
    client_ip = req.client.host
    logger.info(f"Fetching available models from IP: {client_ip}")
    return {"data": [{"id": model, "object": "model"} for model in Blackbox.models]}

# Endpoint: GET /v1/models/{model}/status
@app.get("/v1/models/{model}/status", dependencies=[Depends(rate_limiter_per_ip)])
async def model_status(model: str, req: Request):
    client_ip = req.client.host
    logger.info(f"Model status requested for '{model}' from IP: {client_ip}")
    if model in Blackbox.models:
        return {"model": model, "status": "available"}
    elif model in Blackbox.model_aliases and Blackbox.model_aliases[model] in Blackbox.models:
        actual_model = Blackbox.model_aliases[model]
        return {"model": actual_model, "status": "available via alias"}
    else:
        logger.warning(f"Model not found: {model} from IP: {client_ip}")
        raise HTTPException(status_code=404, detail="Model not found")

# Endpoint: GET /v1/health
@app.get("/v1/health", dependencies=[Depends(rate_limiter_per_ip)])
async def health_check(req: Request):
    client_ip = req.client.host
    logger.info(f"Health check requested from IP: {client_ip}")
    return {"status": "ok"}

# Endpoint: GET /v1/chat/completions (GET method)
@app.get("/v1/chat/completions")
async def chat_completions_get(req: Request):
    client_ip = req.client.host
    logger.info(f"GET request made to /v1/chat/completions from IP: {client_ip}, redirecting to 'about:blank'")
    return RedirectResponse(url='about:blank')

# Custom exception handler to match OpenAI's error format
@app.exception_handler(HTTPException)
async def http_exception_handler(request: Request, exc: HTTPException):
    client_ip = request.client.host
    logger.error(f"HTTPException: {exc.detail} | Path: {request.url.path} | IP: {client_ip}")
    return JSONResponse(
        status_code=exc.status_code,
        content={
            "error": {
                "message": exc.detail,
                "type": "invalid_request_error",
                "param": None,
                "code": None
            }
        },
    )

# Image Processing Utilities
def decode_base64_image(base64_str: str) -> Image.Image:
    try:
        image_data = base64.b64decode(base64_str)
        image = Image.open(BytesIO(image_data))
        return image
    except Exception as e:
        logger.error("Failed to decode base64 image.")
        raise HTTPException(status_code=400, detail="Invalid base64 image data.") from e

def analyze_image(image: Image.Image) -> str:
    """
    Placeholder for image analysis.
    Replace this with actual image analysis logic.
    """
    # Example: Return image size as analysis
    width, height = image.size
    return f"Image analyzed successfully. Width: {width}px, Height: {height}px."

# Run the application
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)