Update main.py

main.py

@@ -1,5 +1,607 @@
-import uvicorn
-from api.app import app
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8001)
+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
+
+# 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 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,
+            "validated": "00f37b34-a166-4efb-bce5-1312d87f2f94"
+        }
+
+        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
+                break  # Exit the retry loop if successful
+            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: str
+
+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
+
+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("/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}")
+
+    try:
+        # Validate that the requested model is available
+        if request.model not in Blackbox.models and request.model not in Blackbox.model_aliases:
+            logger.warning(f"Attempt to use unavailable model: {request.model} from IP: {client_ip}")
+            raise HTTPException(status_code=400, detail="Requested model is not available.")
+
+        # Process the request with actual message content, but don't log it
+        async_generator = Blackbox.create_async_generator(
+            model=request.model,
+            messages=[{"role": msg.role, "content": msg.content} for msg in request.messages],  # Actual message content used here
+            image=None,
+            image_name=None,
+            webSearchMode=request.webSearchMode
+        )
+
+        if request.stream:
+            async def generate():
+                try:
+                    async for chunk in async_generator:
+                        if isinstance(chunk, ImageResponse):
+                            image_markdown = f"![image]({chunk.url})"
+                            response_chunk = create_response(image_markdown, request.model)
+                        else:
+                            response_chunk = create_response(chunk, request.model)
+                        
+                        yield f"data: {json.dumps(response_chunk)}\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(), 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
+
+            logger.info(f"Completed non-streaming response generation for API key: {api_key} | IP: {client_ip}")
+            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": {
+                    "prompt_tokens": sum(len(msg.content.split()) for msg in request.messages),
+                    "completion_tokens": len(response_content.split()),
+                    "total_tokens": sum(len(msg.content.split()) for msg in request.messages) + len(response_content.split())
+                },
+            }
+    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))
+
+# Re-added endpoints without API key authentication
+
+# Endpoint: POST /v1/tokenizer
+class TokenizerRequest(BaseModel):
+    text: str
+
+@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
+            }
+        },
+    )
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)