Update main.py

main.py

@@ -386,3 +386,371 @@ class Blackbox:
                 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")
+            streaming_response = await 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")
+            streaming_response = await 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)