Update main.py

main.py

@@ -14,6 +14,7 @@ from aiohttp import ClientSession, ClientResponseError
 from fastapi import FastAPI, HTTPException, Request, Depends, Header
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
+from datetime import datetime
 
 # Configure logging
 logging.basicConfig(
@@ -38,8 +39,8 @@ rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})
 CLEANUP_INTERVAL = 60  # seconds
 RATE_LIMIT_WINDOW = 60  # seconds
 
-# Define ImageResponse for image models
-class ImageResponseModel(BaseModel):
+# Define ImageResponse using Pydantic
+class ImageResponse(BaseModel):
     images: str
     alt: str
 
@@ -302,8 +303,198 @@ class Blackbox:
             except Exception as e:
                 return f"Unexpected error during /api/chat request: {str(e)}"
 
-    # If needed, you can integrate create_async_generator here
-    # ...
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: List[Dict[str, str]],
+        proxy: Optional[str] = None,
+        websearch: bool = False,
+        **kwargs
+    ) -> AsyncGenerator[Union[str, ImageResponse], None]:
+        """
+        Creates an asynchronous generator for streaming responses from Blackbox AI.
+
+        Parameters:
+            model (str): Model to use for generating responses.
+            messages (List[Dict[str, str]]): Message history.
+            proxy (Optional[str]): Proxy URL, if needed.
+            websearch (bool): Enables or disables web search mode.
+            **kwargs: Additional keyword arguments.
+
+        Yields:
+            Union[str, ImageResponse]: Segments of the generated response or ImageResponse objects.
+        """
+        model = cls.get_model(model)
+
+        chat_id = cls.generate_random_string()
+        next_action = cls.generate_next_action()
+        next_router_state_tree = cls.generate_next_router_state_tree()
+
+        agent_mode = cls.agentMode.get(model, {})
+        trending_agent_mode = cls.trendingAgentMode.get(model, {})
+
+        prefix = cls.model_prefixes.get(model, "")
+        
+        formatted_prompt = ""
+        for message in messages:
+            role = message.get('role', '').capitalize()
+            content = message.get('content', '')
+            if role and content:
+                formatted_prompt += f"{role}: {content}\n"
+        
+        if prefix:
+            formatted_prompt = f"{prefix} {formatted_prompt}".strip()
+
+        referer_path = cls.model_referers.get(model, f"/?model={model}")
+        referer_url = f"{cls.url}{referer_path}"
+
+        common_headers = {
+            'accept': '*/*',
+            'accept-language': 'en-US,en;q=0.9',
+            'cache-control': 'no-cache',
+            'origin': cls.url,
+            'pragma': 'no-cache',
+            'priority': 'u=1, i',
+            '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'
+        }
+
+        headers_api_chat = {
+            'Content-Type': 'application/json',
+            'Referer': referer_url
+        }
+        headers_api_chat_combined = {**common_headers, **headers_api_chat}
+
+        payload_api_chat = {
+            "messages": [
+                {
+                    "id": chat_id,
+                    "content": formatted_prompt,
+                    "role": "user"
+                }
+            ],
+            "id": chat_id,
+            "previewToken": None,
+            "userId": None,
+            "codeModelMode": True,
+            "agentMode": agent_mode,
+            "trendingAgentMode": trending_agent_mode,
+            "isMicMode": False,
+            "userSystemPrompt": None,
+            "maxTokens": 1024,
+            "playgroundTopP": 0.9,
+            "playgroundTemperature": 0.5,
+            "isChromeExt": False,
+            "githubToken": None,
+            "clickedAnswer2": False,
+            "clickedAnswer3": False,
+            "clickedForceWebSearch": False,
+            "visitFromDelta": False,
+            "mobileClient": False,
+            "webSearchMode": websearch,
+            "userSelectedModel": cls.userSelectedModel.get(model, model)
+        }
+
+        headers_chat = {
+            'Accept': 'text/x-component',
+            'Content-Type': 'text/plain;charset=UTF-8',
+            'Referer': f'{cls.url}/chat/{chat_id}?model={model}',
+            'next-action': next_action,
+            'next-router-state-tree': next_router_state_tree,
+            'next-url': '/'
+        }
+        headers_chat_combined = {**common_headers, **headers_chat}
+
+        data_chat = '[]'
+
+        async with ClientSession(headers=common_headers) as session:
+            try:
+                async with session.post(
+                    cls.api_endpoint,
+                    headers=headers_api_chat_combined,
+                    json=payload_api_chat,
+                    proxy=proxy
+                ) as response_api_chat:
+                    response_api_chat.raise_for_status()
+                    text = await response_api_chat.text()
+                    cleaned_response = cls.clean_response(text)
+
+                    if model in cls.image_models:
+                        match = re.search(r'!\[.*?\]\((https?://[^\)]+)\)', cleaned_response)
+                        if match:
+                            image_url = match.group(1)
+                            image_response = ImageResponse(images=image_url, alt="Generated Image")
+                            yield image_response
+                        else:
+                            yield cleaned_response
+                    else:
+                        if websearch:
+                            match = re.search(r'\$~~~\$(.*?)\$~~~\$', cleaned_response, re.DOTALL)
+                            if match:
+                                source_part = match.group(1).strip()
+                                answer_part = cleaned_response[match.end():].strip()
+                                try:
+                                    sources = json.loads(source_part)
+                                    source_formatted = "**Source:**\n"
+                                    for item in sources:
+                                        title = item.get('title', 'No Title')
+                                        link = item.get('link', '#')
+                                        position = item.get('position', '')
+                                        source_formatted += f"{position}. [{title}]({link})\n"
+                                    final_response = f"{answer_part}\n\n{source_formatted}"
+                                except json.JSONDecodeError:
+                                    final_response = f"{answer_part}\n\nSource information is unavailable."
+                            else:
+                                final_response = cleaned_response
+                        else:
+                            if '$~~~$' in cleaned_response:
+                                final_response = cleaned_response.split('$~~~$')[0].strip()
+                            else:
+                                final_response = cleaned_response
+
+                        yield final_response
+            except ClientResponseError as e:
+                error_text = f"Error {e.status}: {e.message}"
+                try:
+                    error_response = await e.response.text()
+                    cleaned_error = cls.clean_response(error_response)
+                    error_text += f" - {cleaned_error}"
+                except Exception:
+                    pass
+                yield error_text
+            except Exception as e:
+                yield f"Unexpected error during /api/chat request: {str(e)}"
+
+            chat_url = f'{cls.url}/chat/{chat_id}?model={model}'
+
+            try:
+                async with session.post(
+                    chat_url,
+                    headers=headers_chat_combined,
+                    data=data_chat,
+                    proxy=proxy
+                ) as response_chat:
+                    response_chat.raise_for_status()
+                    pass
+            except ClientResponseError as e:
+                error_text = f"Error {e.status}: {e.message}"
+                try:
+                    error_response = await e.response.text()
+                    cleaned_error = cls.clean_response(error_response)
+                    error_text += f" - {cleaned_error}"
+                except Exception:
+                    pass
+                yield error_text
+            except Exception as e:
+                yield f"Unexpected error during /chat/{chat_id} request: {str(e)}"
 
 # Custom exception for model not working
 class ModelNotWorkingException(Exception):
@@ -488,6 +679,49 @@ async def http_exception_handler(request: Request, exc: HTTPException):
         },
     )
 
+# Optional: Additional Endpoint for Streaming Responses (Using create_async_generator)
+# This endpoint leverages the new create_async_generator method for streaming responses.
+# Note: Streaming responses may require clients that support Server-Sent Events (SSE) or WebSockets.
+
+@app.post("/v1/chat/completions/stream", dependencies=[Depends(rate_limiter_per_ip)])
+async def chat_completions_stream(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 streaming 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.")
+
+        # Create an asynchronous generator for the response
+        async_generator = Blackbox.create_async_generator(
+            model=request.model,
+            messages=[{"role": msg.role, "content": msg.content} for msg in request.messages],
+            temperature=request.temperature,
+            max_tokens=request.max_tokens
+        )
+
+        logger.info(f"Started streaming response for API key: {api_key} | IP: {client_ip}")
+        return JSONResponse(
+            status_code=200,
+            content={"stream": True, "message": "Streaming started."}
+        )
+        # Note: Implement proper streaming responses using StreamingResponse or WebSockets if needed.
+
+    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 streaming chat completions request from IP: {client_ip}.")
+        raise HTTPException(status_code=500, detail=str(e))
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)