api/utils.py

from datetime import datetime
import json
import uuid
import asyncio
import random
import string
from typing import Any, Dict, Optional

import httpx
from fastapi import HTTPException
from api.config import (
    MODEL_MAPPING,
    get_headers_api_chat,
    get_headers_chat,
    BASE_URL,
    AGENT_MODE,
    TRENDING_AGENT_MODE,
    MODEL_PREFIXES,
    MODEL_REFERERS
)
from api.models import ChatRequest
from api.logger import setup_logger
from api.validate import getHid  # Import the asynchronous getHid function
import tiktoken

logger = setup_logger(__name__)

# Define the blocked message
BLOCKED_MESSAGE = "Generated by BLACKBOX.AI, try unlimited chat https://www.blackbox.ai and for API requests replace https://www.blackbox.ai with https://api.blackbox.ai"

# Function to calculate tokens using tiktoken
def calculate_tokens(text: str, model: str) -> int:
    try:
        encoding = tiktoken.encoding_for_model(model)
        tokens = encoding.encode(text)
        return len(tokens)
    except KeyError:
        # Handle the case where the model is not supported by tiktoken
        logger.warning(f"Model '{model}' not supported by tiktoken for token counting. Using a generic method.")
        return len(text.split())

# Helper function to create chat completion data
def create_chat_completion_data(
    content: str, model: str, timestamp: int, request_id: str, prompt_tokens: int = 0, completion_tokens: int = 0, finish_reason: Optional[str] = None
) -> Dict[str, Any]:
    if finish_reason == "stop":
        usage = {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "total_tokens": prompt_tokens + completion_tokens,
        }
    else:
        usage = None
    return {
        "id": request_id,
        "object": "chat.completion.chunk",
        "created": timestamp,
        "model": model,
        "choices": [
            {
                "index": 0,
                "delta": {"content": content, "role": "assistant"},
                "finish_reason": finish_reason,
            }
        ],
        "usage": usage,
    }

# Function to convert message to dictionary format, ensuring base64 data and optional model prefix
def message_to_dict(message, model_prefix: Optional[str] = None):
    content = message.content if isinstance(message.content, str) else message.content[0]["text"]
    if model_prefix:
        content = f"{model_prefix} {content}"
    if isinstance(message.content, list) and len(message.content) == 2 and "image_url" in message.content[1]:
        # Ensure base64 images are always included for all models
        image_base64 = message.content[1]["image_url"]["url"]
        return {
            "role": message.role,
            "content": content,
            "data": {
                "imageBase64": image_base64,
                "fileText": "",
                "title": "snapshot",
                # Added imagesData field here
                "imagesData": [
                    {
                        "filePath": f"MultipleFiles/{uuid.uuid4().hex}.jpg",
                        "contents": image_base64
                    }
                ],
            },
        }
    return {"role": message.role, "content": content}

# Function to strip model prefix from content if present
def strip_model_prefix(content: str, model_prefix: Optional[str] = None) -> str:
    """Remove the model prefix from the response content if present."""
    if model_prefix and content.startswith(model_prefix):
        logger.debug(f"Stripping prefix '{model_prefix}' from content.")
        return content[len(model_prefix):].strip()
    return content

# Process streaming response with headers from config.py
async def process_streaming_response(request: ChatRequest):
    # Generate a unique ID for this request
    request_id = f"chatcmpl-{uuid.uuid4()}"
    logger.info(f"Processing request with ID: {request_id} - Model: {request.model}")

    # Get the appropriate configuration for the requested model
    agent_mode = AGENT_MODE.get(request.model, {})
    trending_agent_mode = TRENDING_AGENT_MODE.get(request.model, {})
    model_prefix = MODEL_PREFIXES.get(request.model, "")

    # Adjust headers_api_chat since referer_url is removed
    headers_api_chat = get_headers_api_chat(BASE_URL)

    if request.model == 'o1-preview':
        delay_seconds = random.randint(1, 60)
        logger.info(
            f"Introducing a delay of {delay_seconds} seconds for model 'o1-preview' "
            f"(Request ID: {request_id})"
        )
        await asyncio.sleep(delay_seconds)

    # Fetch the h-value for the 'validated' field
    h_value = await getHid()
    if not h_value:
        logger.error("Failed to retrieve h-value for validation.")
        raise HTTPException(
            status_code=500, detail="Validation failed due to missing h-value."
        )

    messages = [
        message_to_dict(msg, model_prefix=model_prefix) for msg in request.messages
    ]

    json_data = {
        "agentMode": agent_mode,
        "clickedAnswer2": False,
        "clickedAnswer3": False,
        "clickedForceWebSearch": False,
        "codeModelMode": True,
        "githubToken": None,
        "id": request_id,
        "isChromeExt": False,
        "isMicMode": False,
        "maxTokens": request.max_tokens,
        "messages": messages,
        "mobileClient": False,
        "playgroundTemperature": request.temperature,
        "playgroundTopP": request.top_p,
        "previewToken": None,
        "trendingAgentMode": trending_agent_mode,
        "userId": None,
        "userSelectedModel": MODEL_MAPPING.get(request.model, request.model),
        "userSystemPrompt": None,
        "validated": h_value,  # Dynamically set the validated field
        "visitFromDelta": False,
        "webSearchModePrompt": False,
        "imageGenerationMode": False,  # Added this line
    }

    prompt_tokens = 0
    for message in messages:
        if 'content' in message:
            prompt_tokens += calculate_tokens(message['content'], request.model)
        if 'data' in message and 'imageBase64' in message['data']:
            prompt_tokens += calculate_tokens(message['data']['imageBase64'], request.model)

    completion_tokens = 0
    async with httpx.AsyncClient() as client:
        try:
            async with client.stream(
                "POST",
                f"{BASE_URL}/api/chat",
                headers=headers_api_chat,
                json=json_data,
                timeout=100,
            ) as response:
                response.raise_for_status()
                async for chunk in response.aiter_text():
                    timestamp = int(datetime.now().timestamp())
                    if chunk:
                        content = chunk
                        if content.startswith("$@$v=undefined-rv1$@$"):
                            content = content[21:]
                        # Remove the blocked message if present
                        if BLOCKED_MESSAGE in content:
                            logger.info(
                                f"Blocked message detected in response for Request ID {request_id}."
                            )
                            content = content.replace(BLOCKED_MESSAGE, '').strip()
                            if not content:
                                continue  # Skip if content is empty after removal
                        cleaned_content = strip_model_prefix(content, model_prefix)
                        completion_tokens += calculate_tokens(cleaned_content, request.model)
                        yield f"data: {json.dumps(create_chat_completion_data(cleaned_content, request.model, timestamp, request_id))}\n\n"

                yield f"data: {json.dumps(create_chat_completion_data('', request.model, timestamp, request_id, prompt_tokens, completion_tokens, 'stop'))}\n\n"
                yield "data: [DONE]\n\n"
        except httpx.HTTPStatusError as e:
            logger.error(f"HTTP error occurred for Request ID {request_id}: {e}")
            error_message = f"HTTP error occurred: {e}"
            try:
                error_details = e.response.json()
                error_message += f" Details: {error_details}"
            except ValueError:
                error_message += f" Response body: {e.response.text}"
            
            yield f"data: {json.dumps(create_chat_completion_data(error_message, request.model, timestamp, request_id, prompt_tokens, completion_tokens, 'error'))}\n\n"
            yield "data: [DONE]\n\n"
            # raise HTTPException(status_code=e.response.status_code, detail=error_message)
        except httpx.RequestError as e:
            logger.error(
                f"Error occurred during request for Request ID {request_id}: {e}"
            )
            error_message = f"Request error occurred: {e}"
            yield f"data: {json.dumps(create_chat_completion_data(error_message, request.model, timestamp, request_id, prompt_tokens, completion_tokens, 'error'))}\n\n"
            yield "data: [DONE]\n\n"
            # raise HTTPException(status_code=500, detail=error_message)
        except Exception as e:
            logger.error(f"An unexpected error occurred for Request ID {request_id}: {e}")
            error_message = f"An unexpected error occurred: {e}"
            yield f"data: {json.dumps(create_chat_completion_data(error_message, request.model, timestamp, request_id, prompt_tokens, completion_tokens, 'error'))}\n\n"
            yield "data: [DONE]\n\n"
            # raise HTTPException(status_code=500, detail=error_message)

# Process non-streaming response with headers from config.py
async def process_non_streaming_response(request: ChatRequest):
    # Generate a unique ID for this request
    request_id = f"chatcmpl-{uuid.uuid4()}"
    logger.info(f"Processing request with ID: {request_id} - Model: {request.model}")

    # Get the appropriate configuration for the requested model
    agent_mode = AGENT_MODE.get(request.model, {})
    trending_agent_mode = TRENDING_AGENT_MODE.get(request.model, {})
    model_prefix = MODEL_PREFIXES.get(request.model, "")

    # Adjust headers_api_chat and headers_chat since referer_url is removed
    headers_api_chat = get_headers_api_chat(BASE_URL)
    headers_chat = get_headers_chat(
        BASE_URL,
        next_action=str(uuid.uuid4()),
        next_router_state_tree=json.dumps([""]),
    )

    if request.model == 'o1-preview':
        delay_seconds = random.randint(20, 60)
        logger.info(
            f"Introducing a delay of {delay_seconds} seconds for model 'o1-preview' "
            f"(Request ID: {request_id})"
        )
        await asyncio.sleep(delay_seconds)

    # Fetch the h-value for the 'validated' field
    h_value = "00f37b34-a166-4efb-bce5-1312d87f2f94"
    if not h_value:
        logger.error("Failed to retrieve h-value for validation.")
        raise HTTPException(
            status_code=500, detail="Validation failed due to missing h-value."
        )

    messages = [
        message_to_dict(msg, model_prefix=model_prefix) for msg in request.messages
    ]

    json_data = {
        "agentMode": agent_mode,
        "clickedAnswer2": False,
        "clickedAnswer3": False,
        "clickedForceWebSearch": False,
        "codeModelMode": True,
        "githubToken": None,
        "id": request_id,
        "isChromeExt": False,
        "isMicMode": False,
        "maxTokens": request.max_tokens,
        "messages": messages,
        "mobileClient": False,
        "playgroundTemperature": request.temperature,
        "playgroundTopP": request.top_p,
        "previewToken": None,
        "trendingAgentMode": trending_agent_mode,
        "userId": None,
        "userSelectedModel": MODEL_MAPPING.get(request.model, request.model),
        "userSystemPrompt": None,
        "validated": h_value,  # Dynamically set the validated field
        "visitFromDelta": False,
        "webSearchModePrompt": False,
        "imageGenerationMode": False,  # Added this line
    }

    prompt_tokens = 0
    for message in messages:
        if 'content' in message:
            prompt_tokens += calculate_tokens(message['content'], request.model)
        if 'data' in message and 'imageBase64' in message['data']:
            prompt_tokens += calculate_tokens(message['data']['imageBase64'], request.model)

    full_response = ""
    async with httpx.AsyncClient() as client:
        try:
            async with client.stream(
                method="POST",
                url=f"{BASE_URL}/api/chat",
                headers=headers_api_chat,
                json=json_data,
            ) as response:
                response.raise_for_status()
                async for chunk in response.aiter_text():
                    full_response += chunk
        except httpx.HTTPStatusError as e:
            logger.error(f"HTTP error occurred for Request ID {request_id}: {e}")
            error_message = f"HTTP error occurred: {e}"
            try:
                error_details = e.response.json()
                error_message += f" Details: {error_details}"
            except ValueError:
                error_message += f" Response body: {e.response.text}"
            
            return {
                "id": request_id,
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": request.model,
                "choices": [
                    {
                        "index": 0,
                        "message": {"role": "assistant", "content": error_message},
                        "finish_reason": "error",
                    }
                ],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": 0,
                    "total_tokens": prompt_tokens,
                },
            }
        except httpx.RequestError as e:
            logger.error(
                f"Error occurred during request for Request ID {request_id}: {e}"
            )
            error_message = f"Request error occurred: {e}"
            return {
                "id": request_id,
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": request.model,
                "choices": [
                    {
                        "index": 0,
                        "message": {"role": "assistant", "content": error_message},
                        "finish_reason": "error",
                    }
                ],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": 0,
                    "total_tokens": prompt_tokens,
                },
            }
        except Exception as e:
            logger.error(f"An unexpected error occurred for Request ID {request_id}: {e}")
            error_message = f"An unexpected error occurred: {e}"
            return {
                "id": request_id,
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": request.model,
                "choices": [
                    {
                        "index": 0,
                        "message": {"role": "assistant", "content": error_message},
                        "finish_reason": "error",
                    }
                ],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": 0,
                    "total_tokens": prompt_tokens,
                },
            }

    if full_response.startswith("$@$v=undefined-rv1$@$"):
        full_response = full_response[21:]

    # Remove the blocked message if present
    if BLOCKED_MESSAGE in full_response:
        logger.info(
            f"Blocked message detected in response for Request ID {request_id}."
        )
        full_response = full_response.replace(BLOCKED_MESSAGE, '').strip()
        if not full_response:
            raise HTTPException(
                status_code=500, detail="Blocked message detected in response."
            )

    cleaned_full_response = strip_model_prefix(full_response, model_prefix)
    completion_tokens = calculate_tokens(cleaned_full_response, request.model)

    return {
        "id": request_id,
        "object": "chat.completion",
        "created": int(datetime.now().timestamp()),
        "model": request.model,
        "choices": [
            {
                "index": 0,
                "message": {"role": "assistant", "content": cleaned_full_response},
                "finish_reason": "stop",
            }
        ],
        "usage": {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "total_tokens": prompt_tokens + completion_tokens,
        },
    }