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import os
import json
import uuid
import time
import base64
import sys
import inspect
import secrets
import asyncio
import contextvars
import functools
import re
import threading
from dataclasses import dataclass, field
from typing import List, Optional, Any, Dict, Union, Tuple, Iterator
from pathlib import Path
from contextlib import contextmanager
from concurrent.futures import ThreadPoolExecutor
from enum import Enum
import requests
from flask import (
Flask,
request,
Response,
jsonify,
stream_with_context,
render_template,
redirect,
session,
)
from curl_cffi import requests as curl_requests
from werkzeug.middleware.proxy_fix import ProxyFix
from patchright.async_api import async_playwright, Browser, BrowserContext
class PlaywrightStatsigManager:
"""
x-statsig-id capture using Playwright (adapted from Grok3API driver.py)
This approach captures authentic x-statsig-id headers by:
1. Patching window.fetch to intercept grok.com's own API calls
2. Triggering a real request on grok.com to generate authentic headers
3. Capturing and storing the real x-statsig-id for reuse
"""
def __init__(self, proxy_url: Optional[str] = None):
self._cached_statsig_id: Optional[str] = None
self._cache_timestamp: Optional[int] = None
self._cache_duration = 300
self._context: Optional[BrowserContext] = None
self._playwright = None
self._lock = threading.Lock()
self._base_url = "https://grok.com/"
self._proxy_url = proxy_url
def _run_async(self, coro):
"""Run async function in thread-safe manner"""
try:
loop = asyncio.get_event_loop()
if loop.is_running():
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(asyncio.run, coro)
return future.result()
else:
return loop.run_until_complete(coro)
except RuntimeError:
return asyncio.run(coro)
async def _ensure_browser(self):
"""Ensure browser is available and ready"""
if not self._context:
self._playwright = await async_playwright().start()
context_options = {
"viewport": {"width": 1920, "height": 1080},
"user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36",
}
if self._proxy_url:
context_options["proxy"] = {"server": self._proxy_url}
self._context = await self._playwright.chromium.launch_persistent_context(
user_data_dir="./data/chrome",
headless=True,
no_viewport=True,
channel="chrome",
args=[
"--no-first-run",
"--force-color-profile=srgb",
"--metrics-recording-only",
"--password-store=basic",
"--no-default-browser-check",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
"--disable-web-security",
"--disable-features=VizDisplayCompositor",
"--user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36",
],
**context_options,
)
async def check_real_ip(self) -> str:
"""Check the real IP address using Playwright browser"""
try:
await self._ensure_browser()
page = await self._context.new_page() # type: ignore
try:
print("Checking real IP address via ipify API")
await page.goto("https://api.ipify.org?format=json", timeout=30000)
content = await page.content()
ip_info = await page.evaluate(
"""
() => {
try {
const bodyText = document.body.textContent || document.body.innerText;
return JSON.parse(bodyText);
} catch (e) {
return null;
}
}
"""
)
if ip_info and ip_info.get("ip"):
ip_address = ip_info["ip"]
print(f"Playwright real IP address: {ip_address}")
return ip_address
else:
print("Failed to parse IP from ipify response")
return "unknown"
except Exception as e:
print(f"Error checking IP address: {e}")
return "error"
finally:
await page.close()
except Exception as e:
print(f"Failed to check real IP address: {e}")
return "failed"
async def _cleanup(self):
"""Clean up browser resources"""
if self._context:
await self._context.close()
self._context = None
if self._playwright:
await self._playwright.stop()
self._playwright = None
def cleanup(self):
"""Synchronous cleanup wrapper"""
if self._context:
self._run_async(self._cleanup())
async def _patch_fetch_for_statsig(self, page):
"""Patch window.fetch to intercept x-statsig-id headers (adapted from driver.py)"""
result = await page.evaluate(
"""
(() => {
if (window.__fetchPatched) {
return "fetch already patched";
}
window.__fetchPatched = false;
const originalFetch = window.fetch;
window.__xStatsigId = null;
window.fetch = async function(...args) {
console.log("Intercepted fetch call with args:", args);
const response = await originalFetch.apply(this, args);
try {
const req = args[0];
const opts = args[1] || {};
const url = typeof req === 'string' ? req : req.url;
const headers = opts.headers || {};
const targetUrl = "https://grok.com/rest/app-chat/conversations/new";
if (url === targetUrl) {
let id = null;
if (headers["x-statsig-id"]) {
id = headers["x-statsig-id"];
} else if (typeof opts.headers?.get === "function") {
id = opts.headers.get("x-statsig-id");
}
if (id) {
window.__xStatsigId = id;
console.log("Captured x-statsig-id:", id);
} else {
console.warn("x-statsig-id not found in headers");
}
} else {
console.log("Skipped fetch, URL doesn't match target:", url);
}
} catch (e) {
console.warn("Error capturing x-statsig-id:", e);
}
return response;
};
window.__fetchPatched = true;
return "fetch successfully patched";
})()
"""
)
print(f"Fetch patching result: {result}")
async def _initiate_answer(self, page):
"""Trigger a real request to grok.com to capture x-statsig-id"""
try:
await page.wait_for_selector("div.relative.z-10 textarea", timeout=2000)
import random
import string
random_char = random.choice(string.ascii_lowercase)
await page.fill("div.relative.z-10 textarea", random_char)
await page.press("div.relative.z-10 textarea", "Enter")
print(f"Triggered request with character: {random_char}")
except Exception as e:
print(f"Error triggering answer: {e}")
title = await page.title()
url = page.url
print(f"Page title: {title}, URL: {url}")
raise
async def _capture_statsig_id_async(
self, restart_session: bool = False
) -> Optional[str]:
"""Capture x-statsig-id from real grok.com interaction"""
try:
await self._ensure_browser()
page = await self._context.new_page() # type: ignore
try:
print("Navigating to grok.com")
await page.goto(
self._base_url, wait_until="domcontentloaded", timeout=30000
)
await self._patch_fetch_for_statsig(page)
captcha_visible = await page.evaluate(
"""
(() => {
const elements = document.querySelectorAll("p");
for (const el of elements) {
if (el.textContent.includes("Making sure you're human")) {
const style = window.getComputedStyle(el);
if (style.visibility !== 'hidden' && style.display !== 'none') {
return true;
}
}
}
return false;
})()
"""
)
if captcha_visible:
print("Captcha detected, cannot capture x-statsig-id")
return None
await self._initiate_answer(page)
try:
await page.locator("div.message-bubble p[dir='auto']").or_(
page.locator("div.w-full.max-w-\\[48rem\\]")
).or_(
page.locator("p", has_text="Making sure you're human")
).wait_for(
timeout=20000
)
except:
print("No response elements found within timeout")
error_elements = await page.query_selector_all(
"div.w-full.max-w-\\[48rem\\]"
)
if error_elements:
print("Authentication error detected")
return None
captcha_elements = await page.query_selector_all(
"p:has-text('Making sure you\\'re human')"
)
if captcha_elements:
print("Captcha appeared during request")
return None
statsig_id = await page.evaluate("window.__xStatsigId")
if statsig_id:
print(f"Successfully captured x-statsig-id: {statsig_id[:30]}...")
return statsig_id
else:
print("No x-statsig-id was captured")
return None
finally:
await page.close()
except Exception as e:
print(f"Error capturing x-statsig-id: {e}")
return None
def capture_statsig_id(self, restart_session: bool = False) -> Optional[str]:
"""Capture x-statsig-id (sync wrapper)"""
with self._lock:
return self._run_async(self._capture_statsig_id_async(restart_session))
def check_real_ip_sync(self) -> str:
"""Check real IP address (sync wrapper)"""
with self._lock:
return self._run_async(self.check_real_ip())
def generate_xai_request_id(self) -> str:
"""Generate x-xai-request-id (simple UUID)"""
return str(uuid.uuid4())
def get_dynamic_headers(
self, method: str = "POST", pathname: str = "/rest/app-chat/conversations/new"
) -> Dict[str, str]:
"""Get dynamic headers including captured x-statsig-id and x-xai-request-id"""
headers = {}
current_time = int(time.time())
if (
self._cached_statsig_id
and self._cache_timestamp
and (current_time - self._cache_timestamp) < self._cache_duration
):
print("Using cached x-statsig-id")
headers["x-statsig-id"] = self._cached_statsig_id
else:
print("Capturing fresh x-statsig-id")
statsig_id = self.capture_statsig_id()
if statsig_id:
self._cached_statsig_id = statsig_id
self._cache_timestamp = current_time
headers["x-statsig-id"] = statsig_id
else:
print("Failed to capture x-statsig-id, using fallback")
headers["x-statsig-id"] = (
"ZTpUeXBlRXJyb3I6IENhbm5vdCByZWFkIHByb3BlcnRpZXMgb2YgdW5kZWZpbmVkIChyZWFkaW5nICdjaGlsZE5vZGVzJyk="
)
headers["x-xai-request-id"] = self.generate_xai_request_id()
print(f"Generated dynamic headers: {list(headers.keys())}")
return headers
_global_statsig_manager: Optional[PlaywrightStatsigManager] = None
def initialize_statsig_manager(proxy_url: Optional[str] = None) -> None:
"""Initialize the global StatsigManager instance with configuration"""
global _global_statsig_manager
if _global_statsig_manager is None:
_global_statsig_manager = PlaywrightStatsigManager(proxy_url=proxy_url)
def get_statsig_manager() -> PlaywrightStatsigManager:
"""Get or create the global StatsigManager instance"""
global _global_statsig_manager
if _global_statsig_manager is None:
_global_statsig_manager = PlaywrightStatsigManager()
return _global_statsig_manager
class ModelType(Enum):
"""Supported Grok model types."""
GROK_3 = "grok-3"
GROK_3_SEARCH = "grok-3-search"
GROK_3_IMAGEGEN = "grok-3-imageGen"
GROK_3_DEEPSEARCH = "grok-3-deepsearch"
GROK_3_DEEPERSEARCH = "grok-3-deepersearch"
GROK_3_REASONING = "grok-3-reasoning"
GROK_4 = "grok-4"
GROK_4_REASONING = "grok-4-reasoning"
GROK_4_IMAGEGEN = "grok-4-imageGen"
GROK_4_DEEPSEARCH = "grok-4-deepsearch"
class TokenType(Enum):
"""Token privilege levels."""
NORMAL = "normal"
SUPER = "super"
class ResponseState(Enum):
"""Response processing states."""
IDLE = "idle"
THINKING = "thinking"
GENERATING_IMAGE = "generating_image"
COMPLETE = "complete"
MESSAGE_LENGTH_LIMIT = 40000
MAX_FILE_ATTACHMENTS = 4
DEFAULT_REQUEST_TIMEOUT = 120000
MAX_RETRY_ATTEMPTS = 3
BASE_RETRY_DELAY = 1.0
BASE_HEADERS = {
"Accept": "*/*",
"Accept-Language": "zh-CN,zh;q=0.9",
"Accept-Encoding": "gzip, deflate, br, zstd",
"Content-Type": "text/plain;charset=UTF-8",
"Connection": "keep-alive",
"Origin": "https://grok.com",
"Priority": "u=1, i",
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36",
"Sec-Ch-Ua": '"Not(A:Brand";v="99", "Google Chrome";v="133", "Chromium";v="133"',
"Sec-Ch-Ua-Mobile": "?0",
"Sec-Ch-Ua-Platform": '"macOS"',
"Sec-Fetch-Dest": "empty",
"Sec-Fetch-Mode": "cors",
"Sec-Fetch-Site": "same-origin",
"Baggage": "sentry-public_key=b311e0f2690c81f25e2c4cf6d4f7ce1c",
}
def get_dynamic_headers(
method: str = "POST", pathname: str = "/rest/app-chat/conversations/new"
) -> Dict[str, str]:
"""
Get headers with dynamic x-statsig-id and x-xai-request-id
Args:
method: HTTP method for the request
pathname: Request pathname for statsig generation
Returns:
Dictionary with all headers including dynamic ones
"""
try:
headers = BASE_HEADERS.copy()
statsig_manager = get_statsig_manager()
dynamic_headers = statsig_manager.get_dynamic_headers(method, pathname)
headers.update(dynamic_headers)
print(f"Generated dynamic headers for {method} {pathname}")
return headers
except Exception as e:
print(f"Error generating dynamic headers: {e}")
headers = BASE_HEADERS.copy()
headers["x-xai-request-id"] = str(uuid.uuid4())
headers["x-statsig-id"] = (
"ZTpUeXBlRXJyb3I6IENhbm5vdCByZWFkIHByb3BlcnRpZXMgb2YgdW5kZWZpbmVkIChyZWFkaW5nICdjaGlsZE5vZGVzJyk="
)
return headers
class GrokApiException(Exception):
"""Base exception for Grok API errors."""
def __init__(self, message: str, error_code: str = "UNKNOWN_ERROR"):
super().__init__(message)
self.error_code = error_code
class TokenException(GrokApiException):
"""Token-related exceptions."""
pass
class ValidationException(GrokApiException):
"""Input validation exceptions."""
pass
class RateLimitException(GrokApiException):
"""Rate limiting exceptions."""
pass
@dataclass
class TokenCredential:
"""Represents a token credential with validation."""
sso_token: str
token_type: TokenType
def __post_init__(self):
"""Validate token format."""
if not self.sso_token or not self.sso_token.strip():
raise ValidationException("SSO token cannot be empty")
if "sso=" not in self.sso_token:
raise ValidationException("Invalid SSO token format")
@classmethod
def from_raw_token(
cls, raw_token: str, token_type: TokenType = TokenType.NORMAL
) -> "TokenCredential":
"""Create TokenCredential from raw SSO value."""
if not raw_token or not raw_token.strip():
raise ValidationException("Raw token cannot be empty")
formatted_token = f"sso-rw={raw_token.strip()};sso={raw_token.strip()}"
return cls(formatted_token, token_type)
def extract_sso_value(self) -> str:
"""Extract the SSO value from the token."""
try:
return self.sso_token.split("sso=")[1].split(";")[0]
except (IndexError, AttributeError) as e:
raise TokenException(f"Failed to parse SSO token: {self.sso_token}") from e
@dataclass
class GeneratedImage:
"""Represents a generated image with metadata."""
url: str
base_url: str = "https://assets.grok.com"
cookies: List[Dict[str, Any]] = field(default_factory=list)
def __post_init__(self):
"""Validate image data."""
if not self.url:
raise ValidationException("Image URL cannot be empty")
@dataclass
class ProcessingState:
"""Immutable state for response processing."""
is_thinking: bool = False
is_generating_image: bool = False
image_generation_phase: int = 0
def with_thinking(self, thinking: bool) -> "ProcessingState":
"""Return new state with updated thinking status."""
return ProcessingState(
thinking, self.is_generating_image, self.image_generation_phase
)
def with_image_generation(
self, generating: bool, phase: int = 0
) -> "ProcessingState":
"""Return new state with updated image generation status."""
return ProcessingState(self.is_thinking, generating, phase)
@dataclass
class ModelResponse:
"""Enhanced model response with proper validation and transformation."""
response_id: str
message: str
sender: str
create_time: str
parent_response_id: str
manual: bool
partial: bool
shared: bool
query: str
query_type: str
web_search_results: List[Any] = field(default_factory=list)
xpost_ids: List[Any] = field(default_factory=list)
xposts: List[Any] = field(default_factory=list)
generated_images: List[GeneratedImage] = field(default_factory=list)
image_attachments: List[Any] = field(default_factory=list)
file_attachments: List[Any] = field(default_factory=list)
card_attachments_json: List[Any] = field(default_factory=list)
file_uris: List[Any] = field(default_factory=list)
file_attachments_metadata: List[Any] = field(default_factory=list)
is_control: bool = False
steps: List[Any] = field(default_factory=list)
media_types: List[Any] = field(default_factory=list)
@classmethod
def from_api_response(
cls, data: Dict[str, Any], enable_artifact_files: bool = False
) -> "ModelResponse":
"""Create ModelResponse from API response data with validation."""
try:
response_id = str(data.get("responseId", ""))
sender = str(data.get("sender", ""))
create_time = str(data.get("createTime", ""))
parent_response_id = str(data.get("parentResponseId", ""))
query = str(data.get("query", ""))
query_type = str(data.get("queryType", ""))
manual = bool(data.get("manual", False))
partial = bool(data.get("partial", False))
shared = bool(data.get("shared", False))
is_control = bool(data.get("isControl", False))
raw_message = data.get("message", "")
processed_message = cls._transform_xai_artifacts(str(raw_message))
generated_images = []
for image_url in data.get("generatedImageUrls", []):
if image_url:
generated_images.append(GeneratedImage(url=str(image_url)))
return cls(
response_id=response_id,
message=processed_message,
sender=sender,
create_time=create_time,
parent_response_id=parent_response_id,
manual=manual,
partial=partial,
shared=shared,
query=query,
query_type=query_type,
web_search_results=data.get("webSearchResults", []),
xpost_ids=data.get("xpostIds", []),
xposts=data.get("xposts", []),
generated_images=generated_images,
image_attachments=data.get("imageAttachments", []),
file_attachments=data.get("fileAttachments", []),
card_attachments_json=data.get("cardAttachmentsJson", []),
file_uris=data.get("fileUris", []),
file_attachments_metadata=data.get("fileAttachmentsMetadata", []),
is_control=is_control,
steps=data.get("steps", []),
media_types=data.get("mediaTypes", []),
)
except Exception as e:
print(f"Failed to create ModelResponse: {e}")
return cls(
response_id="",
message="Error processing response",
sender="system",
create_time=str(int(time.time())),
parent_response_id="",
manual=False,
partial=False,
shared=False,
query="",
query_type="",
)
@staticmethod
def _transform_xai_artifacts(text: str) -> str:
"""
Transform xaiArtifact blocks to proper markdown code blocks.
Comprehensive version that handles all xaiArtifact formats including:
1. <xaiArtifact contentType="text/..."> blocks → ```<lang>\ncode\n```
2. ```x-<lang>src format → ```<lang>
3. ```x-<lang> format → ```<lang>
4. Any xaiArtifact with artifact_id, title, etc.
5. Self-closing xaiArtifact tags
"""
if not text:
return text
def replace_artifact_with_content(match):
full_match = match.group(0)
content = match.group(1).strip() if match.group(1) else ""
content_type_match = re.search(r'contentType="([^"]+)"', full_match)
if content_type_match:
content_type = content_type_match.group(1).strip()
if "/" in content_type:
lang = content_type.split("/")[-1]
else:
lang = content_type
if content:
return f"```{lang}\n{content}\n```"
else:
return ""
else:
return content
text = re.sub(
r"<xaiArtifact[^>]*?>(.*?)</xaiArtifact>",
replace_artifact_with_content,
text,
flags=re.DOTALL,
)
text = re.sub(r"<xaiArtifact[^>]*?/>", "", text)
text = re.sub(r"<xaiArtifact[^>]*>", "", text)
text = re.sub(r"</xaiArtifact>", "", text)
text = re.sub(
r"```x-([a-zA-Z0-9_+-]+)src\b", lambda m: f"```{m.group(1)}", text
)
text = re.sub(
r"```x-([a-zA-Z0-9_+-]+)\b(?![a-zA-Z0-9_-]*src)",
lambda m: f"```{m.group(1)}",
text,
)
return text
@dataclass
class GrokResponse:
"""Complete Grok API response wrapper."""
model_response: ModelResponse
is_thinking: bool = False
is_soft_stop: bool = False
response_id: str = ""
conversation_id: Optional[str] = None
title: Optional[str] = None
conversation_create_time: Optional[str] = None
conversation_modify_time: Optional[str] = None
temporary: Optional[bool] = None
error: Optional[str] = None
error_code: Optional[Union[int, str]] = None
@classmethod
def from_api_response(
cls, data: Dict[str, Any], enable_artifact_files: bool = False
) -> "GrokResponse":
"""Create GrokResponse from API response data."""
try:
error = data.get("error")
error_code = data.get("error_code")
result = data.get("result", {})
response_data = result.get("response", {})
model_response = ModelResponse.from_api_response(
response_data.get("modelResponse", {}), enable_artifact_files
)
is_thinking = bool(response_data.get("isThinking", False))
is_soft_stop = bool(response_data.get("isSoftStop", False))
response_id = str(response_data.get("responseId", ""))
conversation_id = response_data.get("conversationId")
new_title = result.get("newTitle") or result.get("title")
title = new_title if new_title else None
conversation_create_time = response_data.get("createTime")
conversation_modify_time = response_data.get("modifyTime")
temporary = response_data.get("temporary")
return cls(
model_response=model_response,
is_thinking=is_thinking,
is_soft_stop=is_soft_stop,
response_id=response_id,
conversation_id=conversation_id,
title=title,
conversation_create_time=conversation_create_time,
conversation_modify_time=conversation_modify_time,
temporary=temporary,
error=error,
error_code=error_code,
)
except Exception as e:
error_msg = str(e)
return cls(
model_response=ModelResponse.from_api_response({}),
error=error_msg,
error_code="RESPONSE_PARSING_ERROR",
)
class ConfigurationManager:
"""Centralized configuration management with validation."""
def __init__(self):
"""Initialize configuration with environment variables."""
self.data_dir = Path("/data")
self.data_dir.mkdir(parents=True, exist_ok=True)
self._config = self._load_configuration()
self._validate_configuration()
def _load_configuration(self) -> Dict[str, Any]:
"""Load configuration from environment variables."""
return {
"MODELS": {
model.value: model.value.split("-")[0] + "-" + model.value.split("-")[1]
for model in ModelType
},
"API": {
"IS_TEMP_CONVERSATION": self._get_bool_env(
"IS_TEMP_CONVERSATION", True
),
"IS_CUSTOM_SSO": self._get_bool_env("IS_CUSTOM_SSO", False),
"BASE_URL": "https://grok.com",
"API_KEY": os.environ.get("API_KEY", "sk-123456"),
"PICGO_KEY": os.environ.get("PICGO_KEY"),
"TUMY_KEY": os.environ.get("TUMY_KEY"),
"RETRY_TIME": 1000,
"PROXY": os.environ.get("PROXY"),
},
"ADMIN": {
"MANAGER_SWITCH": os.environ.get("MANAGER_SWITCH"),
"PASSWORD": os.environ.get("ADMINPASSWORD"),
},
"SERVER": {
"CF_CLEARANCE": os.environ.get("CF_CLEARANCE"),
"PORT": int(os.environ.get("PORT", 5200)),
},
"RETRY": {
"RETRYSWITCH": False,
"MAX_ATTEMPTS": MAX_RETRY_ATTEMPTS,
},
"TOKEN_STATUS_FILE": str(self.data_dir / "token_status.json"),
"SHOW_THINKING": self._get_bool_env("SHOW_THINKING", False),
"SHOW_SEARCH_RESULTS": self._get_bool_env("SHOW_SEARCH_RESULTS", True),
"IS_SUPER_GROK": self._get_bool_env("IS_SUPER_GROK", False),
"FILTERED_TAGS": self._get_list_env("FILTERED_TAGS", ["xaiArtifact"]),
}
def _get_bool_env(self, key: str, default: bool = False) -> bool:
"""Get boolean environment variable."""
return os.environ.get(key, str(default)).lower() == "true"
def _get_list_env(self, key: str, default: List[str]) -> List[str]:
"""Get comma-separated list from environment variable."""
value = os.environ.get(key)
if not value:
return default
return [tag.strip() for tag in value.split(",") if tag.strip()]
def _validate_configuration(self) -> None:
"""Validate configuration settings."""
issues = []
if not os.environ.get("API_KEY"):
issues.append("Missing required environment variable: API_KEY")
if not self._config["API"]["IS_CUSTOM_SSO"]:
sso_env = os.environ.get("SSO", "")
sso_super_env = os.environ.get("SSO_SUPER", "")
if not sso_env and not sso_super_env:
issues.append(
"No SSO tokens configured. Set SSO or SSO_SUPER environment variables."
)
proxy = self._config["API"]["PROXY"]
if proxy and not any(
proxy.startswith(p) for p in ["http://", "https://", "socks5://"]
):
issues.append(f"Invalid proxy format: {proxy}")
if issues:
for issue in issues:
print(f"Configuration issue: {issue}")
else:
print("Configuration validation passed")
def get(self, key_path: str, default: Any = None) -> Any:
"""Get configuration value using dot notation."""
keys = key_path.split(".")
value = self._config
for key in keys:
if isinstance(value, dict) and key in value:
value = value[key]
else:
return default
return value
def set(self, key_path: str, value: Any) -> None:
"""Set configuration value using dot notation."""
keys = key_path.split(".")
config = self._config
for key in keys[:-1]:
if key not in config:
config[key] = {}
config = config[key]
config[keys[-1]] = value
@property
def models(self) -> Dict[str, str]:
"""Get supported models mapping."""
return self._config["MODELS"]
@property
def data_directory(self) -> Path:
"""Get data directory path."""
return self.data_dir
class UtilityFunctions:
"""Collection of utility functions for common operations."""
@staticmethod
def get_proxy_configuration(proxy_url: Optional[str]) -> Dict[str, Any]:
"""Get proxy configuration for requests."""
if not proxy_url:
return {}
print(f"Using proxy: {proxy_url}")
if proxy_url.startswith("socks5://"):
proxy_config: Dict[str, Any] = {"proxy": proxy_url}
if "@" in proxy_url:
auth_part = proxy_url.split("@")[0].split("://")[1]
if ":" in auth_part:
username, password = auth_part.split(":", 1)
proxy_config["proxy_auth"] = (username, password)
return proxy_config
else:
return {"proxies": {"https": proxy_url, "http": proxy_url}}
@staticmethod
def organize_search_results(search_results: Dict[str, Any]) -> str:
"""Format search results for display."""
if not search_results or "results" not in search_results:
return ""
results = search_results["results"]
formatted_results = []
for index, result in enumerate(results):
title = result.get("title", "未知标题")
url = result.get("url", "#")
preview = result.get("preview", "无预览内容")
formatted_result = (
f"\r\n<details><summary>资料[{index}]: {title}</summary>\r\n"
f"{preview}\r\n\n[Link]({url})\r\n</details>"
)
formatted_results.append(formatted_result)
return "\n\n".join(formatted_results)
@staticmethod
def parse_error_response(response_text: str) -> Dict[str, Any]:
"""Parse error response with structured handling."""
if not response_text or not response_text.strip():
return {
"error_code": "EMPTY_RESPONSE",
"error": "Empty or invalid response received",
"details": [],
}
try:
try:
response = json.loads(response_text)
if isinstance(response, dict):
if "error" in response:
error = response["error"]
if isinstance(error, dict):
return {
"error_code": error.get("code"),
"error": error.get("message") or response_text,
"details": (
error.get("details", [])
if isinstance(error.get("details"), list)
else []
),
}
else:
return {
"error_code": "Unknown",
"error": str(error),
"details": [],
}
elif "message" in response:
return {
"error_code": response.get("code"),
"error": response.get("message") or response_text,
"details": (
response.get("details", [])
if isinstance(response.get("details"), list)
else []
),
}
else:
return {
"error_code": "Unknown",
"error": response_text,
"details": [],
}
except json.JSONDecodeError:
pass
if " - " in response_text:
json_str = response_text.split(" - ", 1)[1]
response = json.loads(json_str)
if isinstance(response, dict):
if "error" in response:
error = response["error"]
if isinstance(error, dict):
return {
"error_code": error.get("code"),
"error": error.get("message") or response_text,
"details": (
error.get("details", [])
if isinstance(error.get("details"), list)
else []
),
}
else:
return {
"error_code": "Unknown",
"error": str(error),
"details": [],
}
elif "message" in response:
return {
"error_code": response.get("code"),
"error": response.get("message") or response_text,
"details": (
response.get("details", [])
if isinstance(response.get("details"), list)
else []
),
}
except (json.JSONDecodeError, KeyError, AttributeError) as e:
print(f"Error parsing error response: {e}")
return {
"error_code": "Unknown",
"error": response_text or "Unknown error occurred",
"details": [],
}
@staticmethod
def create_retry_decorator(
max_attempts: int = MAX_RETRY_ATTEMPTS, base_delay: float = BASE_RETRY_DELAY
):
"""Create retry decorator with exponential backoff."""
def retry_decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
attempts = 0
last_error = None
while attempts < max_attempts:
try:
return func(*args, **kwargs)
except Exception as e:
attempts += 1
last_error = e
if attempts >= max_attempts:
print(
f"All retries failed ({max_attempts} attempts): {e}",
"RetryMechanism",
)
raise e
delay = min(base_delay * (2 ** (attempts - 1)), 60)
print(
f"Retry {attempts}/{max_attempts}, delay {delay}s: {e}",
"RetryMechanism",
)
time.sleep(delay)
raise last_error or Exception("Retry mechanism failed unexpectedly")
return wrapper
return retry_decorator
@staticmethod
async def run_in_thread_pool(func, *args, **kwargs):
"""Run synchronous function in thread pool for async compatibility."""
try:
loop = asyncio.get_running_loop()
ctx = contextvars.copy_context()
func_call = functools.partial(ctx.run, func, *args, **kwargs)
return await loop.run_in_executor(None, func_call)
except RuntimeError:
with ThreadPoolExecutor() as executor:
future = executor.submit(func, *args, **kwargs)
return future.result()
@staticmethod
def create_structured_error_response(
error_data: Union[str, Dict[str, Any]], status_code: int = 500
) -> Tuple[Dict[str, Any], int]:
"""Create structured error response."""
if isinstance(error_data, str):
error_data = UtilityFunctions.parse_error_response(error_data)
error_message = error_data.get("error", "Unknown error")
error_code = error_data.get("error_code")
error_details = error_data.get("details", [])
if not error_message or error_message.strip() == "":
error_message = "An error occurred while processing the request"
return {
"error": {
"message": str(error_message),
"type": "server_error",
"code": str(error_code),
"details": list(error_details) if error_details else [],
}
}, status_code
@dataclass
class TokenEntry:
"""Represents a single token entry with usage tracking."""
credential: TokenCredential
max_request_count: int
request_count: int
added_time: int
start_call_time: Optional[int] = None
def is_available(self) -> bool:
"""Check if token is available for use."""
return self.request_count < self.max_request_count
def can_be_reset(self, expiration_time_ms: int, current_time_ms: int) -> bool:
"""Check if token can be reset based on expiration time."""
if not self.start_call_time:
return False
return current_time_ms - self.start_call_time >= expiration_time_ms
def use_token(self) -> None:
"""Mark token as used."""
if not self.start_call_time:
self.start_call_time = int(time.time() * 1000)
self.request_count += 1
def reset_usage(self) -> None:
"""Reset token usage counters."""
self.request_count = 0
self.start_call_time = None
@dataclass
class ModelLimits:
"""Configuration for model request limits."""
request_frequency: int
expiration_time_ms: int
class ThreadSafeTokenManager:
"""Thread-safe token management with proper synchronization."""
def __init__(self, config: ConfigurationManager):
"""Initialize token manager with configuration."""
self.config = config
self._lock = threading.RLock()
self._token_storage: Dict[str, List[TokenEntry]] = {}
self._token_status: Dict[str, Dict[str, Dict[str, Any]]] = {}
self._expired_tokens: List[Tuple[str, str, int, TokenType]] = []
self._super_limits = {
"grok-3": ModelLimits(100, 3 * 60 * 60 * 1000),
"grok-3-deepsearch": ModelLimits(30, 24 * 60 * 60 * 1000),
"grok-3-deepersearch": ModelLimits(10, 3 * 60 * 60 * 1000),
"grok-3-reasoning": ModelLimits(30, 3 * 60 * 60 * 1000),
"grok-4": ModelLimits(20, 3 * 60 * 60 * 1000),
}
self._normal_limits = {
"grok-4": ModelLimits(5, int(11.5 * 60 * 60 * 1000)),
"grok-3": ModelLimits(20, 3 * 60 * 60 * 1000),
"grok-3-deepsearch": ModelLimits(10, 24 * 60 * 60 * 1000),
"grok-3-deepersearch": ModelLimits(3, 24 * 60 * 60 * 1000),
"grok-3-reasoning": ModelLimits(8, 24 * 60 * 60 * 1000),
}
self._reset_timer_started = False
self._load_token_status()
def _normalize_model_name(self, model: str) -> str:
"""Normalize model name for consistent lookup."""
if model.startswith("grok-") and not any(
keyword in model for keyword in ["deepsearch", "deepersearch", "reasoning"]
):
parts = model.split("-")
return f"{parts[0]}-{parts[1]}" if len(parts) >= 2 else model
return model
def _get_model_limits(self, token_type: TokenType) -> Dict[str, ModelLimits]:
"""Get model limits based on token type."""
return (
self._super_limits if token_type == TokenType.SUPER else self._normal_limits
)
def _save_token_status(self) -> None:
"""Save token status to persistent storage."""
try:
status_file = Path(self.config.get("TOKEN_STATUS_FILE"))
with open(status_file, "w", encoding="utf-8") as f:
json.dump(self._token_status, f, indent=2, ensure_ascii=False)
print("Token status saved to file")
except Exception as e:
print(f"Failed to save token status: {e}")
def _load_token_status(self) -> None:
"""Load token status from persistent storage and reconstruct token storage."""
try:
status_file = Path(self.config.get("TOKEN_STATUS_FILE"))
if status_file.exists():
with open(status_file, "r", encoding="utf-8") as f:
self._token_status = json.load(f)
print("Token status loaded from file")
self._reconstruct_token_storage()
except Exception as e:
print(f"Failed to load token status: {e}")
self._token_status = {}
def _reconstruct_token_storage(self) -> None:
"""Reconstruct _token_storage from _token_status."""
try:
reconstructed_count = 0
for sso_value, models_data in self._token_status.items():
for model, model_data in models_data.items():
is_super = model_data.get("isSuper", False)
token_type = TokenType.SUPER if is_super else TokenType.NORMAL
credential = TokenCredential.from_raw_token(sso_value, token_type)
token_entry = TokenEntry(
credential=credential,
max_request_count=model_data.get("max_request_count", 20),
request_count=model_data.get("request_count", 0),
added_time=model_data.get(
"added_time", int(time.time() * 1000)
),
start_call_time=model_data.get("start_call_time"),
)
if model_data.get("is_expired", False):
print(f"Skipping expired token for {model}")
continue
if model not in self._token_storage:
self._token_storage[model] = []
existing = next(
(
entry
for entry in self._token_storage[model]
if entry.credential.sso_token == credential.sso_token
),
None,
)
if not existing:
self._token_storage[model].append(token_entry)
reconstructed_count += 1
if reconstructed_count > 0:
print(f"Reconstructed {reconstructed_count} token entries")
except Exception as e:
print(f"Failed to reconstruct token storage: {e}")
def record_token_failure(
self, model: str, token_string: str, failure_reason: str, status_code: int
) -> None:
"""Record a token failure and potentially mark as expired."""
with self._lock:
try:
normalized_model = self._normalize_model_name(model)
credential = TokenCredential(token_string, TokenType.NORMAL)
sso_value = credential.extract_sso_value()
if (
sso_value in self._token_status
and normalized_model in self._token_status[sso_value]
):
status = self._token_status[sso_value][normalized_model]
status["failed_request_count"] = (
status.get("failed_request_count", 0) + 1
)
status["last_failure_time"] = int(time.time() * 1000)
status["last_failure_reason"] = f"{status_code}: {failure_reason}"
failure_threshold = 3
if status[
"failed_request_count"
] >= failure_threshold and status_code in [401, 403]:
status["is_expired"] = True
status["isValid"] = False
print(
f"Token marked as expired after {status['failed_request_count']} failures: {failure_reason}",
"TokenManager",
)
self._save_token_status()
print(
f"Recorded token failure for {model}: {failure_reason} (total failures: {status['failed_request_count']})",
"TokenManager",
)
except Exception as e:
print(f"Failed to record token failure: {e}")
def _is_token_expired(self, token_entry: TokenEntry, model: str) -> bool:
"""Check if a token is marked as expired."""
try:
sso_value = token_entry.credential.extract_sso_value()
if (
sso_value in self._token_status
and model in self._token_status[sso_value]
):
status = self._token_status[sso_value][model]
return status.get("is_expired", False)
return False
except Exception as e:
print(f"Failed to check token expiration: {e}")
return False
def add_token(
self, credential: TokenCredential, is_initialization: bool = False
) -> bool:
"""Add token to the management system."""
with self._lock:
try:
model_limits = self._get_model_limits(credential.token_type)
sso_value = credential.extract_sso_value()
for model, limits in model_limits.items():
if model not in self._token_storage:
self._token_storage[model] = []
existing_entry = next(
(
entry
for entry in self._token_storage[model]
if entry.credential.sso_token == credential.sso_token
),
None,
)
if not existing_entry:
token_entry = TokenEntry(
credential=credential,
max_request_count=limits.request_frequency,
request_count=0,
added_time=int(time.time() * 1000),
)
self._token_storage[model].append(token_entry)
if sso_value not in self._token_status:
self._token_status[sso_value] = {}
if model not in self._token_status[sso_value]:
self._token_status[sso_value][model] = {
"isValid": True,
"invalidatedTime": None,
"totalRequestCount": 0,
"isSuper": credential.token_type == TokenType.SUPER,
"max_request_count": limits.request_frequency,
"request_count": 0,
"added_time": token_entry.added_time,
"start_call_time": None,
"failed_request_count": 0,
"is_expired": False,
"last_failure_time": None,
"last_failure_reason": None,
}
if not is_initialization:
self._save_token_status()
print(
f"Token added successfully for type: {credential.token_type.value}",
"TokenManager",
)
return True
except Exception as e:
print(f"Failed to add token: {e}")
return False
def get_token_for_model(self, model: str) -> Optional[str]:
"""Get available token for specified model."""
with self._lock:
normalized_model = self._normalize_model_name(model)
if normalized_model not in self._token_storage:
return None
tokens = self._token_storage[normalized_model]
if not tokens:
return None
for token_entry in tokens:
if self._is_token_expired(token_entry, normalized_model):
continue
if token_entry.is_available():
token_entry.use_token()
try:
sso_value = token_entry.credential.extract_sso_value()
if (
sso_value in self._token_status
and normalized_model in self._token_status[sso_value]
):
status = self._token_status[sso_value][normalized_model]
status["totalRequestCount"] += 1
status["request_count"] = token_entry.request_count
status["start_call_time"] = token_entry.start_call_time
if (
token_entry.request_count
>= token_entry.max_request_count
):
status["isValid"] = False
status["invalidatedTime"] = int(time.time() * 1000)
except Exception as e:
print(f"Failed to update token status: {e}")
if not self._reset_timer_started:
self._start_reset_timer()
self._save_token_status()
return token_entry.credential.sso_token
return None
def remove_token_from_model(self, model: str, token_string: str) -> bool:
"""Remove specific token from model."""
with self._lock:
normalized_model = self._normalize_model_name(model)
if normalized_model not in self._token_storage:
return False
tokens = self._token_storage[normalized_model]
for i, token_entry in enumerate(tokens):
if token_entry.credential.sso_token == token_string:
removed_entry = tokens.pop(i)
self._expired_tokens.append(
(
token_string,
normalized_model,
int(time.time() * 1000),
removed_entry.credential.token_type,
)
)
print(f"Token removed from model {model}")
return True
return False
def get_token_count_for_model(self, model: str) -> int:
"""Get available token count for model."""
with self._lock:
normalized_model = self._normalize_model_name(model)
if normalized_model not in self._token_storage:
return 0
return len(self._token_storage[normalized_model])
def get_remaining_capacity(self) -> Dict[str, int]:
"""Get remaining request capacity for each model."""
with self._lock:
capacity_map = {}
for model, tokens in self._token_storage.items():
total_capacity = sum(entry.max_request_count for entry in tokens)
used_requests = sum(entry.request_count for entry in tokens)
capacity_map[model] = max(0, total_capacity - used_requests)
return capacity_map
def reduce_token_request_count(self, model: str, count: int) -> bool:
"""Reduce token request count (for error recovery)."""
with self._lock:
normalized_model = self._normalize_model_name(model)
if normalized_model not in self._token_storage:
return False
tokens = self._token_storage[normalized_model]
if not tokens:
return False
token_entry = tokens[0]
original_count = token_entry.request_count
token_entry.request_count = max(0, token_entry.request_count - count)
reduction = original_count - token_entry.request_count
try:
sso_value = token_entry.credential.extract_sso_value()
if (
sso_value in self._token_status
and normalized_model in self._token_status[sso_value]
):
status = self._token_status[sso_value][normalized_model]
status["totalRequestCount"] = max(
0, status["totalRequestCount"] - reduction
)
except Exception as e:
print(
f"Failed to update token status during reduction: {e}",
"TokenManager",
)
return True
def _start_reset_timer(self) -> None:
"""Start the token reset timer."""
def reset_expired_tokens():
while True:
try:
current_time = int(time.time() * 1000)
with self._lock:
tokens_to_remove = []
for token_info in self._expired_tokens:
token, model, expired_time, token_type = token_info
model_limits = self._get_model_limits(token_type)
if model in model_limits:
expiration_time = model_limits[model].expiration_time_ms
if current_time - expired_time >= expiration_time:
try:
credential = TokenCredential(token, token_type)
self._reactivate_token(
model, credential, model_limits[model]
)
tokens_to_remove.append(token_info)
except Exception as e:
print(
f"Failed to reactivate token: {e}",
"TokenManager",
)
for token_info in tokens_to_remove:
self._expired_tokens.remove(token_info)
for model, tokens in self._token_storage.items():
for token_entry in tokens:
token_type = token_entry.credential.token_type
model_limits = self._get_model_limits(token_type)
if model in model_limits:
if token_entry.can_be_reset(
model_limits[model].expiration_time_ms,
current_time,
):
token_entry.reset_usage()
try:
sso_value = (
token_entry.credential.extract_sso_value()
)
if (
sso_value in self._token_status
and model
in self._token_status[sso_value]
):
status = self._token_status[sso_value][
model
]
status["isValid"] = True
status["invalidatedTime"] = None
status["totalRequestCount"] = 0
status["request_count"] = (
token_entry.request_count
)
status["start_call_time"] = (
token_entry.start_call_time
)
except Exception as e:
print(
f"Failed to update status during reset: {e}",
"TokenManager",
)
self._save_token_status()
except Exception as e:
print(f"Error in token reset timer: {e}")
time.sleep(3600)
timer_thread = threading.Thread(target=reset_expired_tokens, daemon=True)
timer_thread.start()
self._reset_timer_started = True
def _reactivate_token(
self, model: str, credential: TokenCredential, limits: ModelLimits
) -> None:
"""Reactivate an expired token."""
existing = next(
(
entry
for entry in self._token_storage.get(model, [])
if entry.credential.sso_token == credential.sso_token
),
None,
)
if not existing:
if model not in self._token_storage:
self._token_storage[model] = []
token_entry = TokenEntry(
credential=credential,
max_request_count=limits.request_frequency,
request_count=0,
added_time=int(time.time() * 1000),
)
self._token_storage[model].append(token_entry)
try:
sso_value = credential.extract_sso_value()
if sso_value in self._token_status:
if model not in self._token_status[sso_value]:
self._token_status[sso_value][model] = {}
status = self._token_status[sso_value][model]
status["isValid"] = True
status["invalidatedTime"] = None
status["totalRequestCount"] = 0
status["isSuper"] = credential.token_type == TokenType.SUPER
status["max_request_count"] = token_entry.max_request_count
status["request_count"] = token_entry.request_count
status["added_time"] = token_entry.added_time
status["start_call_time"] = token_entry.start_call_time
except Exception as e:
print(f"Failed to update reactivated token status: {e}")
def delete_token(self, token_string: str) -> bool:
"""Delete token completely from the system."""
with self._lock:
try:
removed = False
credential = TokenCredential(token_string, TokenType.NORMAL)
sso_value = credential.extract_sso_value()
for model in self._token_storage:
tokens = self._token_storage[model]
original_length = len(tokens)
self._token_storage[model] = [
entry
for entry in tokens
if entry.credential.sso_token != token_string
]
if len(self._token_storage[model]) < original_length:
removed = True
if sso_value in self._token_status:
del self._token_status[sso_value]
self._expired_tokens = [
token_info
for token_info in self._expired_tokens
if token_info[0] != token_string
]
if removed:
self._save_token_status()
print(f"Token deleted successfully")
return removed
except Exception as e:
print(f"Failed to delete token: {e}")
return False
def get_all_tokens(self) -> List[str]:
"""Get all token strings in the system."""
with self._lock:
all_tokens = set()
for tokens in self._token_storage.values():
for entry in tokens:
all_tokens.add(entry.credential.sso_token)
return list(all_tokens)
def get_token_status_map(self) -> Dict[str, Dict[str, Dict[str, Any]]]:
"""Get complete token status mapping."""
with self._lock:
return dict(self._token_status)
def get_token_health_summary(self) -> Dict[str, Any]:
"""Get summary of token health across all models."""
with self._lock:
summary = {
"total_tokens": 0,
"healthy_tokens": 0,
"expired_tokens": 0,
"rate_limited_tokens": 0,
"tokens_with_failures": 0,
"total_failures": 0,
"by_model": {},
}
unique_tokens = set()
token_health_status = {}
for sso_value, models_data in self._token_status.items():
unique_tokens.add(sso_value)
is_expired = False
is_rate_limited = False
has_failures = False
total_token_failures = 0
for model, model_data in models_data.items():
if model not in summary["by_model"]:
summary["by_model"][model] = {
"total": 0,
"healthy": 0,
"expired": 0,
"rate_limited": 0,
"with_failures": 0,
}
summary["by_model"][model]["total"] += 1
if model_data.get("is_expired", False):
summary["by_model"][model]["expired"] += 1
is_expired = True
elif not model_data.get("isValid", True):
summary["by_model"][model]["rate_limited"] += 1
is_rate_limited = True
else:
summary["by_model"][model]["healthy"] += 1
failure_count = model_data.get("failed_request_count", 0)
if failure_count > 0:
summary["by_model"][model]["with_failures"] += 1
has_failures = True
total_token_failures += failure_count
token_health_status[sso_value] = {
"is_expired": is_expired,
"is_rate_limited": is_rate_limited,
"has_failures": has_failures,
"total_failures": total_token_failures,
}
summary["total_tokens"] = len(unique_tokens)
for sso_value in unique_tokens:
status = token_health_status[sso_value]
if status["is_expired"]:
summary["expired_tokens"] += 1
elif status["is_rate_limited"]:
summary["rate_limited_tokens"] += 1
else:
summary["healthy_tokens"] += 1
if status["has_failures"]:
summary["tokens_with_failures"] += 1
summary["total_failures"] += status["total_failures"]
return summary
@dataclass
class ImageTypeInfo:
"""Information about an image type."""
mime_type: str
file_name: str
extension: str
class ImageProcessor:
"""Handles image processing and type detection."""
IMAGE_SIGNATURES = {
b"\xff\xd8\xff": ("jpg", "image/jpeg"),
b"\x89PNG\r\n\x1a\n": ("png", "image/png"),
b"GIF89a": ("gif", "image/gif"),
b"GIF87a": ("gif", "image/gif"),
}
@classmethod
def is_base64_image(cls, s: str) -> bool:
"""Check if string is a valid base64 image by examining binary signatures."""
try:
decoded = base64.b64decode(s, validate=True)
return any(decoded.startswith(sig) for sig in cls.IMAGE_SIGNATURES)
except Exception:
return False
@classmethod
def get_extension_and_mime_from_header(cls, data: bytes) -> tuple:
"""Detect image format from binary header."""
for sig, (ext, mime) in cls.IMAGE_SIGNATURES.items():
if data.startswith(sig):
return ext, mime
return "jpg", "image/jpeg"
@classmethod
def get_image_type_info(cls, base64_string: str) -> ImageTypeInfo:
"""Enhanced image type detection with binary signature support."""
mime_type = "image/jpeg"
extension = "jpg"
if "data:image" in base64_string:
matches = re.search(
r"data:([a-zA-Z0-9]+\/[a-zA-Z0-9-.+]+);base64,", base64_string
)
if matches:
mime_type = matches.group(1)
extension = mime_type.split("/")[1]
else:
try:
image_data = base64.b64decode(base64_string, validate=True)
extension, mime_type = cls.get_extension_and_mime_from_header(
image_data
)
except Exception:
pass
file_name = f"image.{extension}"
return ImageTypeInfo(mime_type, file_name, extension)
class FileUploadManager:
"""Handles file and image upload operations."""
def __init__(
self, config: ConfigurationManager, token_manager: ThreadSafeTokenManager
):
"""Initialize file upload manager."""
self.config = config
self.token_manager = token_manager
def upload_text_file(self, content: str, model: str) -> str:
"""Upload text content as a file attachment."""
try:
content_base64 = base64.b64encode(content.encode("utf-8")).decode("utf-8")
upload_data = {
"fileName": "message.txt",
"fileMimeType": "text/plain",
"content": content_base64,
}
print("Uploading text file")
auth_token = self.token_manager.get_token_for_model(model)
if not auth_token:
raise TokenException(f"No available tokens for model: {model}")
cf_clearance = self.config.get("SERVER.CF_CLEARANCE", "")
cookie = f"{auth_token};{cf_clearance}" if cf_clearance else auth_token
proxy_config = UtilityFunctions.get_proxy_configuration(
self.config.get("API.PROXY")
)
response = curl_requests.post(
"https://grok.com/rest/app-chat/upload-file",
headers={
**get_dynamic_headers("POST", "/rest/app-chat/upload-file"),
"Cookie": cookie,
},
json=upload_data,
impersonate="chrome133a",
timeout=60,
**proxy_config,
)
if response.status_code != 200:
raise GrokApiException(
f"File upload failed with status: {response.status_code}",
"UPLOAD_FAILED",
)
result = response.json()
file_metadata_id = result.get("fileMetadataId", "")
if not file_metadata_id:
raise GrokApiException(
"No file metadata ID in response", "INVALID_RESPONSE"
)
print(f"Text file uploaded successfully: {file_metadata_id}", "FileUpload")
return file_metadata_id
except Exception as error:
print(f"Text file upload failed: {error}")
raise GrokApiException(
f"Text file upload failed: {error}", "UPLOAD_ERROR"
) from error
def upload_image(self, image_data: str, model: str) -> str:
"""Upload image with enhanced format support."""
try:
if "data:image" in image_data:
image_buffer = image_data.split(",")[1]
else:
image_buffer = image_data
image_info = ImageProcessor.get_image_type_info(image_data)
upload_data = {
"fileName": image_info.file_name,
"fileMimeType": image_info.mime_type,
"content": image_buffer,
}
print("Uploading image file")
auth_token = self.token_manager.get_token_for_model(model)
if not auth_token:
raise TokenException(f"No available tokens for model: {model}")
cf_clearance = self.config.get("SERVER.CF_CLEARANCE", "")
cookie = f"{auth_token};{cf_clearance}" if cf_clearance else auth_token
proxy_config = UtilityFunctions.get_proxy_configuration(
self.config.get("API.PROXY")
)
response = curl_requests.post(
"https://grok.com/rest/app-chat/upload-file",
headers={
**get_dynamic_headers("POST", "/rest/app-chat/upload-file"),
"Cookie": cookie,
},
json=upload_data,
impersonate="chrome133a",
timeout=60,
**proxy_config,
)
if response.status_code != 200:
print(
f"Image upload failed with status: {response.status_code}",
"ImageUpload",
)
return ""
result = response.json()
file_metadata_id = result.get("fileMetadataId", "")
if file_metadata_id:
print(f"Image uploaded successfully: {file_metadata_id}", "ImageUpload")
return file_metadata_id
except Exception as error:
print(f"Image upload failed: {error}")
return ""
@dataclass
class ProcessedMessage:
"""Result of message processing."""
content: str
file_attachments: List[str]
requires_file_upload: bool
upload_content: str = ""
class MessageContentProcessor:
"""Processes message content and handles complex formats."""
def __init__(self, file_upload_manager: FileUploadManager):
"""Initialize message processor."""
self.file_upload_manager = file_upload_manager
def remove_think_tags_and_images(self, text: str) -> str:
"""Remove think tags and base64 images from text."""
text = re.sub(r"<think>[\s\S]*?<\/think>", "", text).strip()
text = re.sub(r"!\[image\]\(data:.*?base64,.*?\)", "[图片]", text)
return text
def process_content_item(self, content_item: Any) -> str:
"""Process individual content item (text or image)."""
if isinstance(content_item, list):
text_parts = []
for item in content_item:
if isinstance(item, dict):
if item.get("type") == "image_url":
text_parts.append("[图片]")
elif item.get("type") == "text":
text_parts.append(
self.remove_think_tags_and_images(item.get("text", ""))
)
return "\n".join(filter(None, text_parts))
elif isinstance(content_item, dict):
if content_item.get("type") == "image_url":
return "[图片]"
elif content_item.get("type") == "text":
return self.remove_think_tags_and_images(content_item.get("text", ""))
elif isinstance(content_item, str):
return self.remove_think_tags_and_images(content_item)
return ""
def extract_image_attachments(self, content_item: Any, model: str) -> List[str]:
"""Extract and upload image attachments from content."""
attachments = []
if isinstance(content_item, list):
for item in content_item:
if isinstance(item, dict) and item.get("type") == "image_url":
image_url = item.get("image_url", {}).get("url", "")
if image_url:
file_id = self.file_upload_manager.upload_image(
image_url, model
)
if file_id:
attachments.append(file_id)
elif isinstance(content_item, dict) and content_item.get("type") == "image_url":
image_url = content_item.get("image_url", {}).get("url", "")
if image_url:
file_id = self.file_upload_manager.upload_image(image_url, model)
if file_id:
attachments.append(file_id)
return attachments
def process_messages(
self, messages: List[Dict[str, Any]], model: str
) -> ProcessedMessage:
"""Process list of messages into a single formatted string."""
formatted_messages = ""
all_file_attachments = []
message_length = 0
requires_file_upload = False
last_role = None
last_content = ""
for message in messages:
role = "assistant" if message.get("role") == "assistant" else "user"
is_last_message = message == messages[-1]
if is_last_message and "content" in message:
image_attachments = self.extract_image_attachments(
message["content"], model
)
all_file_attachments.extend(image_attachments)
text_content = self.process_content_item(message.get("content", ""))
if text_content or (is_last_message and all_file_attachments):
if role == last_role and text_content:
last_content += "\n" + text_content
role_header = f"{role.upper()}: "
last_index = formatted_messages.rindex(role_header)
formatted_messages = (
formatted_messages[:last_index]
+ f"{role_header}{last_content}\n"
)
else:
content_to_add = text_content or "[图片]"
formatted_messages += f"{role.upper()}: {content_to_add}\n"
last_content = text_content
last_role = role
message_length += len(formatted_messages)
if message_length >= MESSAGE_LENGTH_LIMIT:
requires_file_upload = True
if requires_file_upload:
last_message = messages[-1] if messages else {}
last_role = (
"assistant" if last_message.get("role") == "assistant" else "user"
)
last_text = self.process_content_item(last_message.get("content", ""))
final_content = f"{last_role.upper()}: {last_text or '[图片]'}"
try:
file_id = self.file_upload_manager.upload_text_file(
formatted_messages, model
)
if file_id:
all_file_attachments.insert(0, file_id)
formatted_messages = "基于txt文件内容进行回复："
except Exception as e:
print(f"Failed to upload conversation file: {e}", "MessageProcessor")
formatted_messages = final_content
if not formatted_messages.strip():
if requires_file_upload:
formatted_messages = "基于txt文件内容进行回复："
else:
raise ValidationException("Message content is empty after processing")
return ProcessedMessage(
content=formatted_messages.strip(),
file_attachments=all_file_attachments[:MAX_FILE_ATTACHMENTS],
requires_file_upload=requires_file_upload,
)
@dataclass
class ChatRequestConfig:
"""Configuration for chat request."""
model_name: str
message: str
file_attachments: List[str]
enable_search: bool
enable_image_generation: bool
enable_reasoning: bool
deepsearch_preset: str
temporary_conversation: bool
class GrokApiClient:
"""Clean, focused Grok API client with separated concerns."""
def __init__(
self, config: ConfigurationManager, token_manager: ThreadSafeTokenManager
):
"""Initialize Grok API client."""
self.config = config
self.token_manager = token_manager
self.file_upload_manager = FileUploadManager(config, token_manager)
self.message_processor = MessageContentProcessor(self.file_upload_manager)
def validate_model_and_request(
self, model: str, request_data: Dict[str, Any]
) -> str:
"""Validate model and request parameters."""
if model not in self.config.models:
raise ValidationException(f"Unsupported model: {model}")
if (
model in ["grok-4-imageGen", "grok-3-imageGen"]
and request_data.get("stream", False)
and not self.config.get("API.PICGO_KEY")
and not self.config.get("API.TUMY_KEY")
):
raise ValidationException(
"Image generation models require PICGO or TUMY API key for streaming"
)
return self.config.models[model]
def determine_search_and_generation_settings(self, model: str) -> tuple:
"""Determine search and generation settings based on model."""
enable_search = model in [
"grok-4-deepsearch",
"grok-3-search",
"grok-3-deepsearch",
"grok-3-deepersearch",
]
enable_image_generation = model in ["grok-4-imageGen", "grok-3-imageGen"]
enable_reasoning = model in [
"grok-3-reasoning",
"grok-4-reasoning",
"grok-3-deepsearch",
"grok-3-deepersearch",
"grok-4-deepsearch",
]
deepsearch_preset = ""
if model == "grok-3-deepsearch":
deepsearch_preset = "default"
elif model == "grok-3-deepersearch":
deepsearch_preset = "deeper"
return (
enable_search,
enable_image_generation,
enable_reasoning,
deepsearch_preset,
)
def validate_message_requirements(
self, model: str, messages: List[Dict[str, Any]]
) -> None:
"""Validate message requirements for specific models."""
if model in ["grok-4-imageGen", "grok-3-imageGen", "grok-3-deepsearch"]:
if not messages:
raise ValidationException("Messages cannot be empty")
last_message = messages[-1]
if last_message.get("role") != "user":
raise ValidationException(
f"Model {model} requires the last message to be from user"
)
def prepare_chat_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
"""Prepare chat request with clean separation of concerns."""
try:
model = str(request_data.get("model"))
messages = request_data.get("messages", [])
normalized_model = self.validate_model_and_request(model, request_data)
self.validate_message_requirements(model, messages)
(
enable_search,
enable_image_generation,
enable_reasoning,
deepsearch_preset,
) = self.determine_search_and_generation_settings(model)
if model in ["grok-4-imageGen", "grok-3-imageGen", "grok-3-deepsearch"]:
messages = [messages[-1]]
processed_message = self.message_processor.process_messages(messages, model)
request_config = ChatRequestConfig(
model_name=normalized_model,
message=processed_message.content,
file_attachments=processed_message.file_attachments,
enable_search=enable_search,
enable_image_generation=enable_image_generation,
enable_reasoning=enable_reasoning,
deepsearch_preset=deepsearch_preset,
temporary_conversation=self.config.get(
"API.IS_TEMP_CONVERSATION", False
),
)
return self.build_request_payload(request_config)
except Exception as e:
print(f"Failed to prepare chat request: {e}")
raise
def build_request_payload(self, config: ChatRequestConfig) -> Dict[str, Any]:
"""Build the final request payload."""
return {
"temporary": config.temporary_conversation,
"modelName": config.model_name,
"message": config.message,
"fileAttachments": config.file_attachments,
"imageAttachments": [],
"disableSearch": not config.enable_search,
"enableImageGeneration": config.enable_image_generation,
"returnImageBytes": False,
"returnRawGrokInXaiRequest": False,
"enableImageStreaming": False,
"imageGenerationCount": 1,
"forceConcise": False,
"toolOverrides": {
"imageGen": config.enable_image_generation,
"webSearch": config.enable_search,
"xSearch": config.enable_search,
"xMediaSearch": config.enable_search,
"trendsSearch": config.enable_search,
"xPostAnalyze": config.enable_search,
},
"enableSideBySide": True,
"sendFinalMetadata": True,
"customPersonality": "",
"deepsearchPreset": config.deepsearch_preset,
"isReasoning": config.enable_reasoning,
"disableTextFollowUps": True,
}
def make_request(
self, payload: Dict[str, Any], model: str, stream: bool = False
) -> Tuple[requests.Response, str]:
"""Make the actual HTTP request to Grok API and return response with used token."""
auth_token = self.token_manager.get_token_for_model(model)
if not auth_token:
token_count = self.token_manager.get_token_count_for_model(model)
if token_count == 0:
raise TokenException(
f"No tokens available for model: {model}. Please add tokens or check configuration."
)
else:
raise TokenException(
f"All tokens for model {model} are currently rate limited. Please try again later."
)
cf_clearance = self.config.get("SERVER.CF_CLEARANCE", "")
cookie = f"{auth_token};{cf_clearance}" if cf_clearance else auth_token
proxy_config = UtilityFunctions.get_proxy_configuration(
self.config.get("API.PROXY")
)
print(f"Making request to Grok API for model: {model}")
try:
response = curl_requests.post(
f"{self.config.get('API.BASE_URL')}/rest/app-chat/conversations/new",
headers={
**get_dynamic_headers("POST", "/rest/app-chat/conversations/new"),
"Cookie": cookie,
},
data=json.dumps(payload),
impersonate="chrome133a",
stream=stream,
timeout=120,
**proxy_config,
)
print(f"Response status: {response.status_code}")
return response, auth_token # type: ignore
except Exception as e:
print(f"HTTP request failed: {e}")
raise GrokApiException(f"HTTP request failed: {e}", "REQUEST_FAILED") from e
@dataclass
class ProcessingResult:
"""Result of processing a model response."""
token: Optional[str] = None
image_url: Optional[str] = None
new_state: Optional[ProcessingState] = None
should_skip: bool = False
class ModelResponseProcessor:
"""Stateless response processor for different model types."""
def __init__(self, config: ConfigurationManager):
"""Initialize response processor."""
self.config = config
def process_response(
self, response_data: Dict[str, Any], model: str, current_state: ProcessingState
) -> ProcessingResult:
"""Process model response based on model type and current state."""
try:
if response_data.get("doImgGen") or response_data.get(
"imageAttachmentInfo"
):
new_state = current_state.with_image_generation(True)
return ProcessingResult(new_state=new_state)
if current_state.is_generating_image:
cached_response = response_data.get("cachedImageGenerationResponse")
if cached_response and not current_state.image_generation_phase:
image_url = cached_response.get("imageUrl")
if image_url:
new_state = current_state.with_image_generation(True, 1)
return ProcessingResult(
image_url=image_url, new_state=new_state
)
if model == "grok-3":
return self._process_grok_3_response(response_data, current_state)
elif model == "grok-3-search":
return self._process_grok_3_search_response(
response_data, current_state
)
elif model in [
"grok-3-deepsearch",
"grok-3-deepersearch",
"grok-4-deepsearch",
]:
return self._process_deepsearch_response(response_data, current_state)
elif model == "grok-3-reasoning":
return self._process_reasoning_response(response_data, current_state)
elif model == "grok-4":
return self._process_grok_4_response(response_data, current_state)
elif model == "grok-4-reasoning":
return self._process_grok_4_reasoning_response(
response_data, current_state
)
else:
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token)
except Exception as e:
print(f"Error processing {model} response: {e}", "ResponseProcessor")
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token)
def _process_grok_3_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process Grok-3 model response."""
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
def _process_grok_3_search_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process Grok-3 search model response."""
if response_data.get("webSearchResults") and self.config.get(
"SHOW_SEARCH_RESULTS", True
):
search_results = UtilityFunctions.organize_search_results(
response_data["webSearchResults"]
)
token = f"\r\n<think>{search_results}</think>\r\n"
processed_token = self._transform_artifacts(token)
return ProcessingResult(token=processed_token, new_state=current_state)
else:
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
def _process_deepsearch_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process deep search model responses with thinking state management."""
show_thinking = self.config.get("SHOW_THINKING", False)
has_step_id = bool(response_data.get("messageStepId"))
message_tag = response_data.get("messageTag", "")
if has_step_id and not show_thinking:
return ProcessingResult(should_skip=True, new_state=current_state)
if has_step_id and not current_state.is_thinking:
token = "<think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(True)
return ProcessingResult(token=processed_token, new_state=new_state)
if not has_step_id and current_state.is_thinking and message_tag == "final":
token = "</think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(False)
return ProcessingResult(token=processed_token, new_state=new_state)
if (
has_step_id and current_state.is_thinking and message_tag == "assistant"
) or message_tag == "final":
token = response_data.get("token", "")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
if current_state.is_thinking and isinstance(response_data.get("token"), dict):
token_dict = response_data.get("token", {})
if token_dict.get("action") == "webSearch":
action_input = token_dict.get("action_input", {})
query = action_input.get("query", "")
processed_token = self._transform_artifacts(query) if query else None
return ProcessingResult(token=processed_token, new_state=current_state)
if current_state.is_thinking and response_data.get("webSearchResults"):
search_results = UtilityFunctions.organize_search_results(
response_data["webSearchResults"]
)
processed_token = self._transform_artifacts(search_results)
return ProcessingResult(token=processed_token, new_state=current_state)
return ProcessingResult(new_state=current_state)
def _process_reasoning_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process reasoning model responses."""
show_thinking = self.config.get("SHOW_THINKING", False)
is_thinking = response_data.get("isThinking", False)
if is_thinking and not show_thinking:
return ProcessingResult(should_skip=True, new_state=current_state)
if is_thinking and not current_state.is_thinking:
token = "<think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(True)
return ProcessingResult(token=processed_token, new_state=new_state)
if not is_thinking and current_state.is_thinking:
token = "</think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(False)
return ProcessingResult(token=processed_token, new_state=new_state)
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
def _process_grok_4_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process Grok-4 model response."""
if response_data.get("isThinking"):
return ProcessingResult(should_skip=True, new_state=current_state)
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
def _process_grok_4_reasoning_response(
self, response_data: Dict[str, Any], current_state: ProcessingState
) -> ProcessingResult:
"""Process Grok-4 reasoning model response."""
show_thinking = self.config.get("SHOW_THINKING", False)
is_thinking = response_data.get("isThinking", False)
message_tag = response_data.get("messageTag", "")
if is_thinking and not show_thinking:
return ProcessingResult(should_skip=True, new_state=current_state)
if is_thinking and not current_state.is_thinking and message_tag == "assistant":
token = "<think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(True)
return ProcessingResult(token=processed_token, new_state=new_state)
if not is_thinking and current_state.is_thinking and message_tag == "final":
token = "</think>" + response_data.get("token", "")
processed_token = self._transform_artifacts(token)
new_state = current_state.with_thinking(False)
return ProcessingResult(token=processed_token, new_state=new_state)
token = response_data.get("token")
processed_token = self._transform_artifacts(token) if token else None
return ProcessingResult(token=processed_token, new_state=current_state)
def _transform_artifacts(self, text: Any) -> str:
"""Artifact transformation now handled at streaming level - return unchanged."""
if not text:
return ""
return str(text) if not isinstance(text, str) else text
class ResponseImageHandler:
"""Handles image responses and uploads to image hosting services."""
def __init__(self, config: ConfigurationManager):
"""Initialize image handler."""
self.config = config
def handle_image_response(self, image_url: str) -> str:
"""Process image response and return appropriate format."""
max_retries = 2
retry_count = 0
image_data = None
while retry_count < max_retries:
try:
proxy_config = UtilityFunctions.get_proxy_configuration(
self.config.get("API.PROXY")
)
response = curl_requests.get(
f"https://assets.grok.com/{image_url}",
headers=get_dynamic_headers("GET", f"/assets/{image_url}"),
impersonate="chrome133a",
timeout=60,
**proxy_config,
)
if response.status_code == 200:
image_data = response.content
break
retry_count += 1
if retry_count == max_retries:
raise GrokApiException(
f"Failed to retrieve image after {max_retries} attempts: {response.status_code}",
"IMAGE_RETRIEVAL_FAILED",
)
time.sleep(self.config.get("API.RETRY_TIME", 1000) / 1000 * retry_count)
except Exception as error:
retry_count += 1
if retry_count == max_retries:
print(f"Image retrieval failed: {error}")
raise
time.sleep(self.config.get("API.RETRY_TIME", 1000) / 1000 * retry_count)
if not image_data:
raise GrokApiException("No image data retrieved", "NO_IMAGE_DATA")
picgo_key = self.config.get("API.PICGO_KEY")
tumy_key = self.config.get("API.TUMY_KEY")
if not picgo_key and not tumy_key:
base64_image = base64.b64encode(image_data).decode("utf-8")
content_type = "image/jpeg"
return f""
print("Uploading to image hosting service")
if picgo_key:
return self._upload_to_picgo(image_data, picgo_key)
elif tumy_key:
return self._upload_to_tumy(image_data, tumy_key)
base64_image = base64.b64encode(image_data).decode("utf-8")
return f""
def _upload_to_picgo(self, image_data: bytes, api_key: str) -> str:
"""Upload image to PicGo service."""
try:
files = {"source": ("image.jpg", image_data, "image/jpeg")}
headers = {"X-API-Key": api_key}
response = requests.post(
"https://www.picgo.net/api/1/upload",
files=files,
headers=headers,
timeout=30,
)
if response.status_code == 200:
result = response.json()
image_url = result.get("image", {}).get("url", "")
if image_url:
print("Image uploaded to PicGo successfully", "ImageHandler")
return f""
print(f"PicGo upload failed: {response.status_code}", "ImageHandler")
return "Image generation failed - PicGo upload error"
except Exception as e:
print(f"PicGo upload exception: {e}")
return "Image generation failed - PicGo service error"
def _upload_to_tumy(self, image_data: bytes, api_key: str) -> str:
"""Upload image to TuMy service."""
try:
files = {"file": ("image.jpg", image_data, "image/jpeg")}
headers = {
"Accept": "application/json",
"Authorization": f"Bearer {api_key}",
}
response = requests.post(
"https://tu.my/api/v1/upload", files=files, headers=headers, timeout=30
)
if response.status_code == 200:
result = response.json()
image_url = result.get("data", {}).get("links", {}).get("url", "")
if image_url:
print("Image uploaded to TuMy successfully", "ImageHandler")
return f""
print(f"TuMy upload failed: {response.status_code}", "ImageHandler")
return "Image generation failed - TuMy upload error"
except Exception as e:
print(f"TuMy upload exception: {e}")
return "Image generation failed - TuMy service error"
class StreamingTagFilter:
"""Configurable streaming filter for removing specified XML/HTML tags."""
def __init__(self, filtered_tags: List[str] = []):
"""
Initialize filter with configurable tag list.
Args:
filtered_tags: List of tag names to filter (case-insensitive)
e.g., ["xaiArtifact", "someOtherTag"]
"""
self.buffer = ""
self.filtered_tags = [tag.lower() for tag in (filtered_tags or ["xaiArtifact"])]
def _starts_with_special_tag(self, text: str) -> bool:
"""Check if text starts with any of our special tag patterns."""
if not text.startswith("<"):
return False
text_lower = text.lower()
if text_lower.startswith("</"):
tag_part = text_lower[2:]
for tag in self.filtered_tags:
if tag.startswith(tag_part) or text_lower.startswith(f"</{tag}"):
return True
else:
tag_part = text_lower[1:]
for tag in self.filtered_tags:
if tag.startswith(tag_part) or text_lower.startswith(f"<{tag}"):
return True
return False
def _extract_tag_name(self, tag_text: str) -> str:
"""Extract the tag name from a complete tag."""
if not tag_text.startswith("<"):
return ""
if tag_text.startswith("</"):
content = tag_text[2:]
else:
content = tag_text[1:]
content = content.rstrip("/>")
return content.split()[0].lower() if content.split() else content.lower()
def filter_chunk(self, chunk: str) -> str:
"""Filter chunk by removing complete special tags."""
if not chunk:
return ""
self.buffer += chunk
result = ""
i = 0
while i < len(self.buffer):
if self.buffer[i] != "<":
result += self.buffer[i]
i += 1
continue
j = i + 1
while j < len(self.buffer) and self.buffer[j] != ">":
j += 1
if j >= len(self.buffer):
partial_tag = self.buffer[i:]
if self._starts_with_special_tag(partial_tag):
break
else:
result += self.buffer[i]
i += 1
continue
complete_tag = self.buffer[i : j + 1]
tag_name = self._extract_tag_name(complete_tag)
if tag_name in self.filtered_tags:
pass
else:
result += complete_tag
i = j + 1
if i < len(self.buffer):
self.buffer = self.buffer[i:]
else:
self.buffer = ""
return result
def finalize(self) -> str:
"""Get any remaining content from buffer when stream ends."""
result = self.buffer
self.buffer = ""
return result
@dataclass
class StreamingContext:
"""Context for streaming response processing."""
model: str
processor: ModelResponseProcessor
image_handler: ResponseImageHandler
tag_filter: StreamingTagFilter
state: ProcessingState = field(default_factory=ProcessingState)
class StreamProcessor:
"""Handles streaming response processing."""
@staticmethod
def process_non_stream_response(
response: requests.Response, context: StreamingContext
) -> str:
"""Process non-streaming response and return complete content."""
print("Processing non-streaming response")
full_response = ""
current_state = context.state
try:
for chunk in response.iter_lines():
if not chunk:
continue
try:
line_data = json.loads(chunk.decode("utf-8").strip())
if line_data.get("error"):
print(
f"API error: {json.dumps(line_data, indent=2)}",
"StreamProcessor",
)
return json.dumps({"error": "RateLimitError"}) + "\n\n"
response_data = line_data.get("result", {}).get("response")
if not response_data:
continue
result = context.processor.process_response(
response_data, context.model, current_state
)
if result.new_state:
current_state = result.new_state
if result.should_skip:
continue
if result.token:
# Apply streaming tag filter to remove configured tags
filtered_token = context.tag_filter.filter_chunk(result.token)
if filtered_token:
full_response += filtered_token
if result.image_url:
image_content = context.image_handler.handle_image_response(
result.image_url
)
return image_content
except json.JSONDecodeError:
continue
except Exception as e:
print(f"Error processing response line: {e}", "StreamProcessor")
continue
# Finalize the tag filter to get any remaining content
final_content = context.tag_filter.finalize()
if final_content:
full_response += final_content
return full_response
except Exception as e:
print(f"Non-stream response processing failed: {e}", "StreamProcessor")
raise
@staticmethod
def process_stream_response(
response: requests.Response, context: StreamingContext
) -> Iterator[str]:
"""Process streaming response and yield formatted chunks."""
print("Processing streaming response")
current_state = context.state
try:
for chunk in response.iter_lines():
if not chunk:
continue
try:
line_data = json.loads(chunk.decode("utf-8").strip())
if line_data.get("error"):
print(
f"API error: {json.dumps(line_data, indent=2)}",
"StreamProcessor",
)
yield json.dumps({"error": "RateLimitError"}) + "\n\n"
return
response_data = line_data.get("result", {}).get("response")
if not response_data:
continue
result = context.processor.process_response(
response_data, context.model, current_state
)
if result.new_state:
current_state = result.new_state
if result.should_skip:
continue
if result.token:
# Apply streaming tag filter to remove configured tags
filtered_token = context.tag_filter.filter_chunk(result.token)
# Only yield if we have content after filtering
if filtered_token:
formatted_response = (
MessageProcessor.create_chat_completion_chunk(
filtered_token, context.model
)
)
yield f"data: {json.dumps(formatted_response)}\n\n"
if result.image_url:
image_content = context.image_handler.handle_image_response(
result.image_url
)
formatted_response = (
MessageProcessor.create_chat_completion_chunk(
image_content, context.model
)
)
yield f"data: {json.dumps(formatted_response)}\n\n"
except json.JSONDecodeError:
continue
except Exception as e:
print(f"Error processing stream line: {e}", "StreamProcessor")
continue
# Finalize the tag filter to get any remaining content
final_content = context.tag_filter.finalize()
if final_content:
formatted_response = MessageProcessor.create_chat_completion_chunk(
final_content, context.model
)
yield f"data: {json.dumps(formatted_response)}\n\n"
yield "data: [DONE]\n\n"
except Exception as e:
print(f"Stream processing failed: {e}")
error_response = MessageProcessor.create_error_response(str(e))
yield f"data: {json.dumps(error_response)}\n\n"
class MessageProcessor:
"""Creates properly formatted chat completion responses."""
@staticmethod
def create_chat_completion(message: str, model: str) -> Dict[str, Any]:
"""Create a complete chat completion response."""
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": message},
"finish_reason": "stop",
}
],
"usage": None,
}
@staticmethod
def create_chat_completion_chunk(message: str, model: str) -> Dict[str, Any]:
"""Create a streaming chat completion chunk."""
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [{"index": 0, "delta": {"content": message}}],
}
@staticmethod
def create_error_response(error_message: str) -> Dict[str, Any]:
"""Create an error response."""
return {
"id": f"chatcmpl-{uuid.uuid4()}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"error": {"message": error_message, "type": "server_error"},
}
class AuthenticationService:
"""Handles authentication and authorization."""
def __init__(
self, config: ConfigurationManager, token_manager: ThreadSafeTokenManager
):
"""Initialize authentication service."""
self.config = config
self.token_manager = token_manager
def validate_api_key(self, auth_header: Optional[str]) -> str:
"""Validate API key from authorization header."""
if not auth_header:
raise ValidationException("Authorization header missing")
auth_token = auth_header.replace("Bearer ", "").strip()
if not auth_token:
raise ValidationException("API key missing")
return auth_token
def process_authentication(self, auth_header: Optional[str]) -> bool:
"""Process authentication and add token if needed."""
try:
auth_token = self.validate_api_key(auth_header)
if self.config.get("API.IS_CUSTOM_SSO", False):
try:
credential = TokenCredential.from_raw_token(
auth_token, TokenType.NORMAL
)
success = self.token_manager.add_token(credential)
if not success:
print("Failed to add custom SSO token", "AuthenticationService")
return True
except Exception as e:
print(
f"Failed to process custom SSO token: {e}",
"AuthenticationService",
)
raise ValidationException(f"Invalid SSO token format: {e}")
else:
expected_key = self.config.get("API.API_KEY", "sk-123456")
if auth_token != expected_key:
print(f"Invalid API key provided", "AuthenticationService")
raise ValidationException("Invalid API key")
return True
except ValidationException:
raise
except Exception as e:
print(f"Authentication processing failed: {e}", "AuthenticationService")
raise ValidationException(f"Authentication failed: {e}") from e
def create_app(config: ConfigurationManager) -> Flask:
"""Create and configure Flask application."""
app = Flask(__name__)
app.config["SECRET_KEY"] = secrets.token_urlsafe(32)
app.wsgi_app = ProxyFix(app.wsgi_app, x_for=1, x_proto=1, x_host=1, x_prefix=1)
token_manager = ThreadSafeTokenManager(config)
auth_service = AuthenticationService(config, token_manager)
grok_client = GrokApiClient(config, token_manager)
response_processor = ModelResponseProcessor(config)
image_handler = ResponseImageHandler(config)
def create_error_response(
error_data: Union[str, Dict[str, Any]], status_code: int
) -> Tuple[Dict[str, Any], int]:
"""Create consistent error responses."""
return UtilityFunctions.create_structured_error_response(
error_data, status_code
)
@app.errorhandler(ValidationException)
def handle_validation_error(e: ValidationException) -> Tuple[Dict[str, Any], int]:
"""Handle validation exceptions."""
return create_error_response(
{"error": str(e), "error_code": "VALIDATION_ERROR"}, 400
)
@app.errorhandler(TokenException)
def handle_token_error(e: TokenException) -> Tuple[Dict[str, Any], int]:
"""Handle token-related exceptions."""
return create_error_response(
{"error": str(e), "error_code": "TOKEN_ERROR"}, 429
)
@app.errorhandler(RateLimitException)
def handle_rate_limit_error(e: RateLimitException) -> Tuple[Dict[str, Any], int]:
"""Handle rate limiting exceptions."""
return create_error_response(
{"error": str(e), "error_code": "RATE_LIMIT_ERROR"}, 429
)
@app.errorhandler(GrokApiException)
def handle_grok_api_error(e: GrokApiException) -> Tuple[Dict[str, Any], int]:
"""Handle Grok API exceptions."""
return create_error_response({"error": str(e), "error_code": e.error_code}, 500)
@app.errorhandler(500)
def handle_internal_error(e) -> Tuple[Dict[str, Any], int]:
"""Handle internal server errors."""
print(f"Internal server error: {e}")
return create_error_response("Internal server error", 500)
@app.route("/v1/chat/completions", methods=["POST"])
def chat_completions():
"""Main chat completions endpoint with clean separation of concerns."""
response_status_code = 500
try:
if not request.is_json:
raise ValidationException("Request must be JSON")
data = request.get_json()
if not data:
raise ValidationException("Request body is empty")
auth_service.process_authentication(request.headers.get("Authorization"))
model = data.get("model")
messages = data.get("messages", [])
stream = data.get("stream", False)
if not model:
raise ValidationException("Model parameter is required")
if not messages:
raise ValidationException("Messages parameter is required")
print(f"Processing chat completion request for model: {model}")
payload = grok_client.prepare_chat_request(data)
response = None
used_token = None
retry_count = 0
max_retries = config.get("RETRY.MAX_ATTEMPTS", MAX_RETRY_ATTEMPTS)
while retry_count < max_retries:
try:
response, used_token = grok_client.make_request(
payload, model, stream
)
response_status_code = response.status_code
if response.status_code == 200:
break
elif response.status_code == 429:
if token_manager.get_token_count_for_model(model) > 1:
print("Rate limited, retrying with different token")
retry_count += 1
continue
else:
raise RateLimitException(
"Rate limit exceeded and no alternative tokens available"
)
else:
error_text = (
response.text
if response.text
else f"HTTP {response.status_code} error"
)
print(
f"API request failed with status {response.status_code}: {error_text}"
)
if used_token:
token_manager.record_token_failure(
model, used_token, error_text, response.status_code
)
error_data = UtilityFunctions.parse_error_response(error_text)
raise GrokApiException(
error_data.get(
"error",
f"API request failed with status {response.status_code}",
),
error_data.get("error_code", "API_ERROR"),
)
except requests.exceptions.RequestException as e:
retry_count += 1
if retry_count >= max_retries:
raise GrokApiException(
f"Request failed after {max_retries} attempts: {e}",
"REQUEST_FAILED",
)
delay = BASE_RETRY_DELAY * (2 ** (retry_count - 1))
print(f"Request failed, retrying in {delay}s: {e}")
time.sleep(delay)
if not response or response.status_code != 200:
error_msg = f"Request failed with status: {response_status_code}"
raise GrokApiException(error_msg, "REQUEST_FAILED")
# Get configured filtered tags
filtered_tags = config.get("FILTERED_TAGS", ["xaiArtifact"])
context = StreamingContext(
model=model,
processor=response_processor,
image_handler=image_handler,
tag_filter=StreamingTagFilter(filtered_tags),
)
if stream:
def generate():
try:
yield from StreamProcessor.process_stream_response(
response, context
)
except Exception as e:
print(f"Stream processing error: {e}")
error_response = MessageProcessor.create_error_response(str(e))
yield f"data: {json.dumps(error_response)}\n\n"
return Response(
stream_with_context(generate()),
content_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
},
)
else:
message_content = StreamProcessor.process_non_stream_response(
response, context
)
formatted_response = MessageProcessor.create_chat_completion(
message_content, model
)
return jsonify(formatted_response)
except (
ValidationException,
TokenException,
RateLimitException,
GrokApiException,
):
raise
except Exception as e:
print(f"Unexpected error in chat completions: {e}")
raise GrokApiException("Internal server error", "INTERNAL_ERROR") from e
@app.route("/v1/models", methods=["GET"])
def list_models():
"""List available models."""
models_data = []
current_time = int(time.time())
for model_key in config.models.keys():
models_data.append(
{
"id": model_key,
"object": "model",
"created": current_time,
"owned_by": "grok",
}
)
return jsonify({"object": "list", "data": models_data})
@app.route("/health", methods=["GET"])
def health_check():
"""Health check endpoint."""
return jsonify(
{"status": "healthy", "timestamp": int(time.time()), "version": "2.0.0"}
)
@app.route("/", methods=["GET"])
def index():
"""Index page with basic information."""
return jsonify(
{"message": "Grok API Gateway", "version": "2.0.0", "status": "running"}
)
def check_admin_auth() -> bool:
"""Check admin authentication."""
if not config.get("ADMIN.MANAGER_SWITCH"):
return False
password = request.form.get("password") or request.args.get("password")
expected_password = config.get("ADMIN.PASSWORD")
return bool(password and expected_password and password == expected_password)
@app.route("/add_token", methods=["POST"])
def add_token():
"""Add token endpoint (admin only)."""
if not check_admin_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
token_data = request.form.get("tokens") or (
request.json and request.json.get("tokens")
)
if not token_data:
return jsonify({"error": "Token data required"}), 400
if isinstance(token_data, str):
try:
token_dict = json.loads(token_data)
except json.JSONDecodeError:
token_dict = {"token": token_data, "type": "normal"}
else:
token_dict = token_data
token_string = token_dict.get("token", "")
token_type_str = token_dict.get("type")
token_type = (
TokenType.SUPER if token_type_str == "super" else TokenType.NORMAL
)
if not token_string:
return jsonify({"error": "Token string required"}), 400
credential = TokenCredential(token_string, token_type)
success = token_manager.add_token(credential)
if success:
return jsonify({"message": "Token added successfully"})
else:
return jsonify({"error": "Failed to add token"}), 500
except Exception as e:
print(f"Error adding token: {e}")
return jsonify({"error": f"Failed to add token: {e}"}), 500
@app.route("/tokens_info", methods=["GET"])
def tokens_info():
"""Get token information (admin only)."""
if not check_admin_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
status_map = token_manager.get_token_status_map()
capacity_map = token_manager.get_remaining_capacity()
health_summary = token_manager.get_token_health_summary()
return jsonify(
{
"token_status": status_map,
"remaining_capacity": capacity_map,
"health_summary": health_summary,
}
)
except Exception as e:
print(f"Error getting token info: {e}")
return jsonify({"error": f"Failed to get token info: {e}"}), 500
def check_session_auth() -> bool:
"""Check session-based authentication for web manager."""
return session.get("is_logged_in", False)
@app.route("/manager/login", methods=["GET", "POST"])
def manager_login():
"""Manager login page and handler."""
if not config.get("ADMIN.MANAGER_SWITCH"):
return redirect("/")
if request.method == "POST":
password = request.form.get("password")
if password == config.get("ADMIN.PASSWORD"):
session["is_logged_in"] = True
return redirect("/manager")
return render_template("login.html", error=True)
return render_template("login.html", error=False)
@app.route("/manager")
def manager():
"""Main manager dashboard."""
if not check_session_auth():
return redirect("/manager/login")
return render_template("manager.html")
@app.route("/manager/api/get")
def get_manager_tokens():
"""Get tokens via manager API."""
if not check_session_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
status_map = token_manager.get_token_status_map()
health_summary = token_manager.get_token_health_summary()
return jsonify(
{"token_status": status_map, "health_summary": health_summary}
)
except Exception as e:
print(f"Error getting manager tokens: {e}")
return jsonify({"error": str(e)}), 500
@app.route("/manager/api/add", methods=["POST"])
def add_manager_token():
"""Add token via manager API."""
if not check_session_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
data = request.get_json()
if not data:
return jsonify({"error": "JSON data required"}), 400
sso = data.get("sso")
if not sso or not sso.strip():
return (
jsonify({"error": "SSO token is required and cannot be empty"}),
400,
)
credential = TokenCredential.from_raw_token(sso.strip(), TokenType.NORMAL)
success = token_manager.add_token(credential)
if success:
return jsonify({"success": True})
else:
return jsonify({"error": "Failed to add token"}), 500
except Exception as e:
print(f"Error adding manager token: {e}")
return jsonify({"error": str(e)}), 500
@app.route("/manager/api/delete", methods=["POST"])
def delete_manager_token():
"""Delete token via manager API."""
if not check_session_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
data = request.get_json()
if not data:
return jsonify({"error": "JSON data required"}), 400
sso = data.get("sso")
if not sso:
return jsonify({"error": "SSO token is required"}), 400
token_string = f"sso-rw={sso};sso={sso}"
success = token_manager.delete_token(token_string)
if success:
return jsonify({"success": True})
else:
return jsonify({"error": "Token not found or failed to delete"}), 404
except Exception as e:
print(f"Error deleting manager token: {e}")
return jsonify({"error": str(e)}), 500
@app.route("/manager/api/cf_clearance", methods=["POST"])
def set_cf_clearance():
"""Set CF clearance via manager API."""
if not check_session_auth():
return jsonify({"error": "Unauthorized"}), 401
try:
data = request.get_json()
if not data:
return jsonify({"error": "JSON data required"}), 400
cf_clearance = data.get("cf_clearance")
if not cf_clearance:
return jsonify({"error": "cf_clearance is required"}), 400
config.set("SERVER.CF_CLEARANCE", cf_clearance)
return jsonify({"success": True})
except Exception as e:
print(f"Error setting CF clearance: {e}")
return jsonify({"error": str(e)}), 500
@app.route("/get/tokens", methods=["GET"])
def get_tokens():
"""Legacy endpoint to get tokens."""
auth_token = request.headers.get("Authorization", "").replace("Bearer ", "")
if config.get("API.IS_CUSTOM_SSO", False):
return (
jsonify(
{"error": "Custom SSO mode cannot get polling SSO token status"}
),
403,
)
elif auth_token != config.get("API.API_KEY", "sk-123456"):
return jsonify({"error": "Unauthorized"}), 401
try:
return jsonify(token_manager.get_token_status_map())
except Exception as e:
print(f"Error getting tokens: {e}")
return jsonify({"error": str(e)}), 500
@app.route("/add/token", methods=["POST"])
def add_token_api():
"""API endpoint to add tokens (API key auth)."""
auth_token = request.headers.get("Authorization", "").replace("Bearer ", "")
if config.get("API.IS_CUSTOM_SSO", False):
return jsonify({"error": "Custom SSO mode cannot add SSO tokens"}), 403
elif auth_token != config.get("API.API_KEY", "sk-123456"):
return jsonify({"error": "Unauthorized"}), 401
try:
data = request.get_json()
if not data:
return jsonify({"error": "JSON data required"}), 400
sso = data.get("sso")
if not sso or not sso.strip():
return (
jsonify({"error": "SSO token is required and cannot be empty"}),
400,
)
credential = TokenCredential.from_raw_token(sso.strip(), TokenType.NORMAL)
success = token_manager.add_token(credential)
if success:
return jsonify({"message": "Token added successfully"})
else:
return jsonify({"error": "Failed to add token"}), 500
except Exception as e:
print(f"Error adding token via API: {e}")
return jsonify({"error": str(e)}), 500
return app
def initialize_application(
config: ConfigurationManager, token_manager: ThreadSafeTokenManager
) -> None:
"""Initialize the application with environment tokens."""
tokens_added = 0
sso_tokens = os.environ.get("SSO", "")
if sso_tokens:
for token in sso_tokens.split(","):
token = token.strip()
if token:
try:
credential = TokenCredential(token, TokenType.NORMAL)
if token_manager.add_token(credential, is_initialization=True):
tokens_added += 1
except Exception as e:
print(f"Failed to add normal token: {e}", "Initialization")
sso_super_tokens = os.environ.get("SSO_SUPER", "")
if sso_super_tokens:
for token in sso_super_tokens.split(","):
token = token.strip()
if token:
try:
credential = TokenCredential(token, TokenType.SUPER)
if token_manager.add_token(credential, is_initialization=True):
tokens_added += 1
except Exception as e:
print(f"Failed to add super token: {e}", "Initialization")
if tokens_added > 0:
print(f"Successfully loaded {tokens_added} tokens")
else:
print("No tokens loaded during initialization")
if not config.get("API.IS_CUSTOM_SSO", False):
print(
"Set SSO or SSO_SUPER environment variables, or enable IS_CUSTOM_SSO",
"Initialization",
)
proxy_url = config.get("API.PROXY")
initialize_statsig_manager(proxy_url=proxy_url)
if proxy_url:
print(f"StatsigManager initialized with proxy: {proxy_url}", "Initialization")
else:
print("StatsigManager initialized without proxy")
try:
statsig_manager = get_statsig_manager()
real_ip = statsig_manager.check_real_ip_sync()
if real_ip and real_ip not in ["error", "failed", "unknown"]:
print(f"Playwright real IP address: {real_ip}")
else:
print(f"Failed to get real IP address: {real_ip}")
except Exception as e:
print(f"Error checking real IP address: {e}")
print("Application initialization completed")
def cleanup_resources():
"""Clean up browser resources before shutdown"""
global _global_statsig_manager
if _global_statsig_manager:
try:
_global_statsig_manager.cleanup()
print("Browser resources cleaned up successfully")
except Exception as e:
print(f"Error cleaning up browser resources: {e}")
def main():
"""Main application entry point."""
try:
config = ConfigurationManager()
token_manager = ThreadSafeTokenManager(config)
initialize_application(config, token_manager)
app = create_app(config)
port = config.get("SERVER.PORT", 5200)
print(f"Starting Grok API Gateway on port {port}")
import atexit
atexit.register(cleanup_resources)
app.run(
host="0.0.0.0",
port=port,
debug=False,
threaded=False,
processes=1,
)
except KeyboardInterrupt:
print("Application stopped by user")
cleanup_resources()
except Exception as e:
print(f"Application failed to start: {e}")
cleanup_resources()
sys.exit(1)
finally:
cleanup_resources()
if __name__ == "__main__":
main()
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