1118 lines
46 KiB
Python
1118 lines
46 KiB
Python
"""Twitter GraphQL API client."""
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from __future__ import annotations
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import json
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import logging
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import math
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import random
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import re
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import time
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import urllib.parse
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import bs4
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from curl_cffi import requests as _cffi_requests
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from x_client_transaction import ClientTransaction
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from x_client_transaction.utils import generate_headers as _gen_ct_headers, get_ondemand_file_url
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from .constants import BEARER_TOKEN, USER_AGENT, SEC_CH_UA, SEC_CH_UA_MOBILE, SEC_CH_UA_PLATFORM
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from .models import Author, Metrics, Tweet, TweetMedia, UserProfile
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logger = logging.getLogger(__name__)
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# Shared curl_cffi session — impersonates Chrome 133 TLS/JA3/HTTP2 fingerprint
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_cffi_session = None # type: Optional[Any] # lazy init
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FALLBACK_QUERY_IDS = {
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# Read operations
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"HomeTimeline": "c-CzHF1LboFilMpsx4ZCrQ",
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"HomeLatestTimeline": "BKB7oi212Fi7kQtCBGE4zA",
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"Bookmarks": "VFdMm9iVZxlU6hD86gfW_A",
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"UserByScreenName": "1VOOyvKkiI3FMmkeDNxM9A",
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"UserTweets": "E3opETHurmVJflFsUBVuUQ",
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"SearchTimeline": "nWemVnGJ6A5eQAR5-oQeAg",
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"Likes": "lIDpu_NWL7_VhimGGt0o6A",
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"TweetDetail": "xd_EMdYvB9hfZsZ6Idri0w",
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"ListLatestTweetsTimeline": "RlZzktZY_9wJynoepm8ZsA",
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"Followers": "IOh4aS6UdGWGJUYTqliQ7Q",
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"Following": "zx6e-TLzRkeDO_a7p4b3JQ",
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# Write operations
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"CreateTweet": "IID9x6WsdMnTlXnzXGq8ng",
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"DeleteTweet": "VaenaVgh5q5ih7kvyVjgtg",
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"FavoriteTweet": "lI07N6Otwv1PhnEgXILM7A",
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"UnfavoriteTweet": "ZYKSe-w7KEslx3JhSIk5LA",
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"CreateRetweet": "ojPdsZsimiJrUGLR1sjUtA",
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"DeleteRetweet": "iQtK4dl5hBmXewYZuEOKVw",
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"CreateBookmark": "aoDbu3RHznuiSkQ9aNM67Q",
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"DeleteBookmark": "Wlmlj2-xzyS1GN3a6cj-mQ",
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}
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TWITTER_OPENAPI_URL = (
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"https://raw.githubusercontent.com/fa0311/twitter-openapi/"
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"main/src/config/placeholder.json"
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)
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_DEFAULT_FEATURES = {
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"rweb_video_screen_enabled": False,
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"profile_label_improvements_pcf_label_in_post_enabled": True,
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"responsive_web_profile_redirect_enabled": False,
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"rweb_tipjar_consumption_enabled": False,
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"verified_phone_label_enabled": False,
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"creator_subscriptions_tweet_preview_api_enabled": True,
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"responsive_web_graphql_timeline_navigation_enabled": True,
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"responsive_web_graphql_skip_user_profile_image_extensions_enabled": False,
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"premium_content_api_read_enabled": False,
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"communities_web_enable_tweet_community_results_fetch": True,
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"c9s_tweet_anatomy_moderator_badge_enabled": True,
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"responsive_web_grok_analyze_button_fetch_trends_enabled": False,
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"responsive_web_grok_analyze_post_followups_enabled": True,
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"responsive_web_jetfuel_frame": True,
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"responsive_web_grok_share_attachment_enabled": True,
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"responsive_web_grok_annotations_enabled": True,
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"articles_preview_enabled": True,
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"responsive_web_edit_tweet_api_enabled": True,
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"graphql_is_translatable_rweb_tweet_is_translatable_enabled": True,
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"view_counts_everywhere_api_enabled": True,
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"longform_notetweets_consumption_enabled": True,
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"responsive_web_twitter_article_tweet_consumption_enabled": True,
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"tweet_awards_web_tipping_enabled": False,
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"content_disclosure_indicator_enabled": True,
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"content_disclosure_ai_generated_indicator_enabled": True,
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"responsive_web_grok_show_grok_translated_post": True,
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"responsive_web_grok_analysis_button_from_backend": True,
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"post_ctas_fetch_enabled": True,
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"freedom_of_speech_not_reach_fetch_enabled": True,
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"standardized_nudges_misinfo": True,
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"tweet_with_visibility_results_prefer_gql_limited_actions_policy_enabled": True,
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"longform_notetweets_rich_text_read_enabled": True,
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"longform_notetweets_inline_media_enabled": False,
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"responsive_web_grok_image_annotation_enabled": True,
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"responsive_web_grok_imagine_annotation_enabled": True,
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"responsive_web_grok_community_note_auto_translation_is_enabled": False,
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"responsive_web_enhance_cards_enabled": False,
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}
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# Features dict that gets updated dynamically from x.com JS bundles
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FEATURES = dict(_DEFAULT_FEATURES)
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# Module-level caches (not thread-safe — CLI is single-threaded)
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_cached_query_ids = {} # type: Dict[str, str]
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_bundles_scanned = False
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class TwitterAPIError(RuntimeError):
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"""Represents HTTP/network errors from Twitter APIs."""
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def __init__(self, status_code, message):
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# type: (int, str) -> None
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super().__init__(message)
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self.status_code = status_code
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def _best_chrome_target():
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# type: () -> str
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"""Detect the best available Chrome impersonation target at runtime.
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curl_cffi versions differ in which Chrome targets they ship.
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e.g. 0.14.0 has chrome133a but not chrome133.
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"""
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try:
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from curl_cffi.requests import BrowserType
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available = {e.value for e in BrowserType}
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except Exception:
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available = set()
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# Preference order: exact chrome versions, then suffixed variants
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for target in ("chrome133", "chrome133a", "chrome136", "chrome131", "chrome130"):
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if target in available:
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return target
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# Fallback: pick highest chrome* with a pure numeric suffix
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chrome_targets = sorted(
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[v for v in available if v.startswith("chrome") and v.replace("chrome", "").isdigit()],
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key=lambda x: int(x.replace("chrome", "")),
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reverse=True,
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)
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return chrome_targets[0] if chrome_targets else "chrome131"
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def _get_cffi_session():
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# type: () -> Any
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"""Return shared curl_cffi session with Chrome impersonation and optional proxy."""
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global _cffi_session
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if _cffi_session is None:
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import os
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proxy = os.environ.get("TWITTER_PROXY", "")
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target = _best_chrome_target()
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_cffi_session = _cffi_requests.Session(
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impersonate=target,
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proxies={"https": proxy, "http": proxy} if proxy else None,
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)
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logger.info("curl_cffi impersonating %s", target)
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if proxy:
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logger.info("Using proxy: %s", proxy[:20] + "...")
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return _cffi_session
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def _url_fetch(url, headers=None):
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# type: (str, Optional[Dict[str, str]]) -> str
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"""URL fetch using curl_cffi for proper TLS fingerprint."""
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session = _get_cffi_session()
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resp = session.get(url, headers=headers or {}, timeout=30)
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resp.raise_for_status()
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return resp.text
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def _build_graphql_url(query_id, operation_name, variables, features, field_toggles=None):
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# type: (str, str, Dict[str, Any], Dict[str, Any], Optional[Dict[str, Any]]) -> str
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"""Build GraphQL GET URL with encoded variables/features/fieldToggles."""
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url = "https://x.com/i/api/graphql/%s/%s?variables=%s&features=%s" % (
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query_id,
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operation_name,
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urllib.parse.quote(json.dumps(variables, separators=(",", ":"))),
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urllib.parse.quote(json.dumps(features, separators=(",", ":"))),
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)
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if field_toggles:
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url += "&fieldToggles=%s" % urllib.parse.quote(
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json.dumps(field_toggles, separators=(",", ":"))
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)
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return url
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def _scan_bundles():
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# type: () -> None
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"""Scan Twitter JS bundles and cache queryId mappings."""
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global _bundles_scanned
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if _bundles_scanned:
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return
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_bundles_scanned = True
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try:
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html = _url_fetch("https://x.com", {"user-agent": USER_AGENT})
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script_pattern = re.compile(
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r'(?:src|href)=["\']'
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r'(https://abs\.twimg\.com/responsive-web/client-web[^"\']+\.js)'
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r'["\']'
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)
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script_urls = script_pattern.findall(html)
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except Exception as exc: # pragma: no cover - network-dependent branch
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logger.warning("Failed to scan JS bundles: %s", exc)
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return
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for script_url in script_urls:
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try:
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bundle = _url_fetch(script_url)
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op_pattern = re.compile(
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r'queryId:\s*"([A-Za-z0-9_-]+)"[^}]{0,200}'
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r'operationName:\s*"([^"]+)"'
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)
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for match in op_pattern.finditer(bundle):
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query_id, operation_name = match.group(1), match.group(2)
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_cached_query_ids.setdefault(operation_name, query_id)
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except Exception:
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continue
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logger.info("Scanned %d JS bundles, cached %d query IDs", len(script_urls), len(_cached_query_ids))
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def _update_features_from_html(html):
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# type: (str) -> None
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"""Extract live feature flags from x.com HTML and update the global FEATURES dict.
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Twitter embeds feature switch config in inline scripts on the homepage.
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We parse these to keep FEATURES in sync with the current frontend.
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"""
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try:
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# Look for feature flags in inline script content
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# Pattern: "featureSwitch":{"...":{"value":true/false},...}
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# Also try: features:{key:!0, key2:!1, ...} in JS bundles
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feature_pattern = re.compile(
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r'"([a-z][a-z0-9_]+)":\s*\{\s*"value"\s*:\s*(true|false)',
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re.IGNORECASE,
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)
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found = 0
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for match in feature_pattern.finditer(html):
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key = match.group(1)
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value = match.group(2).lower() == "true"
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# Only update keys that look like feature flags
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if any(prefix in key for prefix in ("responsive_web_", "rweb_", "longform_", "creator_", "communities_", "c9s_")):
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FEATURES[key] = value
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found += 1
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if found:
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logger.info("Updated %d feature flags from x.com", found)
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except Exception as exc:
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logger.debug("Feature extraction from HTML failed: %s", exc)
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def _fetch_from_github(operation_name):
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# type: (str) -> Optional[str]
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"""Fetch queryId from community-maintained twitter-openapi file."""
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try:
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payload = _url_fetch(TWITTER_OPENAPI_URL)
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parsed = json.loads(payload)
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operation = parsed.get(operation_name, {})
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query_id = operation.get("queryId")
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if isinstance(query_id, str) and query_id:
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return query_id
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except Exception as exc: # pragma: no cover - network-dependent branch
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logger.debug("GitHub queryId lookup failed: %s", exc)
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return None
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def _invalidate_query_id(operation_name):
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# type: (str) -> None
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"""Remove a cached queryId for an operation."""
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_cached_query_ids.pop(operation_name, None)
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def _resolve_query_id(operation_name, prefer_fallback=True):
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# type: (str, bool) -> str
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"""Resolve queryId using cache, remote sources, and fallback constants."""
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cached = _cached_query_ids.get(operation_name)
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if cached:
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return cached
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fallback = FALLBACK_QUERY_IDS.get(operation_name)
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if prefer_fallback and fallback:
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_cached_query_ids[operation_name] = fallback
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return fallback
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github_query_id = _fetch_from_github(operation_name)
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if github_query_id:
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_cached_query_ids[operation_name] = github_query_id
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return github_query_id
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_scan_bundles()
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cached = _cached_query_ids.get(operation_name)
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if cached:
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return cached
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if fallback:
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_cached_query_ids[operation_name] = fallback
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return fallback
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raise RuntimeError('Cannot resolve queryId for "%s"' % operation_name)
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# Hard ceiling to prevent accidental massive fetches
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_ABSOLUTE_MAX_COUNT = 500
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class TwitterClient:
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"""Twitter GraphQL API client using cookie authentication."""
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def __init__(self, auth_token, ct0, rate_limit_config=None, cookie_string=None):
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# type: (str, str, Optional[Dict[str, Any]], Optional[str]) -> None
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self._auth_token = auth_token
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self._ct0 = ct0
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self._cookie_string = cookie_string # Full browser cookie string
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rl = rate_limit_config or {}
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self._request_delay = float(rl.get("requestDelay", 2.5))
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self._max_retries = int(rl.get("maxRetries", 3))
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self._retry_base_delay = float(rl.get("retryBaseDelay", 5.0))
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self._max_count = min(int(rl.get("maxCount", 200)), _ABSOLUTE_MAX_COUNT)
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self._client_transaction = None # type: Optional[Any]
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self._ct_init_attempted = False
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# Eagerly initialize ClientTransaction on construction
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self._ensure_client_transaction()
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def fetch_home_timeline(self, count=20):
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# type: (int) -> List[Tweet]
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"""Fetch home timeline tweets."""
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return self._fetch_timeline(
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"HomeTimeline",
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count,
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lambda data: _deep_get(data, "data", "home", "home_timeline_urt", "instructions"),
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)
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def fetch_following_feed(self, count=20):
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# type: (int) -> List[Tweet]
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"""Fetch chronological following feed."""
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return self._fetch_timeline(
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"HomeLatestTimeline",
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count,
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lambda data: _deep_get(data, "data", "home", "home_timeline_urt", "instructions"),
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)
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def fetch_bookmarks(self, count=50):
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# type: (int) -> List[Tweet]
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"""Fetch bookmarked tweets."""
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def get_instructions(data):
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# type: (Any) -> Any
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instructions = _deep_get(data, "data", "bookmark_timeline", "timeline", "instructions")
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if instructions is None:
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instructions = _deep_get(data, "data", "bookmark_timeline_v2", "timeline", "instructions")
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return instructions
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return self._fetch_timeline("Bookmarks", count, get_instructions)
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def fetch_user(self, screen_name):
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# type: (str) -> UserProfile
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"""Fetch user profile by screen name."""
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variables = {
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"screen_name": screen_name,
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"withSafetyModeUserFields": True,
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}
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features = {
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"hidden_profile_subscriptions_enabled": True,
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"rweb_tipjar_consumption_enabled": True,
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"responsive_web_graphql_exclude_directive_enabled": True,
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"verified_phone_label_enabled": False,
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"subscriptions_verification_info_is_identity_verified_enabled": True,
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"subscriptions_verification_info_verified_since_enabled": True,
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"highlights_tweets_tab_ui_enabled": True,
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"responsive_web_twitter_article_notes_tab_enabled": True,
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"subscriptions_feature_can_gift_premium": True,
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"creator_subscriptions_tweet_preview_api_enabled": True,
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"responsive_web_graphql_skip_user_profile_image_extensions_enabled": False,
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"responsive_web_graphql_timeline_navigation_enabled": True,
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}
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data = self._graphql_get("UserByScreenName", variables, features)
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result = _deep_get(data, "data", "user", "result")
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if not result:
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raise RuntimeError("User @%s not found" % screen_name)
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legacy = result.get("legacy", {})
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return UserProfile(
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id=result.get("rest_id", ""),
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name=legacy.get("name", ""),
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screen_name=legacy.get("screen_name", screen_name),
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bio=legacy.get("description", ""),
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location=legacy.get("location", ""),
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url=_deep_get(legacy, "entities", "url", "urls", 0, "expanded_url") or "",
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followers_count=_parse_int(legacy.get("followers_count"), 0),
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following_count=_parse_int(legacy.get("friends_count"), 0),
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tweets_count=_parse_int(legacy.get("statuses_count"), 0),
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likes_count=_parse_int(legacy.get("favourites_count"), 0),
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verified=bool(result.get("is_blue_verified") or legacy.get("verified", False)),
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profile_image_url=legacy.get("profile_image_url_https", ""),
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created_at=legacy.get("created_at", ""),
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)
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def fetch_user_tweets(self, user_id, count=20):
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# type: (str, int) -> List[Tweet]
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"""Fetch tweets posted by a user."""
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return self._fetch_timeline(
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"UserTweets",
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count,
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lambda data: _deep_get(data, "data", "user", "result", "timeline_v2", "timeline", "instructions"),
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extra_variables={
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"userId": user_id,
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"withQuickPromoteEligibilityTweetFields": True,
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"withVoice": True,
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"withV2Timeline": True,
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},
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)
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def fetch_user_likes(self, user_id, count=20):
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# type: (str, int) -> List[Tweet]
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"""Fetch tweets liked by a user."""
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return self._fetch_timeline(
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"Likes",
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count,
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lambda data: _deep_get(data, "data", "user", "result", "timeline_v2", "timeline", "instructions"),
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extra_variables={
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"userId": user_id,
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"includePromotedContent": False,
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"withClientEventToken": False,
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"withBirdwatchNotes": False,
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"withVoice": True,
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},
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override_base_variables=True,
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)
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def fetch_search(self, query, count=20, product="Top"):
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# type: (str, int, str) -> List[Tweet]
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"""Search tweets by query.
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Args:
|
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query: Search query string.
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count: Max number of tweets to return.
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product: Search tab — "Top", "Latest", "People", "Photos", "Videos".
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"""
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return self._fetch_timeline(
|
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"SearchTimeline",
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count,
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lambda data: _deep_get(
|
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data, "data", "search_by_raw_query", "search_timeline", "timeline", "instructions",
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),
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extra_variables={
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"rawQuery": query,
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"querySource": "typed_query",
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"product": product,
|
|
},
|
|
override_base_variables=True,
|
|
)
|
|
|
|
def fetch_tweet_detail(self, tweet_id, count=20):
|
|
# type: (str, int) -> List[Tweet]
|
|
"""Fetch a tweet and its conversation thread (replies)."""
|
|
return self._fetch_timeline(
|
|
"TweetDetail",
|
|
count,
|
|
lambda data: _deep_get(data, "data", "tweetResult", "result", "timeline", "instructions")
|
|
or _deep_get(data, "data", "threaded_conversation_with_injections_v2", "instructions"),
|
|
extra_variables={
|
|
"focalTweetId": tweet_id,
|
|
"referrer": "tweet",
|
|
"with_rux_injections": False,
|
|
"includePromotedContent": True,
|
|
"rankingMode": "Relevance",
|
|
"withCommunity": True,
|
|
"withQuickPromoteEligibilityTweetFields": True,
|
|
"withBirdwatchNotes": True,
|
|
"withVoice": True,
|
|
},
|
|
override_base_variables=True,
|
|
field_toggles={
|
|
"withArticleRichContentState": True,
|
|
"withArticlePlainText": False,
|
|
"withGrokAnalyze": False,
|
|
"withDisallowedReplyControls": False,
|
|
},
|
|
)
|
|
|
|
def fetch_list_timeline(self, list_id, count=20):
|
|
# type: (str, int) -> List[Tweet]
|
|
"""Fetch tweets from a Twitter List."""
|
|
return self._fetch_timeline(
|
|
"ListLatestTweetsTimeline",
|
|
count,
|
|
lambda data: _deep_get(data, "data", "list", "tweets_timeline", "timeline", "instructions"),
|
|
extra_variables={"listId": list_id},
|
|
override_base_variables=True,
|
|
)
|
|
|
|
def fetch_followers(self, user_id, count=20):
|
|
# type: (str, int) -> List[UserProfile]
|
|
"""Fetch followers of a user."""
|
|
return self._fetch_user_list(
|
|
"Followers", user_id, count,
|
|
lambda data: _deep_get(data, "data", "user", "result", "timeline", "timeline", "instructions"),
|
|
)
|
|
|
|
def fetch_following(self, user_id, count=20):
|
|
# type: (str, int) -> List[UserProfile]
|
|
"""Fetch users that a user is following."""
|
|
return self._fetch_user_list(
|
|
"Following", user_id, count,
|
|
lambda data: _deep_get(data, "data", "user", "result", "timeline", "timeline", "instructions"),
|
|
)
|
|
|
|
# ── Write operations ────────────────────────────────────────────────
|
|
|
|
def _write_delay(self):
|
|
# type: () -> None
|
|
"""Sleep a random interval after write operations to avoid rate limits."""
|
|
delay = random.uniform(1.5, 4.0)
|
|
logger.debug("Write operation delay: %.1fs", delay)
|
|
time.sleep(delay)
|
|
|
|
def create_tweet(self, text, reply_to_id=None):
|
|
# type: (str, Optional[str]) -> str
|
|
"""Post a new tweet. Returns the new tweet ID."""
|
|
variables = {
|
|
"tweet_text": text,
|
|
"media": {"media_entities": [], "possibly_sensitive": False},
|
|
"semantic_annotation_ids": [],
|
|
"dark_request": False,
|
|
} # type: Dict[str, Any]
|
|
if reply_to_id:
|
|
variables["reply"] = {
|
|
"in_reply_to_tweet_id": reply_to_id,
|
|
"exclude_reply_user_ids": [],
|
|
}
|
|
data = self._graphql_post("CreateTweet", variables, FEATURES)
|
|
self._write_delay()
|
|
result = _deep_get(data, "data", "create_tweet", "tweet_results", "result")
|
|
if result:
|
|
return result.get("rest_id", "")
|
|
raise RuntimeError("Failed to create tweet")
|
|
|
|
def delete_tweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Delete a tweet. Returns True on success."""
|
|
variables = {"tweet_id": tweet_id, "dark_request": False}
|
|
self._graphql_post("DeleteTweet", variables)
|
|
self._write_delay()
|
|
return True
|
|
|
|
def like_tweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Like a tweet. Returns True on success."""
|
|
self._graphql_post("FavoriteTweet", {"tweet_id": tweet_id})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def unlike_tweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Unlike a tweet. Returns True on success."""
|
|
self._graphql_post("UnfavoriteTweet", {"tweet_id": tweet_id, "dark_request": False})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def retweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Retweet a tweet. Returns True on success."""
|
|
self._graphql_post("CreateRetweet", {"tweet_id": tweet_id, "dark_request": False})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def unretweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Undo a retweet. Returns True on success."""
|
|
self._graphql_post("DeleteRetweet", {"source_tweet_id": tweet_id, "dark_request": False})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def bookmark_tweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Bookmark a tweet. Returns True on success."""
|
|
self._graphql_post("CreateBookmark", {"tweet_id": tweet_id})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def unbookmark_tweet(self, tweet_id):
|
|
# type: (str) -> bool
|
|
"""Remove a tweet from bookmarks. Returns True on success."""
|
|
self._graphql_post("DeleteBookmark", {"tweet_id": tweet_id})
|
|
self._write_delay()
|
|
return True
|
|
|
|
def _fetch_timeline(self, operation_name, count, get_instructions, extra_variables=None, override_base_variables=False, field_toggles=None):
|
|
# type: (str, int, Callable[[Any], Any], Optional[Dict[str, Any]], bool, Optional[Dict[str, Any]]) -> List[Tweet]
|
|
"""Generic timeline fetcher with pagination and deduplication.
|
|
|
|
Args:
|
|
override_base_variables: If True, use only extra_variables + count/cursor
|
|
instead of the default timeline base variables. Needed for
|
|
endpoints like SearchTimeline that reject unknown variables.
|
|
"""
|
|
if count <= 0:
|
|
return []
|
|
|
|
# Enforce max count cap
|
|
count = min(count, self._max_count)
|
|
|
|
tweets = [] # type: List[Tweet]
|
|
seen_ids = set() # type: Set[str]
|
|
cursor = None # type: Optional[str]
|
|
attempts = 0
|
|
max_attempts = int(math.ceil(count / 20.0)) + 2
|
|
|
|
while len(tweets) < count and attempts < max_attempts:
|
|
attempts += 1
|
|
if override_base_variables:
|
|
variables = {"count": min(count - len(tweets) + 5, 40)} # type: Dict[str, Any]
|
|
else:
|
|
variables = {
|
|
"count": min(count - len(tweets) + 5, 40),
|
|
"includePromotedContent": False,
|
|
"latestControlAvailable": True,
|
|
"requestContext": "launch",
|
|
} # type: Dict[str, Any]
|
|
if extra_variables:
|
|
variables.update(extra_variables)
|
|
if cursor:
|
|
variables["cursor"] = cursor
|
|
|
|
data = self._graphql_get(operation_name, variables, FEATURES, field_toggles=field_toggles)
|
|
new_tweets, next_cursor = self._parse_timeline_response(data, get_instructions)
|
|
|
|
for tweet in new_tweets:
|
|
if tweet.id and tweet.id not in seen_ids:
|
|
seen_ids.add(tweet.id)
|
|
tweets.append(tweet)
|
|
|
|
if not next_cursor or not new_tweets:
|
|
break
|
|
cursor = next_cursor
|
|
|
|
# Rate-limit: sleep between paginated requests with jitter
|
|
if len(tweets) < count and self._request_delay > 0:
|
|
jitter = self._request_delay * random.uniform(0.7, 1.5)
|
|
logger.debug("Sleeping %.1fs between requests", jitter)
|
|
time.sleep(jitter)
|
|
|
|
return tweets[:count]
|
|
|
|
def _graphql_get(self, operation_name, variables, features, field_toggles=None):
|
|
# type: (str, Dict[str, Any], Dict[str, Any], Optional[Dict[str, Any]]) -> Dict[str, Any]
|
|
"""Issue GraphQL GET request with automatic stale-fallback retry."""
|
|
query_id = _resolve_query_id(operation_name, prefer_fallback=True)
|
|
using_fallback = query_id == FALLBACK_QUERY_IDS.get(operation_name)
|
|
url = _build_graphql_url(query_id, operation_name, variables, features, field_toggles)
|
|
|
|
try:
|
|
return self._api_get(url)
|
|
except TwitterAPIError as exc:
|
|
# Fallback query IDs can go stale. Retry with live lookup if 404.
|
|
if exc.status_code == 404 and using_fallback:
|
|
logger.info("Retrying %s with live queryId after 404", operation_name)
|
|
_invalidate_query_id(operation_name)
|
|
refreshed_query_id = _resolve_query_id(operation_name, prefer_fallback=False)
|
|
retry_url = _build_graphql_url(refreshed_query_id, operation_name, variables, features, field_toggles)
|
|
return self._api_get(retry_url)
|
|
raise RuntimeError(str(exc))
|
|
|
|
def _ensure_client_transaction(self):
|
|
# type: () -> None
|
|
"""Initialize ClientTransaction for x-client-transaction-id header.
|
|
|
|
Also attempts to extract live feature flags from JS bundles.
|
|
"""
|
|
if self._ct_init_attempted:
|
|
return
|
|
self._ct_init_attempted = True
|
|
try:
|
|
# Use curl_cffi for ClientTransaction init to maintain consistent
|
|
# Chrome TLS fingerprint. Using Python requests here would leak
|
|
# a different TLS fingerprint on the same IP — a detection vector.
|
|
cffi_session = _get_cffi_session()
|
|
ct_headers = _gen_ct_headers()
|
|
home_page = cffi_session.get(
|
|
"https://x.com", headers=ct_headers, timeout=10,
|
|
)
|
|
home_page_response = bs4.BeautifulSoup(home_page.content, "html.parser")
|
|
ondemand_url = get_ondemand_file_url(response=home_page_response)
|
|
ondemand_file = cffi_session.get(
|
|
ondemand_url, headers=ct_headers, timeout=10,
|
|
)
|
|
self._client_transaction = ClientTransaction(
|
|
home_page_response=home_page_response,
|
|
ondemand_file_response=ondemand_file.text,
|
|
)
|
|
logger.info("ClientTransaction initialized for x-client-transaction-id")
|
|
|
|
# Try to extract live FEATURES from the homepage JS bundles
|
|
_update_features_from_html(home_page.text)
|
|
except Exception as exc:
|
|
logger.warning("Failed to init ClientTransaction: %s", exc)
|
|
|
|
def _build_headers(self, url="", method="GET"):
|
|
# type: (str, str) -> Dict[str, str]
|
|
"""Build shared headers for authenticated API calls."""
|
|
headers = {
|
|
"Authorization": "Bearer %s" % BEARER_TOKEN,
|
|
"Cookie": self._cookie_string or "auth_token=%s; ct0=%s" % (self._auth_token, self._ct0),
|
|
"X-Csrf-Token": self._ct0,
|
|
"X-Twitter-Active-User": "yes",
|
|
"X-Twitter-Auth-Type": "OAuth2Session",
|
|
"X-Twitter-Client-Language": "en",
|
|
"User-Agent": USER_AGENT,
|
|
"Origin": "https://x.com",
|
|
"Referer": "https://x.com",
|
|
"Accept": "*/*",
|
|
"Accept-Language": "en-US,en;q=0.9",
|
|
"sec-ch-ua": SEC_CH_UA,
|
|
"sec-ch-ua-mobile": SEC_CH_UA_MOBILE,
|
|
"sec-ch-ua-platform": SEC_CH_UA_PLATFORM,
|
|
"Sec-Fetch-Dest": "empty",
|
|
"Sec-Fetch-Mode": "cors",
|
|
"Sec-Fetch-Site": "same-origin",
|
|
}
|
|
if method == "POST":
|
|
headers["Content-Type"] = "application/json"
|
|
# Generate x-client-transaction-id if available
|
|
if self._client_transaction and url:
|
|
try:
|
|
path = urllib.parse.urlparse(url).path
|
|
tid = self._client_transaction.generate_transaction_id(
|
|
method=method, path=path,
|
|
)
|
|
headers["X-Client-Transaction-Id"] = tid
|
|
except Exception as exc:
|
|
logger.debug("Failed to generate transaction id: %s", exc)
|
|
return headers
|
|
|
|
def _api_get(self, url):
|
|
# type: (str) -> Dict[str, Any]
|
|
"""Make authenticated GET request to Twitter API."""
|
|
return self._api_request(url, method="GET")
|
|
|
|
def _graphql_post(self, operation_name, variables, features=None):
|
|
# type: (str, Dict[str, Any], Optional[Dict[str, Any]]) -> Dict[str, Any]
|
|
"""Issue GraphQL POST request with automatic stale-fallback retry."""
|
|
query_id = _resolve_query_id(operation_name, prefer_fallback=True)
|
|
using_fallback = query_id == FALLBACK_QUERY_IDS.get(operation_name)
|
|
|
|
def _do_post(qid):
|
|
# type: (str) -> Dict[str, Any]
|
|
url = "https://x.com/i/api/graphql/%s/%s" % (qid, operation_name)
|
|
body = {"variables": variables, "queryId": qid} # type: Dict[str, Any]
|
|
if features:
|
|
body["features"] = features
|
|
return self._api_request(url, method="POST", body=body)
|
|
|
|
try:
|
|
return _do_post(query_id)
|
|
except TwitterAPIError as exc:
|
|
if exc.status_code == 404 and using_fallback:
|
|
logger.info("Retrying POST %s with live queryId after 404", operation_name)
|
|
_invalidate_query_id(operation_name)
|
|
refreshed = _resolve_query_id(operation_name, prefer_fallback=False)
|
|
return _do_post(refreshed)
|
|
raise RuntimeError(str(exc))
|
|
|
|
def _api_request(self, url, method="GET", body=None):
|
|
# type: (str, str, Optional[Dict[str, Any]]) -> Dict[str, Any]
|
|
"""Make authenticated request to Twitter API with retry on rate limits.
|
|
|
|
Uses curl_cffi for Chrome TLS/JA3/HTTP2 fingerprint impersonation.
|
|
Handles both GET and POST. Retries on HTTP 429 and JSON error code 88.
|
|
"""
|
|
headers = self._build_headers(url=url, method=method)
|
|
session = _get_cffi_session()
|
|
json_body = body # curl_cffi handles JSON serialization
|
|
|
|
for attempt in range(self._max_retries + 1):
|
|
try:
|
|
if method == "POST":
|
|
response = session.post(
|
|
url, headers=headers, json=json_body, timeout=30,
|
|
)
|
|
else:
|
|
response = session.get(url, headers=headers, timeout=30)
|
|
|
|
status_code = response.status_code
|
|
if status_code == 429 and attempt < self._max_retries:
|
|
wait = self._retry_base_delay * (2 ** attempt) + random.uniform(0, 2)
|
|
logger.warning(
|
|
"Rate limited (429), retrying in %.1fs (attempt %d/%d)",
|
|
wait, attempt + 1, self._max_retries,
|
|
)
|
|
time.sleep(wait)
|
|
continue
|
|
if status_code >= 400:
|
|
message = "Twitter API error %d: %s" % (status_code, response.text[:500])
|
|
raise TwitterAPIError(status_code, message)
|
|
|
|
payload = response.text
|
|
except TwitterAPIError:
|
|
raise
|
|
except Exception as exc:
|
|
raise TwitterAPIError(0, "Twitter API network error: %s" % exc)
|
|
|
|
try:
|
|
parsed = json.loads(payload)
|
|
except (json.JSONDecodeError, ValueError):
|
|
raise TwitterAPIError(0, "Twitter API returned invalid JSON")
|
|
|
|
if isinstance(parsed, dict) and parsed.get("errors"):
|
|
err_msg = parsed["errors"][0].get("message", "Unknown error")
|
|
# Rate limit can also surface as a JSON error (code 88)
|
|
err_code = parsed["errors"][0].get("code", 0)
|
|
if err_code == 88 and attempt < self._max_retries:
|
|
wait = self._retry_base_delay * (2 ** attempt) + random.uniform(0, 2)
|
|
logger.warning(
|
|
"Rate limited (code 88), retrying in %.1fs (attempt %d/%d)",
|
|
wait, attempt + 1, self._max_retries,
|
|
)
|
|
time.sleep(wait)
|
|
continue
|
|
# Write operation rate limits (retweet/like/bookmark limits)
|
|
# Code 348 = "retweet limit", 327 = "already retweeted"
|
|
# Provide user-friendly message
|
|
if err_code in (348, 349):
|
|
raise TwitterAPIError(
|
|
429, "Rate limited: %s (try again later, recommended wait: 15+ minutes)" % err_msg
|
|
)
|
|
raise TwitterAPIError(0, "Twitter API returned errors: %s" % err_msg)
|
|
|
|
# GraphQL write mutations return errors in data.errors (separate from top-level)
|
|
if isinstance(parsed, dict) and "data" in parsed:
|
|
data_obj = parsed["data"]
|
|
if isinstance(data_obj, dict):
|
|
for key, val in data_obj.items():
|
|
if isinstance(val, dict) and val.get("errors"):
|
|
inner_errors = val["errors"]
|
|
if inner_errors:
|
|
inner_msg = inner_errors[0].get("message", "Unknown error")
|
|
raise TwitterAPIError(0, "Twitter API: %s" % inner_msg)
|
|
|
|
return parsed
|
|
|
|
# Should not be reached, but just in case
|
|
raise TwitterAPIError(429, "Rate limited after %d retries" % self._max_retries)
|
|
|
|
def _fetch_user_list(self, operation_name, user_id, count, get_instructions):
|
|
# type: (str, str, int, Callable[[Any], Any]) -> List[UserProfile]
|
|
"""Generic user list fetcher (for followers/following) with pagination."""
|
|
if count <= 0:
|
|
return []
|
|
count = min(count, self._max_count)
|
|
users = [] # type: List[UserProfile]
|
|
seen_ids = set() # type: Set[str]
|
|
cursor = None # type: Optional[str]
|
|
attempts = 0
|
|
max_attempts = int(math.ceil(count / 20.0)) + 2
|
|
|
|
while len(users) < count and attempts < max_attempts:
|
|
attempts += 1
|
|
variables = {
|
|
"userId": user_id,
|
|
"count": min(count - len(users) + 5, 40),
|
|
"includePromotedContent": False,
|
|
} # type: Dict[str, Any]
|
|
if cursor:
|
|
variables["cursor"] = cursor
|
|
|
|
data = self._graphql_get(operation_name, variables, FEATURES)
|
|
instructions = get_instructions(data)
|
|
if not instructions:
|
|
logger.warning("No user list instructions found")
|
|
break
|
|
|
|
new_users = [] # type: List[UserProfile]
|
|
next_cursor = None # type: Optional[str]
|
|
for instruction in instructions:
|
|
entries = instruction.get("entries", [])
|
|
for entry in entries:
|
|
content = entry.get("content", {})
|
|
entry_type = content.get("entryType", "")
|
|
|
|
if entry_type == "TimelineTimelineItem":
|
|
item = content.get("itemContent", {})
|
|
user_results = _deep_get(item, "user_results", "result")
|
|
if user_results:
|
|
user = self._parse_user_result(user_results)
|
|
if user:
|
|
new_users.append(user)
|
|
elif entry_type == "TimelineTimelineCursor":
|
|
if content.get("cursorType") == "Bottom":
|
|
next_cursor = content.get("value")
|
|
|
|
for user in new_users:
|
|
if user.id and user.id not in seen_ids:
|
|
seen_ids.add(user.id)
|
|
users.append(user)
|
|
|
|
if not next_cursor or not new_users:
|
|
break
|
|
cursor = next_cursor
|
|
|
|
if len(users) < count and self._request_delay > 0:
|
|
time.sleep(self._request_delay * random.uniform(0.7, 1.5))
|
|
|
|
return users[:count]
|
|
|
|
@staticmethod
|
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def _parse_user_result(user_data):
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# type: (Dict[str, Any]) -> Optional[UserProfile]
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"""Parse a user result object into UserProfile."""
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if user_data.get("__typename") == "UserUnavailable":
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return None
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legacy = user_data.get("legacy", {})
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if not legacy:
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return None
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return UserProfile(
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id=user_data.get("rest_id", ""),
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name=legacy.get("name", ""),
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screen_name=legacy.get("screen_name", ""),
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bio=legacy.get("description", ""),
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location=legacy.get("location", ""),
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url=_deep_get(legacy, "entities", "url", "urls", 0, "expanded_url") or "",
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followers_count=legacy.get("followers_count", 0),
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following_count=legacy.get("friends_count", 0),
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tweets_count=legacy.get("statuses_count", 0),
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likes_count=legacy.get("favourites_count", 0),
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verified=user_data.get("is_blue_verified", False) or legacy.get("verified", False),
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profile_image_url=legacy.get("profile_image_url_https", ""),
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created_at=legacy.get("created_at", ""),
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)
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def _parse_timeline_response(self, data, get_instructions):
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# type: (Any, Callable[[Any], Any]) -> Tuple[List[Tweet], Optional[str]]
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"""Parse timeline GraphQL response into tweets and next cursor."""
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tweets = [] # type: List[Tweet]
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next_cursor = None # type: Optional[str]
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instructions = get_instructions(data)
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if not isinstance(instructions, list):
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logger.warning("No timeline instructions found")
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return tweets, next_cursor
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for instruction in instructions:
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entries = instruction.get("entries") or instruction.get("moduleItems") or []
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for entry in entries:
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content = entry.get("content", {})
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next_cursor = _extract_cursor(content) or next_cursor
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item_content = content.get("itemContent", {})
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result = _deep_get(item_content, "tweet_results", "result")
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if result:
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tweet = self._parse_tweet_result(result)
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if tweet:
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tweets.append(tweet)
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for nested_item in content.get("items", []):
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nested_result = _deep_get(
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nested_item,
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"item",
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"itemContent",
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"tweet_results",
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"result",
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)
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if nested_result:
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tweet = self._parse_tweet_result(nested_result)
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if tweet:
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tweets.append(tweet)
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return tweets, next_cursor
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def _parse_tweet_result(self, result, depth=0):
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# type: (Dict[str, Any], int) -> Optional[Tweet]
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"""Parse a single TweetResult into a Tweet dataclass."""
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if depth > 2:
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return None
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tweet_data = result
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if result.get("__typename") == "TweetWithVisibilityResults" and result.get("tweet"):
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tweet_data = result["tweet"]
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if tweet_data.get("__typename") == "TweetTombstone":
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return None
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legacy = tweet_data.get("legacy")
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core = tweet_data.get("core")
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if not isinstance(legacy, dict) or not isinstance(core, dict):
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return None
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user = _deep_get(core, "user_results", "result") or {}
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user_legacy = user.get("legacy", {})
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user_core = user.get("core", {})
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is_retweet = bool(_deep_get(legacy, "retweeted_status_result", "result"))
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actual_data = tweet_data
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actual_legacy = legacy
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actual_user = user
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actual_user_legacy = user_legacy
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if is_retweet:
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retweet_result = _deep_get(legacy, "retweeted_status_result", "result") or {}
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if retweet_result.get("__typename") == "TweetWithVisibilityResults" and retweet_result.get("tweet"):
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retweet_result = retweet_result["tweet"]
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rt_legacy = retweet_result.get("legacy")
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rt_core = retweet_result.get("core")
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if isinstance(rt_legacy, dict) and isinstance(rt_core, dict):
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actual_data = retweet_result
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actual_legacy = rt_legacy
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actual_user = _deep_get(rt_core, "user_results", "result") or {}
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actual_user_legacy = actual_user.get("legacy", {})
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media = _extract_media(actual_legacy)
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urls = [item.get("expanded_url", "") for item in _deep_get(actual_legacy, "entities", "urls") or []]
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quoted = _deep_get(actual_data, "quoted_status_result", "result")
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quoted_tweet = self._parse_tweet_result(quoted, depth=depth + 1) if isinstance(quoted, dict) else None
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author = _extract_author(actual_user, actual_user_legacy)
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retweeted_by = None # type: Optional[str]
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if is_retweet:
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retweeted_by = user_core.get("screen_name") or user_legacy.get("screen_name", "unknown")
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return Tweet(
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id=actual_data.get("rest_id", ""),
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text=actual_legacy.get("full_text", ""),
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author=author,
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metrics=Metrics(
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likes=_parse_int(actual_legacy.get("favorite_count"), 0),
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retweets=_parse_int(actual_legacy.get("retweet_count"), 0),
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replies=_parse_int(actual_legacy.get("reply_count"), 0),
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quotes=_parse_int(actual_legacy.get("quote_count"), 0),
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views=_parse_int(_deep_get(actual_data, "views", "count"), 0),
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bookmarks=_parse_int(actual_legacy.get("bookmark_count"), 0),
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),
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created_at=actual_legacy.get("created_at", ""),
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media=media,
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urls=urls,
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is_retweet=is_retweet,
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retweeted_by=retweeted_by,
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quoted_tweet=quoted_tweet,
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lang=actual_legacy.get("lang", ""),
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)
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def _extract_media(legacy):
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# type: (Dict[str, Any]) -> List[TweetMedia]
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"""Extract media items from tweet legacy data."""
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media = [] # type: List[TweetMedia]
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for media_item in _deep_get(legacy, "extended_entities", "media") or []:
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media_type = media_item.get("type", "")
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if media_type == "photo":
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media.append(
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TweetMedia(
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type="photo",
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url=media_item.get("media_url_https", ""),
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width=_deep_get(media_item, "original_info", "width"),
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height=_deep_get(media_item, "original_info", "height"),
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)
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)
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elif media_type in {"video", "animated_gif"}:
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variants = media_item.get("video_info", {}).get("variants", [])
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mp4_variants = [v for v in variants if v.get("content_type") == "video/mp4"]
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mp4_variants.sort(key=lambda v: v.get("bitrate", 0), reverse=True)
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media.append(
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TweetMedia(
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type=media_type,
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url=mp4_variants[0]["url"] if mp4_variants else media_item.get("media_url_https", ""),
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width=_deep_get(media_item, "original_info", "width"),
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height=_deep_get(media_item, "original_info", "height"),
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)
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)
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return media
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def _extract_author(user_data, user_legacy):
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# type: (Dict[str, Any], Dict[str, Any]) -> Author
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"""Extract Author from user result data."""
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user_core = user_data.get("core", {})
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return Author(
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id=user_data.get("rest_id", ""),
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name=user_core.get("name") or user_legacy.get("name") or user_data.get("name", "Unknown"),
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screen_name=(
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user_core.get("screen_name")
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or user_legacy.get("screen_name")
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or user_data.get("screen_name", "unknown")
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),
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profile_image_url=(
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user_data.get("avatar", {}).get("image_url")
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or user_legacy.get("profile_image_url_https", "")
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),
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verified=bool(user_data.get("is_blue_verified") or user_legacy.get("verified", False)),
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)
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def _deep_get(data, *keys):
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# type: (Any, *Any) -> Any
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"""Safely get nested dict/list values. Supports int keys for list access."""
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current = data
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for key in keys:
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if isinstance(key, int):
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if isinstance(current, list) and 0 <= key < len(current):
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current = current[key]
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else:
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return None
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elif isinstance(current, dict):
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current = current.get(key)
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else:
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return None
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return current
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def _extract_cursor(content):
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# type: (Dict[str, Any]) -> Optional[str]
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"""Extract Bottom pagination cursor from timeline content."""
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if content.get("cursorType") == "Bottom":
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return content.get("value")
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return None
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def _parse_int(value, default):
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# type: (Any, int) -> int
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"""Best-effort integer conversion. Handles commas and float strings."""
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try:
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text = str(value).replace(",", "").strip()
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if not text:
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return default
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return int(float(text))
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except (TypeError, ValueError):
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return default
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