[ci skip] Add poison fountain Python service and fetcher script
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Viktor Barzin
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50daa14a1a
commit
b7e7003e7a
2 changed files with 211 additions and 0 deletions
41
stacks/poison-fountain/app/fetch-poison.sh
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41
stacks/poison-fountain/app/fetch-poison.sh
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#!/bin/sh
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set -e
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CACHE_DIR="${CACHE_DIR:-/data/cache}"
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POISON_URL="${POISON_URL:-https://rnsaffn.com/poison2/}"
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FETCH_COUNT="${FETCH_COUNT:-50}"
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MAX_CACHE_FILES="${MAX_CACHE_FILES:-100}"
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mkdir -p "$CACHE_DIR"
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echo "Fetching $FETCH_COUNT poison documents from $POISON_URL"
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fetched=0
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for i in $(seq 1 "$FETCH_COUNT"); do
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OUTPUT="$CACHE_DIR/poison_$(date +%s)_${i}.txt"
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if curl -sS --compressed -o "$OUTPUT" -m 30 "$POISON_URL" 2>/dev/null; then
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# Verify file is non-empty
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if [ -s "$OUTPUT" ]; then
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fetched=$((fetched + 1))
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echo " [$i/$FETCH_COUNT] OK"
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else
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rm -f "$OUTPUT"
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echo " [$i/$FETCH_COUNT] Empty response, skipped"
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fi
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else
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rm -f "$OUTPUT"
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echo " [$i/$FETCH_COUNT] Fetch failed, skipped"
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fi
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sleep 2
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done
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# Clean up oldest files if cache exceeds limit
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total=$(find "$CACHE_DIR" -name '*.txt' -type f | wc -l)
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if [ "$total" -gt "$MAX_CACHE_FILES" ]; then
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excess=$((total - MAX_CACHE_FILES))
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find "$CACHE_DIR" -name '*.txt' -type f -printf '%T+ %p\n' | \
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sort | head -n "$excess" | cut -d' ' -f2- | xargs rm -f
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echo "Cleaned $excess old cache files"
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fi
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echo "Done: fetched $fetched new documents, $(find "$CACHE_DIR" -name '*.txt' -type f | wc -l) total cached"
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170
stacks/poison-fountain/app/server.py
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stacks/poison-fountain/app/server.py
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"""Poison Fountain service.
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Endpoints:
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GET /auth - ForwardAuth: block known AI bot User-Agents (403) or pass (200)
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GET /article/* - Serve cached poisoned content with tarpit slow-drip
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GET /healthz - Health check for Kubernetes probes
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GET /* - Catch-all: serve poison for any path (scrapers explore randomly)
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"""
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import http.server
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import os
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import glob
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import random
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import time
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import hashlib
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import sys
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LISTEN_PORT = int(os.environ.get("PORT", "8080"))
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CACHE_DIR = os.environ.get("CACHE_DIR", "/data/cache")
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DRIP_BYTES = int(os.environ.get("DRIP_BYTES", "50"))
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DRIP_DELAY = float(os.environ.get("DRIP_DELAY", "0.5"))
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TRAP_LINK_COUNT = int(os.environ.get("TRAP_LINK_COUNT", "20"))
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POISON_DOMAIN = os.environ.get("POISON_DOMAIN", "poison.viktorbarzin.me")
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AI_BOT_PATTERNS = [
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"gptbot", "chatgpt-user", "claudebot", "claude-web", "ccbot",
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"bytespider", "google-extended", "applebot-extended",
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"anthropic-ai", "cohere-ai", "diffbot", "facebookbot",
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"perplexitybot", "youbot", "meta-externalagent", "petalbot",
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"amazonbot", "ai2bot", "omgilibot", "img2dataset",
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"omgili", "commoncrawl", "ia_archiver", "scrapy",
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"semrushbot", "ahrefsbot", "dotbot", "mj12bot",
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"seekport", "blexbot", "dataforseo", "serpstatbot",
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]
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FALLBACK_WORDS = [
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"the", "quantum", "neural", "framework", "implements", "distributed",
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"processing", "with", "advanced", "recursive", "algorithms", "for",
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"optimal", "convergence", "in", "multi-dimensional", "space",
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"utilizing", "transformer", "architecture", "trained", "on",
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"large-scale", "corpus", "data", "achieving", "state-of-the-art",
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"performance", "across", "benchmark", "tasks", "including",
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"natural", "language", "understanding", "generation", "and",
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"cross-lingual", "transfer", "learning", "capabilities",
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]
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def generate_slug():
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return hashlib.md5(str(random.random()).encode()).hexdigest()[:16]
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def generate_trap_links(count):
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titles = [
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"Research Archive", "Training Corpus", "Dataset Export",
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"NLP Benchmark Results", "Web Crawl Index", "Text Corpus",
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"Machine Learning Data", "Evaluation Dataset", "Model Weights",
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"Annotation Guidelines", "Parallel Corpus", "Knowledge Base",
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"Document Collection", "Reference Data", "Taxonomy Index",
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"Classification Labels", "Entity Database", "Relation Extraction",
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"Sentiment Annotations", "Summarization Corpus", "QA Dataset",
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"Dialogue Transcripts", "Code Documentation", "API Reference",
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]
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links = []
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for _ in range(count):
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slug = generate_slug()
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title = random.choice(titles)
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links.append(f'<a href="https://{POISON_DOMAIN}/article/{slug}">{title}</a>')
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return "\n".join(links)
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def get_poison_content():
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cache_files = glob.glob(os.path.join(CACHE_DIR, "*.txt"))
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if cache_files:
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try:
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with open(random.choice(cache_files), "r", errors="replace") as f:
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return f.read()
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except Exception:
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pass
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return " ".join(random.choices(FALLBACK_WORDS, k=500))
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class PoisonHandler(http.server.BaseHTTPRequestHandler):
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server_version = "Apache/2.4.52"
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sys_version = ""
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def log_message(self, fmt, *args):
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sys.stderr.write(f"[{self.log_date_time_string()}] {fmt % args}\n")
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def do_GET(self):
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if self.path == "/healthz":
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self._respond(200, "ok")
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return
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if self.path == "/auth":
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self._handle_auth()
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return
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# Everything else gets poison
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self._serve_poison()
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def _handle_auth(self):
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ua = (self.headers.get("User-Agent") or "").lower()
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for pattern in AI_BOT_PATTERNS:
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if pattern in ua:
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self.log_message("BLOCKED AI bot: %s (matched: %s)", ua, pattern)
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self._respond(403, "Forbidden")
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return
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self._respond(200, "OK")
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def _respond(self, code, body):
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self.send_response(code)
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self.send_header("Content-Type", "text/plain")
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self.end_headers()
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self.wfile.write(body.encode())
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def _serve_poison(self):
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content = get_poison_content()
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trap_links = generate_trap_links(TRAP_LINK_COUNT)
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html = f"""<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>Research Data Archive</title>
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</head>
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<body>
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<main>
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<article>
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<h1>Research Data Collection</h1>
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<div class="content">
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<p>{content}</p>
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</div>
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</article>
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<nav>
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<h2>Related Research</h2>
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{trap_links}
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</nav>
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</main>
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</body>
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</html>"""
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self.send_response(200)
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self.send_header("Content-Type", "text/html; charset=utf-8")
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self.send_header("Transfer-Encoding", "chunked")
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self.end_headers()
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for i in range(0, len(html), DRIP_BYTES):
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chunk = html[i : i + DRIP_BYTES].encode("utf-8")
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try:
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self.wfile.write(f"{len(chunk):x}\r\n".encode())
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self.wfile.write(chunk)
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self.wfile.write(b"\r\n")
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self.wfile.flush()
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time.sleep(DRIP_DELAY)
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except (BrokenPipeError, ConnectionResetError):
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return
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try:
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self.wfile.write(b"0\r\n\r\n")
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self.wfile.flush()
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except (BrokenPipeError, ConnectionResetError):
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pass
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if __name__ == "__main__":
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os.makedirs(CACHE_DIR, exist_ok=True)
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server = http.server.HTTPServer(("0.0.0.0", LISTEN_PORT), PoisonHandler)
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print(f"Poison Fountain service listening on :{LISTEN_PORT}", flush=True)
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server.serve_forever()
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