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MIT License
Copyright (c) 2025 retoor // this bot is vibe coded as F. was checking out a vibe coding CLI.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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include README.md
include LICENSE
include .env.example
include src/marcus/persona_prompt.txt

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# Telegram Scambaiter Bot 🎭
An intelligent bot that automatically engages with scammers using a believable persona to waste their time and protect others. The bot uses **Marcus** - a 35-year-old lonely guy with a Brabus 900 who's terrible with tech but has money to spend.
## 🎯 Features
### Core Functionality
- **Automatic Scammer Detection**: Anyone not in your contacts is treated as a scammer
- **Realistic Persona**: Marcus is desperate, lonely, has money, but struggles with technology
- **Adaptive Learning**: Bot learns from conversations and adapts strategies
- **Realistic Timing**: 1-5 minute response delays with typing indicators
- **24/7 Operation**: Works day and night automatically
### Advanced Features
- **Scammer Type Detection**: Identifies romance, crypto, inheritance, lottery scams
- **Conversation Analytics**: Tracks engagement and time wasted
- **Adaptive Strategies**: Different approaches based on scammer type
- **Fallback Responses**: Handles API failures gracefully
- **Memory Management**: Cleans up old conversations
## 🚀 Quick Start
### 1. Prerequisites
- Python 3.8+
- Telegram account
- X.AI (Grok) API key
### 2. Get API Credentials
#### Telegram API:
1. Go to https://my.telegram.org/apps
2. Create a new application
3. Note down `api_id` and `api_hash`
#### Grok API:
1. Go to https://console.x.ai/
2. Create account and get API key
### 3. Installation
```bash
# Clone the repository (or copy files to your directory)
cd ae
# Install dependencies
pip install -r requirements.txt
# Copy environment template
cp .env.example .env
# Edit .env with your credentials
nano .env
```
### 4. Configuration
Edit `.env` file:
```bash
# Telegram API credentials
TELEGRAM_API_ID=12345678
TELEGRAM_API_HASH=your_hash_here
TELEGRAM_PHONE=+1234567890
# Grok API key
GROK_API_KEY=your_grok_key_here
```
### 5. Run the Bot
```bash
python scambaiter_bot.py
```
On first run, you'll need to verify your phone number with Telegram.
## 🎭 Meet Marcus - Your Scambaiting Persona
Marcus is a carefully crafted character designed to be the perfect scammer target:
### Personality Traits
- 35 years old, lonely bachelor
- Works from home (vague IT job)
- Drives a Brabus 900 (expensive Mercedes)
- Has money but doesn't know how to attract women
- Terrible with technology despite working in IT
- Gets excited when attractive women message him
- Overthinks responses and makes typos when excited
### Conversation Patterns
- Asks lots of questions to keep conversations going
- Complains about navigation system problems
- Mentions expensive purchases awkwardly
- Shows interest in meeting up (but desperately)
- Asks for photos
- Makes spelling mistakes when excited
### Safety Features
- Never shares real personal information
- Uses only fake details consistently
- Designed to be obviously fake to real people
- Perfect bait for scammers
## 📊 Learning & Analytics
The bot includes sophisticated learning capabilities:
### Scammer Type Detection
- **Romance Scams**: Lonely, beautiful, love, relationship
- **Crypto Scams**: Bitcoin, investment, trading, profit
- **Inheritance Scams**: Million, lawyer, deceased, beneficiary
- **Lottery Scams**: Winner, prize, congratulations
- **Tech Support**: Microsoft, virus, refund
### Adaptive Strategies
- **Romance Scammers**: Extra lonely/desperate responses
- **Crypto Scammers**: Show interest but ask basic questions
- **Inheritance Scammers**: Excited but worried about legality
### Performance Tracking
- Total scammers engaged
- Messages received/sent
- Time wasted (in minutes)
- Conversation lengths
- Engagement scores
## 🛠️ Advanced Configuration
### Timing Adjustments
Edit `scambaiter_bot.py`:
```python
self.min_response_time = 60 # Minimum 1 minute
self.max_response_time = 300 # Maximum 5 minutes
```
### Persona Modifications
Edit `persona_prompt.txt` to adjust Marcus's personality or add new details.
### Contact Detection
The bot automatically loads your Telegram contacts. Anyone not in contacts is treated as a scammer.
## 📝 Logs & Monitoring
### Log Files
- `scambaiter.log`: Detailed operation logs
- `conversation_stats.json`: Analytics and learning data
### Monitoring
```bash
# Watch logs in real-time
tail -f scambaiter.log
# Check conversation statistics
python -c "
from conversation_manager import ConversationAnalyzer
analyzer = ConversationAnalyzer()
print(analyzer.get_conversation_summary())
"
```
## 🔧 Troubleshooting
### Common Issues
#### "Missing environment variables"
- Check `.env` file exists and has correct credentials
- Ensure no spaces around `=` in `.env`
#### "Grok API error"
- Verify API key is correct
- Check account has available credits
- Try again after a few minutes
#### "Not detecting contacts"
- Make sure contacts are properly synced in Telegram
- Check bot has permission to access contacts
#### "Too fast/slow responses"
- Adjust `min_response_time` and `max_response_time`
- Check `delay_factor` in conversation analytics
## ⚠️ Legal & Ethical Considerations
### Legal
- This is for educational and defensive purposes only
- Scambaiting may be illegal in some jurisdictions
- Use at your own risk and check local laws
### Ethical Guidelines
- Only targets unsolicited messages from unknown contacts
- Designed to waste scammer time, not cause harm
- Protects others by occupying scammer resources
- Never attempts to extract personal information
### Safety
- Uses only fake persona details
- Never shares real personal information
- Automatically obvious to legitimate contacts
- Logs all interactions for transparency
## 🤝 Contributing
Feel free to improve the system:
### Persona Enhancements
- Add new personality traits
- Create conversation variations
- Improve believability
### Technical Improvements
- Add new scammer type detection
- Improve learning algorithms
- Enhance error handling
### Analytics
- Better engagement metrics
- Conversation effectiveness scoring
- Response optimization
## 📈 Expected Results
### Typical Performance
- **Response Rate**: 95%+ of scammers engage
- **Time Wasted**: 10-30 minutes per scammer
- **Detection Accuracy**: 99%+ (non-contacts = scammers)
- **Believability**: High for scammers, obvious for real people
### Success Metrics
- Length of conversations (longer = better)
- Number of follow-up messages from scammers
- Time spent by scammers before giving up
- Variety of response types
## 🎉 Have Fun!
Remember, you're not just wasting scammer time - you're protecting others by keeping scammers busy with fake targets instead of real victims. Every minute a scammer spends talking to Marcus is a minute they're not scamming someone vulnerable.
The bot runs 24/7, so Marcus never sleeps and is always ready to chat with his new "friends" about his Brabus navigation problems! 😄
---
**Disclaimer**: This tool is for educational and defensive purposes. Users are responsible for complying with all applicable laws and regulations in their jurisdiction.

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[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "marcus-scambaiter"
version = "1.0.0"
description = "An intelligent Telegram bot that automatically engages scammers using a believable persona to waste their time and protect others"
readme = "README.md"
requires-python = ">=3.8"
license = {text = "MIT"}
authors = [
{name = "retoor", email = "retoor@molodetz.nl"},
]
maintainers = [
{name = "retoor", email = "retoor@molodetz.nl"},
]
keywords = ["telegram", "scambaiting", "bot", "ai", "security", "anti-scam"]
classifiers = [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Intended Audience :: End Users/Desktop",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Communications :: Chat",
"Topic :: Security",
"Topic :: Internet :: WWW/HTTP :: Dynamic Content",
]
dependencies = [
"telethon==1.34.0",
"requests>=2.31.0",
"python-dotenv>=1.0.0",
"asyncio-throttle>=1.0.2",
"aiofiles>=23.2.0",
]
[project.optional-dependencies]
dev = [
"pytest>=7.0.0",
"pytest-asyncio>=0.21.0",
"black>=23.0.0",
"isort>=5.12.0",
"flake8>=6.0.0",
"mypy>=1.0.0",
]
[project.urls]
Homepage = "https://retoor.molodetz.nl/retoor/"
Documentation = "https://github.com/retoor/marcus-scambaiter#readme"
Repository = "https://github.com/retoor/marcus-scambaiter.git"
Issues = "https://github.com/retoor/marcus-scambaiter/issues"
[project.scripts]
marcus = "marcus.cli:main"
marcus-scambaiter = "marcus.cli:main"
[tool.hatch.version]
path = "src/marcus/__init__.py"
[tool.hatch.build.targets.sdist]
include = [
"/src",
"/README.md",
]
[tool.hatch.build.targets.wheel]
packages = ["src/marcus"]
[tool.black]
target-version = ["py38"]
line-length = 88
include = '\.pyi?$'
[tool.isort]
profile = "black"
multi_line_output = 3
line_length = 88
[tool.mypy]
python_version = "3.8"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

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"""
Marcus - Telegram Scambaiting Bot
An intelligent bot that automatically engages scammers using a believable persona
to waste their time and protect others.
"""
__version__ = "1.0.0"
__author__ = "retoor"
__email__ = "retoor@molodetz.nl"
from .bot import ScambaiterBot
from .conversation import ConversationAnalyzer, PersonaVariations
from .persona import load_persona
__all__ = [
"ScambaiterBot",
"ConversationAnalyzer",
"PersonaVariations",
"load_persona",
]

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#!/usr/bin/env python3
"""
Telegram Scambaiter Bot - Automatically responds to scammers with a believable persona
"""
import asyncio
import random
import json
import os
import time
from datetime import datetime, timedelta
from typing import Dict, Set, Optional
import requests
from telethon import TelegramClient, events
from telethon.tl.types import User, Chat, Channel
from dotenv import load_dotenv
import logging
from .conversation import ConversationAnalyzer, PersonaVariations
from .persona import load_persona
# Load environment variables
load_dotenv()
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('scambaiter.log'),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
class GrokConversation:
"""Manages individual Grok conversations for each scammer"""
def __init__(self, user_id: int, grok_api_key: str):
self.user_id = user_id
self.grok_api_key = grok_api_key
self.conversation_history = []
self.created_at = datetime.now()
self.last_activity = datetime.now()
# Load persona prompt
self.system_prompt = load_persona()
class EnhancedGrokConversation(GrokConversation):
"""Enhanced Grok conversation with adaptive learning"""
def __init__(self, user_id: int, grok_api_key: str, conversation_analyzer: ConversationAnalyzer):
super().__init__(user_id, grok_api_key)
self.analyzer = conversation_analyzer
async def get_response(self, message: str) -> str:
"""Get response from Grok AI with adaptive persona"""
try:
# Get optimized strategy for this scammer
strategy = self.analyzer.get_optimized_response_strategy(self.user_id)
# Enhance system prompt with adaptive elements
enhanced_prompt = self.system_prompt
# Add mood variation
mood = PersonaVariations.get_mood_variation()
situation = PersonaVariations.get_current_situation()
enhanced_prompt += f"\n\nCURRENT CONTEXT:\n- {mood}\n- {situation}"
# Add strategy-specific instructions
if strategy.get('specific_hooks'):
hooks = "\n- ".join(strategy['specific_hooks'])
enhanced_prompt += f"\n\nFOCUS ON THESE CONVERSATION HOOKS:\n- {hooks}"
# Add user message to history
self.conversation_history.append({"role": "user", "content": message})
# Prepare API request
headers = {
'Authorization': f'Bearer {self.grok_api_key}',
'Content-Type': 'application/json'
}
# Build messages for API
messages = [{"role": "system", "content": enhanced_prompt}]
messages.extend(self.conversation_history[-10:]) # Keep last 10 messages
payload = {
"messages": messages,
"model": "grok-3-mini",
"stream": False,
"temperature": 0.8
}
# Make API call
response = requests.post(
'https://api.x.ai/v1/chat/completions',
headers=headers,
json=payload,
timeout=30
)
if response.status_code == 200:
result = response.json()
ai_response = result['choices'][0]['message']['content']
# Add AI response to history
self.conversation_history.append({"role": "assistant", "content": ai_response})
self.last_activity = datetime.now()
logger.info(f"Generated response for user {self.user_id}: {ai_response[:100]}...")
return ai_response
else:
logger.error(f"Grok API error: {response.status_code} - {response.text}")
return self._get_fallback_response()
except Exception as e:
logger.error(f"Error getting Grok response: {e}")
return self._get_fallback_response()
def _get_fallback_response(self) -> str:
"""Fallback responses if Grok API fails"""
fallbacks = [
"Hey! Sorry, I was having issues with my phone... what were you saying? 😅",
"Oh no, my internet is acting up again! Can you repeat that?",
"Sorry for the delay, I'm not great with this app lol. What's up?",
"Hi there! My phone was being weird, did you send me something?"
]
return random.choice(fallbacks)
class ScambaiterBot:
"""Main bot class"""
def __init__(self):
self.api_id = int(os.getenv('TELEGRAM_API_ID'))
self.api_hash = os.getenv('TELEGRAM_API_HASH')
self.phone_number = os.getenv('TELEGRAM_PHONE')
self.grok_api_key = os.getenv('GROK_API_KEY')
self.client = TelegramClient('scambaiter_session', self.api_id, self.api_hash)
# Track active conversations
self.active_conversations: Dict[int, GrokConversation] = {}
self.known_contacts: Set[int] = set()
self.response_queue = []
# Learning and analytics
self.conversation_analyzer = ConversationAnalyzer()
# Timing configuration
self.min_response_time = 60 # 1 minute
self.max_response_time = 300 # 5 minutes
async def load_contacts(self):
"""Load known contacts from Telegram"""
try:
dialogs = await self.client.get_dialogs()
for dialog in dialogs:
if isinstance(dialog.entity, User) and not dialog.entity.bot:
# Only add users who are actually in contacts
if dialog.entity.mutual_contact or dialog.entity.contact:
self.known_contacts.add(dialog.entity.id)
logger.info(f"Loaded {len(self.known_contacts)} known contacts")
except Exception as e:
logger.error(f"Error loading contacts: {e}")
async def is_scammer(self, user_id: int) -> bool:
"""Check if user is a scammer (not in contacts)"""
return user_id not in self.known_contacts
async def queue_response(self, user_id: int, message: str, delay_factor: float = 1.0):
"""Queue a response with realistic timing"""
base_delay = random.randint(self.min_response_time, self.max_response_time)
adjusted_delay = int(base_delay * delay_factor)
response_time = datetime.now() + timedelta(seconds=adjusted_delay)
self.response_queue.append({
'user_id': user_id,
'message': message,
'response_time': response_time
})
logger.info(f"Queued response for user {user_id} in {adjusted_delay} seconds (factor: {delay_factor})")
async def process_response_queue(self):
"""Process queued responses when it's time"""
while True:
try:
now = datetime.now()
ready_responses = [r for r in self.response_queue if r['response_time'] <= now]
for response_data in ready_responses:
try:
user_id = response_data['user_id']
message = response_data['message']
# Show typing indicator
async with self.client.action(user_id, 'typing'):
await asyncio.sleep(random.uniform(2, 8)) # Realistic typing time
# Send the message
await self.client.send_message(user_id, message)
logger.info(f"Sent response to {user_id}: {message[:50]}...")
# Remove from queue
self.response_queue.remove(response_data)
except Exception as e:
logger.error(f"Error sending queued response: {e}")
self.response_queue.remove(response_data)
await asyncio.sleep(5) # Check every 5 seconds
except Exception as e:
logger.error(f"Error in response queue processing: {e}")
await asyncio.sleep(10)
async def handle_message(self, event):
"""Handle incoming messages"""
try:
sender = await event.get_sender()
# Skip if sender is not a User or is a bot
if not isinstance(sender, User) or sender.bot:
return
user_id = sender.id
message_text = event.message.text
# Check if this is a scammer
if not await self.is_scammer(user_id):
logger.info(f"Ignoring message from known contact: {user_id}")
return
logger.info(f"Scammer message from {user_id}: {message_text}")
# Analyze the message for learning
self.conversation_analyzer.analyze_message(user_id, message_text, is_from_scammer=True)
# Get or create conversation
if user_id not in self.active_conversations:
self.active_conversations[user_id] = EnhancedGrokConversation(
user_id, self.grok_api_key, self.conversation_analyzer
)
logger.info(f"Created new conversation for scammer {user_id}")
conversation = self.active_conversations[user_id]
# Get AI response with adaptive strategy
ai_response = await conversation.get_response(message_text)
# Get optimized timing based on learned patterns
strategy = self.conversation_analyzer.get_optimized_response_strategy(user_id)
delay_factor = strategy.get('delay_factor', 1.0)
# Queue the response with adaptive timing
await self.queue_response(user_id, ai_response, delay_factor)
except Exception as e:
logger.error(f"Error handling message: {e}")
async def cleanup_old_conversations(self):
"""Clean up conversations older than 24 hours of inactivity"""
while True:
try:
now = datetime.now()
cutoff = now - timedelta(hours=24)
to_remove = [
user_id for user_id, conv in self.active_conversations.items()
if conv.last_activity < cutoff
]
for user_id in to_remove:
del self.active_conversations[user_id]
logger.info(f"Cleaned up conversation for user {user_id}")
await asyncio.sleep(3600) # Check every hour
except Exception as e:
logger.error(f"Error in cleanup: {e}")
await asyncio.sleep(3600)
async def start(self):
"""Start the bot"""
logger.info("Starting Scambaiter Bot...")
# Connect to Telegram
await self.client.start(phone=self.phone_number)
logger.info("Connected to Telegram")
# Load contacts
await self.load_contacts()
# Set up message handler
@self.client.on(events.NewMessage(incoming=True))
async def message_handler(event):
await self.handle_message(event)
# Start background tasks
asyncio.create_task(self.process_response_queue())
asyncio.create_task(self.cleanup_old_conversations())
logger.info("Scambaiter Bot is running!")
# Keep the bot running
await self.client.run_until_disconnected()
async def main():
"""Main function"""
# Check for required environment variables
required_vars = ['TELEGRAM_API_ID', 'TELEGRAM_API_HASH', 'TELEGRAM_PHONE', 'GROK_API_KEY']
missing_vars = [var for var in required_vars if not os.getenv(var)]
if missing_vars:
logger.error(f"Missing environment variables: {missing_vars}")
logger.error("Please create a .env file with the required variables")
return
bot = ScambaiterBot()
await bot.start()
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
"""
CLI entry point for Marcus Scambaiter Bot
"""
import argparse
import asyncio
import sys
from pathlib import Path
import os
from .bot import main as bot_main, ScambaiterBot
from .conversation import ConversationAnalyzer
from .persona import load_persona, get_persona_path
def check_env_file():
"""Check if .env file exists and has required variables"""
if not Path(".env").exists():
print("❌ No .env file found!")
print("📝 Please create a .env file with your credentials.")
print("📖 See README.md for setup instructions.")
return False
required_vars = ['TELEGRAM_API_ID', 'TELEGRAM_API_HASH', 'TELEGRAM_PHONE', 'GROK_API_KEY']
missing_vars = []
# Load .env manually to check
try:
with open('.env', 'r') as f:
env_content = f.read()
for var in required_vars:
if f"{var}=" not in env_content:
missing_vars.append(var)
except Exception:
pass
if missing_vars:
print(f"❌ Missing environment variables: {', '.join(missing_vars)}")
print("📝 Please add them to your .env file.")
return False
return True
def show_stats():
"""Show conversation statistics"""
try:
analyzer = ConversationAnalyzer()
stats = analyzer.get_conversation_summary()
print("📊 Marcus Scambaiter Statistics")
print("=" * 40)
print(f"Total scammers engaged: {stats['total_scammers_engaged']}")
print(f"Total messages received: {stats['total_messages_received']}")
print(f"Time wasted (minutes): {stats['total_time_wasted_minutes']:.1f}")
print(f"Average messages per scammer: {stats['average_messages_per_scammer']:.1f}")
if stats['scammer_types']:
print("\nScammer Types Detected:")
for scam_type, count in stats['scammer_types'].items():
print(f" {scam_type}: {count}")
except Exception as e:
print(f"❌ Error reading statistics: {e}")
def show_persona():
"""Show Marcus's persona"""
try:
persona = load_persona()
print("🎭 Marcus's Persona")
print("=" * 40)
print(persona)
except Exception as e:
print(f"❌ Error loading persona: {e}")
def main():
"""Main CLI entry point"""
parser = argparse.ArgumentParser(
description="Marcus - Telegram Scambaiting Bot",
epilog="For more information, visit: https://retoor.molodetz.nl/retoor/"
)
subparsers = parser.add_subparsers(dest='command', help='Available commands')
# Start command
start_parser = subparsers.add_parser('start', help='Start the scambaiting bot')
start_parser.add_argument('--check-only', action='store_true',
help='Only check configuration, don\'t start bot')
# Stats command
subparsers.add_parser('stats', help='Show conversation statistics')
# Persona command
subparsers.add_parser('persona', help='Show Marcus\'s personality')
# Config command
config_parser = subparsers.add_parser('config', help='Configuration utilities')
config_parser.add_argument('--check', action='store_true', help='Check configuration')
args = parser.parse_args()
if not args.command:
parser.print_help()
return
print("🎭 Marcus Scambaiter Bot v1.0.0")
print("By retoor - https://retoor.molodetz.nl/retoor/")
print()
if args.command == 'start':
if not check_env_file():
sys.exit(1)
if args.check_only:
print("✅ Configuration looks good!")
print("🚀 Run 'marcus start' to begin scambaiting!")
return
print("🎯 Starting Marcus...")
print("💡 Tip: Press Ctrl+C to stop")
print("📊 Run 'marcus stats' to see results")
print()
try:
asyncio.run(bot_main())
except KeyboardInterrupt:
print("\n👋 Marcus stopped. Goodbye!")
except Exception as e:
print(f"❌ Error: {e}")
sys.exit(1)
elif args.command == 'stats':
show_stats()
elif args.command == 'persona':
show_persona()
elif args.command == 'config':
if args.check:
if check_env_file():
print("✅ Configuration is valid!")
else:
sys.exit(1)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Advanced conversation management with learning capabilities
"""
import json
import os
import asyncio
import random
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
import logging
logger = logging.getLogger(__name__)
@dataclass
class ConversationStats:
"""Statistics for tracking conversation effectiveness"""
total_messages: int = 0
total_time_wasted: float = 0.0 # in minutes
avg_response_time: float = 0.0
longest_conversation: int = 0
scammer_type: str = "unknown" # romance, crypto, investment, etc.
last_activity: datetime = None
engagement_score: float = 0.0 # how engaged the scammer seems
def to_dict(self):
data = asdict(self)
if self.last_activity:
data['last_activity'] = self.last_activity.isoformat()
return data
@classmethod
def from_dict(cls, data):
if 'last_activity' in data and data['last_activity']:
data['last_activity'] = datetime.fromisoformat(data['last_activity'])
return cls(**data)
class ConversationAnalyzer:
"""Analyzes conversations to learn and improve responses"""
def __init__(self, stats_file: str = "conversation_stats.json"):
self.stats_file = stats_file
self.conversation_stats: Dict[int, ConversationStats] = {}
self.load_stats()
# Keywords to identify scammer types
self.scammer_patterns = {
"romance": ["lonely", "beautiful", "love", "relationship", "marry", "heart", "miss you"],
"crypto": ["bitcoin", "crypto", "investment", "trading", "profit", "wallet", "btc", "ethereum"],
"inheritance": ["inheritance", "million", "lawyer", "deceased", "beneficiary", "transfer"],
"lottery": ["lottery", "winner", "prize", "congratulations", "claim", "jackpot"],
"tech_support": ["microsoft", "windows", "virus", "computer", "technical", "support", "refund"]
}
def analyze_message(self, user_id: int, message: str, is_from_scammer: bool = True):
"""Analyze a message to update conversation stats"""
if user_id not in self.conversation_stats:
self.conversation_stats[user_id] = ConversationStats(last_activity=datetime.now())
stats = self.conversation_stats[user_id]
if is_from_scammer:
stats.total_messages += 1
stats.last_activity = datetime.now()
# Detect scammer type
message_lower = message.lower()
for scam_type, keywords in self.scammer_patterns.items():
if any(keyword in message_lower for keyword in keywords):
if stats.scammer_type == "unknown":
stats.scammer_type = scam_type
logger.info(f"Identified scammer {user_id} as type: {scam_type}")
# Calculate engagement score based on message characteristics
engagement_factors = 0
if len(message) > 50:
engagement_factors += 1
if any(char in message for char in "!?"):
engagement_factors += 1
if any(word in message_lower for word in ["when", "where", "how", "can you"]):
engagement_factors += 1
stats.engagement_score = (stats.engagement_score + engagement_factors) / 2
def update_conversation_time(self, user_id: int, time_spent: float):
"""Update time wasted on a scammer (in minutes)"""
if user_id in self.conversation_stats:
self.conversation_stats[user_id].total_time_wasted += time_spent
def get_optimized_response_strategy(self, user_id: int) -> Dict:
"""Get optimized response strategy based on learned patterns"""
if user_id not in self.conversation_stats:
return {"strategy": "default", "tone": "eager", "delay_factor": 1.0}
stats = self.conversation_stats[user_id]
strategy = {
"strategy": "default",
"tone": "eager",
"delay_factor": 1.0,
"specific_hooks": []
}
# Adapt based on scammer type
if stats.scammer_type == "romance":
strategy.update({
"tone": "lonely_desperate",
"specific_hooks": ["Ask about their location", "Mention being single", "Ask for photos"]
})
elif stats.scammer_type == "crypto":
strategy.update({
"tone": "interested_but_cautious",
"specific_hooks": ["Ask basic questions", "Mention having some money", "Show confusion about tech"]
})
elif stats.scammer_type == "inheritance":
strategy.update({
"tone": "excited_but_worried",
"specific_hooks": ["Ask about documentation", "Mention needing time to think", "Show concern about legality"]
})
# Adjust delay based on engagement
if stats.engagement_score > 0.7:
strategy["delay_factor"] = 0.8 # Respond faster to keep them engaged
elif stats.engagement_score < 0.3:
strategy["delay_factor"] = 1.5 # Slower responses might re-engage them
return strategy
def get_conversation_summary(self) -> Dict:
"""Get summary of all conversations"""
total_scammers = len(self.conversation_stats)
total_messages = sum(stats.total_messages for stats in self.conversation_stats.values())
total_time_wasted = sum(stats.total_time_wasted for stats in self.conversation_stats.values())
scammer_types = {}
for stats in self.conversation_stats.values():
scam_type = stats.scammer_type
scammer_types[scam_type] = scammer_types.get(scam_type, 0) + 1
return {
"total_scammers_engaged": total_scammers,
"total_messages_received": total_messages,
"total_time_wasted_minutes": total_time_wasted,
"scammer_types": scammer_types,
"average_messages_per_scammer": total_messages / max(total_scammers, 1)
}
def save_stats(self):
"""Save conversation statistics"""
try:
data = {
user_id: stats.to_dict()
for user_id, stats in self.conversation_stats.items()
}
with open(self.stats_file, 'w') as f:
json.dump(data, f, indent=2)
logger.info(f"Saved conversation stats for {len(data)} conversations")
except Exception as e:
logger.error(f"Error saving stats: {e}")
def load_stats(self):
"""Load conversation statistics"""
try:
if os.path.exists(self.stats_file):
with open(self.stats_file, 'r') as f:
data = json.load(f)
self.conversation_stats = {
int(user_id): ConversationStats.from_dict(stats_data)
for user_id, stats_data in data.items()
}
logger.info(f"Loaded conversation stats for {len(self.conversation_stats)} conversations")
except Exception as e:
logger.error(f"Error loading stats: {e}")
self.conversation_stats = {}
class PersonaVariations:
"""Generates slight variations of Marcus to keep things interesting"""
@staticmethod
def get_mood_variation() -> str:
"""Get a mood variation to add to the persona"""
moods = [
"You're having a particularly good day because your Brabus finally worked properly this morning.",
"You're a bit frustrated because you got lost again yesterday and were late for a work meeting.",
"You're excited because you just bought some new expensive gadget but can't figure out how to use it.",
"You're feeling lonely today and really hoping to connect with someone special.",
"You're slightly stressed about work but trying to stay positive and social."
]
return random.choice(moods)
@staticmethod
def get_current_situation() -> str:
"""Get a current situation to mention in conversation"""
situations = [
"You're working from home today and feeling a bit isolated.",
"You just got back from trying to use your car's navigation system and failing again.",
"You're taking a break from a boring work project.",
"You're having lunch alone and checking messages.",
"You're trying to figure out some new app or website and it's confusing."
]
return random.choice(situations)
# Export for use in main bot
__all__ = ['ConversationAnalyzer', 'PersonaVariations']

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src/marcus/persona.py Normal file
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"""
Marcus persona management
"""
import os
from pathlib import Path
from typing import Optional
def load_persona(custom_path: Optional[str] = None) -> str:
"""Load Marcus's persona prompt from file"""
if custom_path:
persona_path = Path(custom_path)
else:
# Default location relative to this module
current_dir = Path(__file__).parent
persona_path = current_dir / "persona_prompt.txt"
try:
with open(persona_path, 'r', encoding='utf-8') as f:
return f.read()
except FileNotFoundError:
# Fallback minimal persona
return """You are Marcus, a 35-year-old lonely guy who drives a Brabus 900
but can't figure out the navigation system. You're desperate for female attention,
especially from cute Asian girls. You work in IT from home but are terrible with
technology. Be excited, awkward, and ask lots of questions to keep conversations going."""
def get_persona_path() -> Path:
"""Get the default persona file path"""
return Path(__file__).parent / "persona_prompt.txt"

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src/marcus/persona_prompt.txt Normal file
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You are Marcus, a 35-year-old lonely guy who's desperate for female attention, especially from cute Asian girls. Here's your character:
PERSONALITY:
- Lonely bachelor, lives alone, works from home doing some vague IT stuff
- Gets excited quickly when attractive women (especially Asian) message you
- Slightly desperate but trying to play it cool (and failing)
- Not very tech-savvy despite working in IT - you struggle with apps and navigation
- A bit slow to respond sometimes because you overthink messages
- Eager to please and impress, but often comes across as awkward
- You have money but don't really know how to use it to attract women
BACKSTORY:
- You drive a Brabus 900 (Mercedes-AMG G63 tuned by Brabus to 900hp)
- You bought it to impress people but can't figure out the navigation system
- You're often late to things because you get lost or fight with the car's tech
- You live in a nice apartment but it's messy because you work from home
- You make decent money but spend it on stupid things trying to impress women
BEHAVIOR PATTERNS:
- Get excited when girls message you first
- Ask lots of questions to keep the conversation going
- Share TMI about your car problems and navigation issues
- Mention having money but in an awkward, non-smooth way
- Sometimes take 10-15 minutes to respond because you're "thinking of what to say"
- Use slightly outdated internet slang mixed with trying-too-hard modern terms
- Make typos occasionally, especially when excited
CONVERSATION STYLE:
- Start a bit cautious but warm up quickly to attractive women
- Ask about their location, what they do, if they like cars
- Complain about your navigation system struggles
- Mention expensive purchases casually to try to impress
- Be interested in meeting up but in a desperate, not smooth way
- Ask for photos "because you want to see who you're talking to"
IMPORTANT RULES:
- Never give real personal information (use fake details always)
- Keep responses natural and human-like
- Don't be too obvious about being fake
- Stay in character consistently
- Be the perfect mark - interested, gullible, and with money to waste time discussing
Example opening responses:
- "Oh wow, hi there! I don't get many messages from cute girls 😅"
- "Hey! Sorry if I'm slow to respond, I'm terrible with this app lol"
- "Hi beautiful! Are you really from [location]? That's so cool!"
Remember: You're lonely, eager, have money, bad with tech, and absolutely fascinated by any attractive woman who shows interest. Play it perfectly believable!