Lark Bot Integration

Lark Bot Integration

Complete Lark (Feishu/飞书) bot system for interactive Milady meme generation and team collaboration.

Overview

Deploy a fully-featured Lark bot that:

• Handles 10+ commands for meme generation
• Processes @mentions and direct messages
• Uploads images directly to Lark chats
• Manages webhooks with automatic token refresh
• Integrates all Skills - Milady generator, AI effects, Twitter content

Quick Start

1. Configure Lark App

# Set environment variables
export LARK_APP_ID="cli_xxxxxxxxxxxxx"
export LARK_APP_SECRET="your_app_secret"

# Verify configuration
python scripts/​setup_webhook.py --verify

2. Start Webhook Server

# Start on port 8000
python webhook_server.py

# Or use startup script
./​start_lark_bot.sh

3. Set Webhook URL

# Get your public URL (use ngrok or public server)
python scripts/​setup_webhook.py --url https://your-domain.com/​webhook

4. Get Chat ID

# Get group chat ID for posting
python scripts/​get_chat_id.py

Supported Commands

Meme Generation Commands

AI Effects Commands

Twitter Content Commands

Social Monitoring Commands

Training Data Commands

Utility Commands

Command Examples

Basic Meme

/​milady 5050

Result: NFT #5050 as image

With Accessories

/​milady 5050 Hat:Beret.png Glasses:Sunglasses.png

Result: NFT #5050 + beret + sunglasses

With Text

/​milady 5050 top:"Good Morning" bottom:"Let's Go"

Result: NFT #5050 with text overlay

Natural Language

/​milady 5050 give her a cowboy hat and heart sunglasses

Result: Bot parses "cowboy hat" → Hat:Cowboy.png, "heart sunglasses" → Glasses:Heart Shaped.png

AI Effects

/​milady_illusion 5050 spiral pattern

Result: NFT #5050 with illusion diffusion effect ($0.006)

/​milady_replace_sam 5050 hat blue baseball cap

Result: SAM detects hat, FLUX replaces with blue baseball cap ($0.05)

Template Meme

/​milady_template drake

Result: Random Milady in Drake meme template

Architecture

Webhook Flow

User sends message in Lark
        ↓
Lark sends POST to /​webhook
        ↓
webhook_server.py receives event
        ↓
LarkMemeBot processes command
        ↓
Calls MemeGenerator/​AI Effects
        ↓
Uploads result to Lark
        ↓
User sees image in chat

File Structure

lark-bot-integration/
├── SKILL.md                    # This file
├── SETUP_GUIDE.md              # Detailed setup
├── COMMAND_REFERENCE.md        # All commands
├── DEPLOYMENT.md               # Production deployment
├── src/
│   ├── lark_meme_bot.py        # Main bot logic
│   ├── webhook_handler.py      # Webhook processing
│   └── lark_api.py             # Lark API wrapper
├── config/
│   └── lark_config.yaml        # Bot configuration
└── scripts/
    ├── setup_webhook.py        # Webhook setup tool
    ├── get_chat_id.py          # Get chat IDs
    └── approve.py              # Approval workflow

Setup Guide

Prerequisites

1. Lark Developer Account

   • Go to https://open.larksuite.com/
   • Create new app
   • Enable bot capability

2. Server with Public URL

   • Can use ngrok for testing
   • Production: VPS or cloud server

3. Required Permissions

   • Send messages
   • Receive messages
   • Upload files
   • Read group info

Detailed Setup Steps

See SETUP_GUIDE.md for step-by-step instructions including:

• Creating Lark app
• Configuring event subscriptions
• Setting up permissions
• Webhook URL verification
• Testing the bot

Using Ngrok (Development)

# Start ngrok tunnel
./​start_tunnel.sh

# Ngrok will output URL like: https://abc123.ngrok.io
# Use this as webhook URL: https://abc123.ngrok.io/​webhook

# Set webhook
python scripts/​setup_webhook.py --url https://abc123.ngrok.io/​webhook

Bot Implementation

Main Bot Class

from src.lark_meme_bot import LarkMemeBot

# Initialize bot
bot = LarkMemeBot(
    app_id=os.getenv("LARK_APP_ID"),
    app_secret=os.getenv("LARK_APP_SECRET")
)

# Process incoming message
bot.handle_message(
    message_id="om_xxxxx",
    chat_id="oc_xxxxx",
    text="/​milady 5050",
    user_id="ou_xxxxx"
)

Adding Custom Commands

# In src/​lark_meme_bot.py

def handle_custom_command(self, text, chat_id):
    """Add your custom command handler"""

    if text.startswith("/​mycommand"):
        # Your logic here
        result = do_something()

        # Send response
        self.send_message(chat_id, "Done!")

        # Upload image if needed
        if image_path:
            self.upload_image(chat_id, image_path)

Webhook Server

from flask import Flask, request
from src.lark_meme_bot import LarkMemeBot

app = Flask(__name__)
bot = LarkMemeBot()

@app.route('/​webhook', methods=['POST'])
def webhook():
    data = request.get_json()

    # Handle event
    if data.get('type') == 'message':
        bot.handle_message(
            message_id=data['message']['message_id'],
            chat_id=data['message']['chat_id'],
            text=data['message']['text']
        )

    return {'success': True}

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)

Message Formats

Text Messages

bot.send_message(chat_id, "Hello from AI Content Studio! 🎀")

Card Messages (Rich Format)

bot.send_card(
    chat_id=chat_id,
    title="Meme Generated!",
    content="Your Milady meme is ready",
    image_key=image_key
)

Image Upload

# Upload and send image
image_key = bot.upload_image(
    chat_id=chat_id,
    image_path="/​path/​to/​meme.png"
)

Error Handling

Common Issues

1. Webhook verification failed

# Check URL is publicly accessible
curl https://your-domain.com/​webhook

# Verify in Lark console:
# Settings > Event Subscriptions > Verify URL

2. Messages not received

# Check event subscriptions are enabled
# Required events:
# - im.message.receive_v1
# - im.message.mention_v1

3. Image upload fails

# Check image size (max 10MB for Lark)
# Check file format (PNG, JPG supported)
# Verify upload permission is granted

4. Token expired

# Token auto-refreshes every 2 hours
# Manual refresh:
bot.refresh_token()

Approval Workflow

For production use with Twitter posting:

# Start approval workflow
python scripts/​approve.py

# Bot generates content → sends to Lark
# Team reviews in Lark chat
# Approved tweets get posted to Twitter

Approval Flow

1. Bot generates tweet/​meme
2. Posts to Lark approval channel
3. Team reacts with ✅ (approve) or ❌ (reject)
4. If approved → Posts to Twitter
5. If rejected → Logs feedback for learning

Production Deployment

Using systemd (Linux)

# Create service file
sudo nano /​etc/​systemd/​system/​lark-bot.service

# Add:
[Unit]
Description=Lark Meme Bot
After=network.target

[Service]
Type=simple
User=your_user
WorkingDirectory=/​path/​to/​ai-content-studio
Environment="LARK_APP_ID=cli_xxxxx"
Environment="LARK_APP_SECRET=xxxxx"
ExecStart=/​usr/​bin/​python3 webhook_server.py
Restart=always

[Install]
WantedBy=multi-user.target

# Enable and start
sudo systemctl enable lark-bot
sudo systemctl start lark-bot

Using Docker

FROM python:3.9-slim

WORKDIR /​app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "webhook_server.py"]

# Build and run
docker build -t lark-bot .
docker run -d -p 8000:8000 \
  -e LARK_APP_ID=cli_xxxxx \
  -e LARK_APP_SECRET=xxxxx \
  lark-bot

Using nohup (Simple)

# Start in background
nohup python3 webhook_server.py > webhook.log 2>&1 &

# Check status
ps aux | grep webhook_server

# View logs
tail -f webhook.log

Monitoring

Check Bot Status

# View logs
tail -f webhook.log

# Check if running
ps aux | grep webhook_server

# Test webhook
curl -X POST https://your-domain.com/​webhook \
  -H "Content-Type: application/​json" \
  -d '{"type":"test"}'

Metrics to Track

• Messages received per hour
• Commands processed
• Images generated
• API errors
• Response time

Security

Best Practices

1. Environment Variables

   # Never commit credentials
   # Use .env file (add to .gitignore)
   LARK_APP_ID=cli_xxxxx
   LARK_APP_SECRET=xxxxx
   

2. Webhook Verification

   # Verify requests come from Lark
   def verify_webhook(request):
       # Check signature
       # Validate timestamp
       # Reject invalid requests
   

3. Rate Limiting

   # Prevent abuse
   from flask_limiter import Limiter
   
   limiter = Limiter(app, default_limits=["100 per hour"])
   

4. Access Control

   # Restrict to specific chats/​users
   ALLOWED_CHATS = ["oc_xxxxx", "oc_yyyyy"]
   
   if chat_id not in ALLOWED_CHATS:
       return "Unauthorized"
   

Advanced Features

Multi-Language Support

# Detect language and respond accordingly
if user_language == "zh-CN":
    response = "梗图生成中..."
else:
    response = "Generating meme..."

Command Shortcuts

# Define aliases
COMMAND_ALIASES = {
    "/​m": "/​milady",
    "/​mr": "/​milady_random",
    "/​mi": "/​milady_illusion"
}

Scheduled Posts

# Use APScheduler for timed posts
from apscheduler.schedulers.background import BackgroundScheduler

scheduler = BackgroundScheduler()
scheduler.add_job(
    func=bot.post_daily_gm,
    trigger="cron",
    hour=9,
    minute=0
)
scheduler.start()

Integration with Other Skills

With Milady Generator

from skills.milady_meme_generator.src.meme_generator_v2 import MemeGeneratorV2

generator = MemeGeneratorV2()
image = generator.generate_meme(nft_id=5050)
bot.upload_image(chat_id, image)

With AI Effects

from skills.ai_image_effects.src.flux_fill_pro import FluxFillPro

flux = FluxFillPro()
result = flux.replace_accessory(image, "hat", "cool beanie")
bot.upload_image(chat_id, result)

With Twitter Content

from skills.twitter_content_ai.src.content_generator import ContentGenerator

content = ContentGenerator()
tweet = content.generate_gm_post()
bot.send_message(chat_id, f"Suggested tweet:\n\n{tweet}")

Troubleshooting

See DEPLOYMENT.md for comprehensive troubleshooting guide.

Related Documentation

• SETUP_GUIDE.md - Step-by-step setup
• COMMAND_REFERENCE.md - All commands
• DEPLOYMENT.md - Production deployment
• Lark Open Platform Docs

Related Skills

• milady-meme-generator - Core meme generation
• ai-image-effects - AI effects integration
• twitter-content-ai - Content generation

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Cost: Free (Lark API is free, only AI effects have costs)