Project Structure

Here’s how the directory structure would typically look:

social-media-bot/
│
├── bot/
│     ├── Dockerfile
│     ├── bot.py
│     ├── requirements.txt
│     └── config/
│          ├── account1.json
│          └── account2.json
│
├── dashboard/
│     ├── Dockerfile
│     ├── app.py
│     ├── requirements.txt
│     └── static/
│          ├── css/
│          └── js/
│
├── database/
│     ├── db_setup.sql
│
├── docker-compose.yml
└── README.md

Step 1: Docker and Bot Setup

Dockerfile for the Bot

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY bot.py .
COPY config/ ./config/

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

Bot Code (bot.py)

# bot/bot.py

import json
import os
import logging
from googleapiclient.discovery import build
from some_social_media_api import SocialMediaAPI  # Placeholder for actual API library

# Load configuration
def load_config(account_name):
    with open(f'config/{account_name}.json') as config_file:
        return json.load(config_file)

logger = logging.getLogger()
logging.basicConfig(level=logging.INFO)

def main():
    account_name = os.getenv("ACCOUNT_NAME", "default")
    config = load_config(account_name)
    
    # Initialize Google Docs API
    service = build('docs', 'v1', credentials='your_credentials_here')
    doc_id = config['google_doc_id']
    
    # Fetch posts and schedule
    # Logic for reading Google Doc and scheduling posts would be here
    # For each post, call the social media API to post the content
    api = SocialMediaAPI(config['social_media_api_key'])
    
    # Your main bot loop or scheduling logic goes here

if __name__ == '__main__':
    main()

Sample requirements.txt for Bot

google-api-python-client
some_social_media_api_library

Sample Configuration File (account1.json)

{
    "google_doc_id": "your_google_doc_id",
    "social_media_api_key": "your_social_media_api_key",
    "posting_schedule": {
        "frequency": "daily",
        "time": "10:00"
    },
    "content_retrieval_rules": {
        "sections": ["posts", "stories"]
    }
}

Step 2: Dashboard Setup

Dockerfile for Dashboard

# dashboard/Dockerfile

FROM flask

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .
COPY static/ ./static/

CMD ["flask", "run", "--host=0.0.0.0"]

Dashboard Code (app.py)

# dashboard/app.py

from flask import Flask, request, jsonify
from docker import Docker

app = Flask(__name__)
docker_client = Docker()

    # Add logic to create Docker container for new social media account
    return jsonify({"status": "Account added"}), 201

    # Check status of specific Docker container
    return jsonify({"status": "Container running"}), 200

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0')

Sample requirements.txt for Dashboard

Flask
docker

Step 3: Docker Compose Configuration

# docker-compose.yml

version: '3.8'

services:
  bot1:
    build: ./bot
    environment:
      - ACCOUNT_NAME=account1
    volumes:
      - ./bot/config:/app/config
    depends_on:
      - database

  bot2:
    build: ./bot
    environment:
      - ACCOUNT_NAME=account2
    volumes:
      - ./bot/config:/app/config
    depends_on:
      - database

  dashboard:
    build: ./dashboard
    ports:
      - "5000:5000"
      
  database:
    image: postgres:alpine
    environment:
      POSTGRES_DB: "social_media"
      POSTGRES_USER: "username"
      POSTGRES_PASSWORD: "password"

Step 4: README Instructions

Your README.md will include the following:

# Social Media Management Bot

## Testing the Bot

- Ensure you have your social media platform set up for testing with sandbox credentials.
- For testing, you can create a sample Google Doc with sections titled "posts" and "stories" to retrieve content.

## JSON Configuration Example
Refer to `bot/config/account1.json` for a sample format.

Additional Recommendations

• Scalability: Use Docker Swarm or Kubernetes for orchestration when scaling beyond a few containers.
• Logging: Implement logging to a centralized location (like ELK stack) for better monitoring.
• Rate Limiting: Incorporate better handling for API rate limits to avoid getting blocked.
• Security: Consider using a secrets management system like Vault for sensitive data rather than environment variables.

This structure provides a comprehensive overview to get you started with the social media management bot using Docker. These files and configurations are basic examples; you can enhance them according to your specific needs and the capabilities of the APIs you integrate with. Always ensure to adhere to best practices and the social media platforms' usage policies when building and deploying your bot.