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Smartsocial

@soufianesejjarion a year ago
1 MIT
FreeCommunity
AI Systems
#agentai#llm#mcp#python
SmartSocial is a comprehensive social media management platform designed to help businesses and individuals streamline their social media presence. It provides tools for content strategy creation, post scheduling, comment monitoring, sentiment analysis, and automated replies, all powered by AI-driven insights.

Overview

What is Smartsocial

smartSocial is a comprehensive social media management platform that helps businesses and individuals streamline their social media presence through AI-driven tools for content strategy, post scheduling, comment monitoring, sentiment analysis, and automated replies.

Use cases

Use cases for smartSocial include managing social media campaigns, analyzing audience sentiment, scheduling promotional posts, generating content ideas, and providing timely responses to customer inquiries.

How to use

Users can utilize smartSocial by creating an account, configuring their social media profiles, and accessing features like content creation, post scheduling, and sentiment analysis through an intuitive dashboard.

Key features

Key features of smartSocial include a user-friendly dashboard, sentiment analysis for comments, content generation using LLMs, post scheduling, automated replies to comments, and profile management for audience targeting.

Where to use

smartSocial can be used in various fields including marketing, public relations, customer service, and any business or individual looking to enhance their social media engagement and presence.

Content

SmartSocial Manager

SmartSocial Manager is a social media management application powered by LLMs (Large Language Models) and Facebook Graph API. It provides features like sentiment analysis, content creation, post scheduling, and automated comment replies.

Features

  • Dashboard: View recent posts, comments, and sentiment analysis.
  • Sentiment Analysis: Analyze the sentiment of comments (positive, negative, neutral).
  • Content Creation: Generate content suggestions using LLMs.
  • Post Scheduling: Schedule posts for future publication.
  • Automated Replies: Generate and send professional replies to comments.
  • Profile Management: Configure page profiles, target audience, and response templates.

Project Structure

├── app/
│   ├── __init__.py         # Application factory
│   ├── config.py           # Configuration using Pydantic
│   ├── models.py           # Database models
│   ├── services/           # Service layer for business logic
│   │   ├── __init__.py
│   │   ├── facebook_service.py  # Facebook Graph API integration
│   │   ├── llm_service.py       # LLM integration (Groq)
│   │   ├── auth_service.py      # Authentication logic
│   │   ├── dashboard_service.py # Dashboard logic
│   │   └── scheduling_service.py # Post scheduling logic
│   ├── blueprints/         # Flask blueprints for routes
│   │   ├── __init__.py
│   │   ├── auth.py             # Authentication routes
│   │   ├── dashboard.py        # Dashboard routes
│   │   ├── content.py          # Content creation routes
│   │   ├── comments.py         # Comment management routes
│   │   └── settings.py         # Profile settings routes
│   ├── templates/          # HTML templates
│   │   ├── base.html           # Base layout
│   │   ├── auth/               # Login and register templates
│   │   ├── dashboard/          # Dashboard templates
│   │   ├── content/            # Content creation templates
│   │   ├── comments/           # Comment management templates
│   │   └── settings/           # Profile settings templates
│   ├── static/             # Static files (CSS, JS)
│   │   └── css/
│   │       └── style.css       # Custom styles
├── tests/                  # Unit tests (not implemented)
├── .env                    # Environment variables
├── requirements.txt        # Python dependencies
├── run.py                  # CLI for running the application
└── README.md               # Project documentation

Installation

Prerequisites

  • Python 3.9 or higher
  • MongoDB installed and running locally
  • Facebook Page Access Token with required permissions
  • Groq API Key

Steps

  1. Clone the repository:

    git clone https://github.com/your-repo/smartsocial.git
    cd smartsocial
    
  2. Create and activate a virtual environment:

    python -m venv venv
    source venv/bin/activate  # On Windows: venv\Scripts\activate
    
  3. Install dependencies:

    pip install -r requirements.txt
    
  4. Set up the .env file:

    FACEBOOK_PAGE_ACCESS_TOKEN=your_facebook_page_access_token
    FACEBOOK_PAGE_ID=your_facebook_page_id
    GROQ_API_KEY=your_groq_api_key
    MONGODB_URI=mongodb://localhost:27017/smartsocial
    SECRET_KEY=your_secret_key
    
  5. Initialize the database:

    python run.py init-db
    
  6. Create an admin user:

    python run.py create-admin
    
  7. Run the application:

    python run.py run
    

Usage

CLI Commands

  • Run the application:

    python run.py run
    
  • Initialize the database:

    python run.py init-db
    
  • Create an admin user:

    python run.py create-admin
    
  • Perform a health check:

    python run.py health-check
    

Features in Detail

Dashboard

  • View recent posts and comments.
  • Analyze sentiment of comments (positive, negative, neutral).
  • View engagement metrics like total comments and average comments per post.

Content Creation

  • Create new posts and schedule them for future publication.
  • Generate content suggestions using LLMs.

Comment Management

  • Monitor comments on posts.
  • Automatically reply to comments using AI-generated responses.
  • Filter and view negative comments.

Profile Settings

  • Configure page profile details like name, category, and target audience.
  • Manage response templates for automated replies.

Technologies Used

  • Backend: Flask
  • Database: MongoDB
  • LLM Integration: Groq (via langchain_groq)
  • Frontend: Bootstrap 5
  • APIs: Facebook Graph API

Development

Running Tests

(Tests are not implemented yet. Add unit tests in the tests/ directory.)

Logging

Logs are stored in the logs/ directory. You can view logs for debugging:

tail -f logs/smartsocial.log

Contributing

  1. Fork the repository.
  2. Create a new branch:
    git checkout -b feature-name
    
  3. Commit your changes:
    git commit -m "Add feature-name"
    
  4. Push to the branch:
    git push origin feature-name
    
  5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.


Contact

For questions or support, please contact:

Tools

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