📦 Module Structure: smlib (SampleSet Library)

🗂 Folder Overview

The smlib module consists of three main folders:

1️⃣ Instagram

Scripts related to Instagram data scraping:

• comments_section_scrapper.py: Scrapes comments from Instagram posts.
• post_scrapper.py: Scrapes post details like captions, media, and metadata.

2️⃣ Twitter

Scripts for Twitter automation:

• post_twit.py: Posts content directly to Twitter.

3️⃣ Ollama

Scripts for AI-generated content:

• caption_creater.py: Uses the Gemini model to generate a summary based on Instagram captions.

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🌐 Flask Web Application (app.py)

This module includes a Flask-based web app that acts as a control center:

• 📋 Data Table: Displays all Instagram post records.
• 📥 Fetch Posts Button: Scrapes Instagram posts and saves them into a MongoDB database.
• 📝 Summarize & Tweet Button: Generates a caption summary and tweets it instantly.

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🛠 Tech Stack

• Backend: Python, Flask
• Database: MongoDB
• Scraping: Custom-built scrapers for Instagram
• AI Integration: Gemini for caption summarization
• Social Media API: Twitter API

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🚀 How to Run

1. Clone the repository.
2. Ensure Docker is running and start services using:

   docker-compose up --build

3. Access the web app at http://localhost:5000.
4. Open Mongo Express at http://localhost:8081 to view stored data.

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📝 Future Enhancements

• Add user authentication and session management.
• Implement better error handling and logging.
• Improve UI with pagination and search features.
• Enhance AI capabilities with more models.

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