Social Media Manager for NASA's Twitter

Overview

This Social Media Manager is a conversational Streamlit application designed to analyze and derive insights from NASA's Twitter engagement data. Powered by LangChain, OpenAI's GPT-4, and a SQL database, it enables users to interactively query and explore tweets, calculate engagement metrics, and generate comparative insights across different post types (text, image, video, gif).

Try It Out

You can try out the Social Media Manager application live at the following hosted website: Live Demo

Features

• Chat-Based Interaction: A user-friendly interface to query the database using natural language.
• SQL Database Integration: Access and analyze NASA's tweets stored in an SQLite database.
• Engagement Metrics Calculation: Compute metrics such as likes, replies, retweets, and total engagement.
• Comparative Insights: Generate quantitative and qualitative insights to compare post performance.
• Memory-Driven Conversations: Retain chat history for continuous, context-aware interactions.

Table Structure

The SQL database (tweets.db) contains a single table nasa_tweets with the following columns:

Column	Type	Description
content	TEXT	The content of the tweet.
likeCount	INTEGER	Number of likes the tweet received.
quoteCount	INTEGER	Number of quotes the tweet received.
replyCount	INTEGER	Number of replies to the tweet.
retweetCount	INTEGER	Number of retweets the tweet received.
url	TEXT	URL to the tweet.
post_type	TEXT	Type of post (Text, Image, Video, Gif).
date	TEXT	Date the tweet was posted (dd/mm/yyyy).
time	TEXT	Time the tweet was posted (hh:mm:ss).

Getting Started

Prerequisites

• Python 3.8+
• SQLite database (tweets.db)
• OpenAI API Key

Installation

1. Clone the Repository: bash git clone https://github.com/your-repo/social-media-manager.git cd social-media-manager

2. Install Dependencies: bash pip install -r requirements.txt

3. Set Up Environment Variables: Create a .env file in the root directory: env OPENAI_API_KEY=your_openai_api_key

4. Prepare the Database: Ensure the tweets.db file is placed in the root directory. Verify the nasa_tweets table structure matches the schema described above.

5. Run the Application: bash streamlit run app.py

Usage

1. Open the application in your web browser (default: http://localhost:8501).
2. Interact with the chat interface to ask questions like:
   • "What is the average engagement for video posts?"
   • "Compare the total engagement for text and image posts."
   • "Which type of post has the highest number of likes on average?"
3. The agent will query the database and return insightful responses based on the data.

Example Queries

• Metrics Calculation:
  • "Calculate the total engagement for all video posts."
  • "What is the average number of retweets for text posts?"
• Insights Generation:
  • "Generate insights comparing engagement across all post types."
  • "Which post type performs better: images or videos?"

Project Structure

├── app.py             # Main Streamlit application
├── tweets.db          # SQLite database containing NASA's tweets
├── requirements.txt   # Python dependencies
├── .env               # Environment variables
└── README.md          # Project documentation

Acknowledgments

• LangChain for the conversational AI framework.
• Streamlit for building an interactive web app.