Social Network Analyzer

This repository contains a Flask-based web application that lets users:

1. Specify how many social network links they want to analyze.
2. Enter and validate each link (Twitter, Instagram, Facebook, Threads, etc.).
3. Provide a personal description about the person whose profiles are being analyzed.
4. Analyze all collected data (links + description) via the OpenAI API to generate a personality overview or short psychological summary.

IMPORTANT DISCLAIMER

• This is not a clinical or professional psychological tool.
• The output can contain errors or "hallucinations" typical of Large Language Models.
• Always comply with each social platform's terms of service regarding data collection/scraping.
• Handle personal data with caution and respect user privacy.

---

🚀 New Features: Platform-Specific API Integration

The application now includes advanced platform-specific API integration for reliable social media data fetching:

✅ Supported Platforms

• Twitter/X: Full API v2 and v1.1 support with rate limiting
• LinkedIn: Profile data extraction with authentication
• Instagram: Public and private profile access via instaloader
• Facebook: Graph API integration for profile data
• Reddit: User data and post history via PRAW
• Generic Platforms: Web scraping fallback for other platforms

🔧 Key Improvements

• Rate Limiting: Automatic API rate limit management
• Authentication: OAuth and API key support for each platform
• Fallback Mechanisms: Web scraping when APIs are unavailable
• Structured Data: Consistent data format across all platforms
• Error Handling: Comprehensive error handling and recovery

📊 Enhanced Data Extraction

• Profile information (name, bio, location, website)
• Follower/following counts
• Verification status
• Recent posts and activity
• Account creation dates
• Profile pictures

For detailed setup instructions, see API_INTEGRATION_GUIDE.md.

---

Features

• Multi-Step Flow

  • Step A: Select the number of social network links.
  • Step B: Enter each link, then validate it against known social platforms.
    • If all links are valid, you can continue. Otherwise, you must correct them.
  • Step C: Provide a personal description for additional context.
  • Analysis: The server combines the links + personal description into a prompt, sends it to OpenAI, and displays a final summary.

• Dynamic Frontend

  • Uses JavaScript (Fetch API) to handle link validation and multi-step UI.
  • Displays ✓ for valid links and ✗ for invalid links.

• OpenAI Integration

  • A sample prompt demonstrates how to create a short "personality analysis."
  • Requires an OpenAI API key (set in .env or as an environment variable).

• Platform-Specific API Integration

  • Reliable data fetching using official APIs where available
  • Automatic fallback to web scraping for unsupported platforms
  • Rate limiting and error handling for robust operation

---

Quickstart

1. Clone the Repository:

   git clone https://github.com/VictoKu1/Social-Network-Analyzer.git
   cd Social-Network-Analyzer
   

2. Install Dependencies:

   • Python 3.8+ recommended.
   • Install packages:

   pip install -r requirements.txt
   

3. Set Your API Keys:

   Create a .env file with your API credentials:

   # Required: OpenAI API
   OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
   
   # Optional: Platform-specific APIs (see config_example.py for details)
   TWITTER_BEARER_TOKEN=your_twitter_bearer_token
   LINKEDIN_EMAIL=your_linkedin_email
   LINKEDIN_PASSWORD=your_linkedin_password
   INSTAGRAM_USERNAME=your_instagram_username
   INSTAGRAM_PASSWORD=your_instagram_password
   FACEBOOK_ACCESS_TOKEN=your_facebook_access_token
   REDDIT_CLIENT_ID=your_reddit_client_id
   REDDIT_CLIENT_SECRET=your_reddit_client_secret

   Note: Only the OpenAI API key is required. Platform-specific APIs are optional and will fall back to web scraping if not provided.

4. Run the Flask App:

   python app.py
   

5. Open Your Browser:

Visit http://127.0.0.1:5000/ to access the web app.

6. Test the API Integration (Optional):

   python test_api_integration.py
   

---

Usage

1. Step A: Select the number of social profiles (e.g., 2).
2. Step B: Enter each URL in the generated fields (e.g., https://x.com/example), then click Validate Links.

• If a link is marked ✗, correct it.
• If all links are ✓, the Continue button appears.

3. Step C: Provide a personal description for context (e.g., "She is very outgoing and enjoys discussing tech trends.").
4. Click Analyze: The server sends the data to OpenAI and displays a final summary.

The application will now use platform-specific APIs when available, providing more reliable and comprehensive data extraction.

---

Project Structure

.
├── static/             
│   └── css/
│       └── style.css    # CSS for the multi-step form
├── js/                 
│   └── app.js          # JavaScript for the multi-step form
├── templates/
│   └── index.html      # Implements the multi-step form using JavaScript
├── app.py              # Main Flask app with routes
├── analyze.py          # OpenAI-related analysis logic (link validation, prompt construction)
├── social_media_fetchers.py  # NEW: Platform-specific API integration
├── test_analyze.py     # Unit tests for the analyze.py logic
├── test_api_integration.py   # NEW: API integration tests
├── config_example.py   # NEW: Example configuration file
├── API_INTEGRATION_GUIDE.md  # NEW: Comprehensive API setup guide
├── requirements.txt    # Python package dependencies
├── README.md           # This README file
├── LICENSE             # License (MIT)
└── .gitignore          # Prevents committing unwanted files

• app.py:

  • Defines the Flask routes:
    • GET / serves the main page (index.html)
    • POST /validate_links checks if each link is a recognized social network
    • POST /analyze calls the analysis function in analyze.py and returns the result

• analyze.py:

  • Contains the core OpenAI analysis logic:
    • validate_social_link(link) checks if a URL is recognized (Twitter, Instagram, etc.).
    • analyze_personality(links_info, personal_description) constructs a prompt and calls the OpenAI API.
    • Updated to use platform-specific fetchers for better data extraction.

• social_media_fetchers.py: NEW

  • Platform-specific API integration for Twitter, LinkedIn, Instagram, Facebook, and Reddit
  • Rate limiting and error handling
  • Fallback mechanisms for unsupported platforms
  • Structured data extraction and formatting

• test_analyze.py:

  • Contains unit tests for functions in analyze.py.
  • Uses Python's built-in unittest or can be adapted for pytest.

• test_api_integration.py: NEW

  • Tests for the platform-specific API integration
  • Demonstrates the difference between old and new fetching methods
  • Validates rate limiting and error handling

• config_example.py: NEW

  • Example configuration file showing all required API credentials
  • Detailed setup instructions for each platform
  • Security best practices

• API_INTEGRATION_GUIDE.md: NEW

  • Comprehensive guide for setting up platform-specific APIs
  • Troubleshooting and performance optimization tips
  • Security considerations and best practices

• Templates/index.html:

  • Implements the multi-step form using JavaScript.
  • Uses the Fetch API to call /validate_links and /analyze.

• requirements.txt:

  • Python package dependencies.
  • Updated to include platform-specific API libraries.

• .gitignore:

  • Hides temporary or sensitive files (e.g., venv/, .env, __pycache__, etc.) from version control.

---

Testing

To run the unit tests for analyze.py, use one of the following:

Using Python's built-in unittest

python -m unittest discover

or specifically:

python -m unittest test_analyze.py

Using pytest (if installed)

pytest

Testing API Integration (NEW)

python test_api_integration.py

Tests in test_analyze.py:

• Mock the OpenAI API to avoid real API calls.
• Verify that functions like validate_social_link and analyze_personality behave as expected.

Tests in test_api_integration.py:

• Compare old generic fetching with new platform-specific fetching
• Validate rate limiting and error handling
• Test platform detection and username extraction
• Verify fallback mechanisms work correctly

---

API Setup

For detailed instructions on setting up platform-specific APIs, see API_INTEGRATION_GUIDE.md.

Quick Setup Summary

1. Twitter/X: Get API keys from Twitter Developer Portal
2. LinkedIn: Use email/password (not recommended for production)
3. Instagram: Use username/password for private profiles
4. Facebook: Get access token from Facebook Developers
5. Reddit: Create app at Reddit App Preferences

Security Notes

• Never commit API keys to version control
• Use environment variables for sensitive data
• Regularly rotate your API keys
• Monitor API usage to avoid rate limits

---

Contributing

1. Fork this repo and clone your fork.
2. Create a new branch for your feature/bugfix:

git checkout -b feature/new-stuff

3. Commit and push your changes:

git commit -m "Add new feature"
git push origin feature/new-stuff

4. Open a Pull Request on GitHub.

---

License

Please see the LICENSE file for details.

Disclaimer

• This software is provided "as is," without any warranty or guarantee.
• Always comply with social media platforms' terms of service and API usage policies.
• Respect user privacy and data protection regulations.