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.

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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).

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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 OpenAI API Key:

   You can either set it as an environment variable:

   • Linux:

     export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxx"
     

   • Windows:

     set OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
     

   Or create a .env file (not tracked in version control) and store your API key there:

    ```
    OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxx
    ```
   

4. Run the Flask App:

   python app.py
   

5. Open Your Browser:

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

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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.

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Project Structure

.
├── static/             
│   └── css/
│       └── stle.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)
├── test_analyze.py     # Unit tests for the analyze.py logic
├── 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.

• test_analyze.py:

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

• 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.

• .gitignore:

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

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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

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.

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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.

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License

Please see the LICENSE file for details.

Disclaimer

• This software is provided “as is,” without any warranty or guarantee.
• The output from any language model can be inaccurate or biased.
• Please use responsibly and always comply with the relevant platform policies and laws.
• Do not use this software for any high-stakes decisions.
• Always respect user privacy and data protection laws.
• If you have any concerns, please contact the author.
• The author is not responsible for any misuse or damage caused by this software.