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https://github.com/victoku1/social-network-analyzer

Social Network Analyzer is a Flask-powered app that combines AI-driven insights with social media links (e.g., Twitter, Instagram). Validate profiles, add user context, and receive intelligent personality summaries via OpenAI’s cutting-edge models. Perfect for exploring the power of AI in an interactive way!
https://github.com/victoku1/social-network-analyzer

ai api-integration backend data-analysis flask frontend information-extraction oauth openai psycology python social-media social-network social-network-analysis web-scraping

Last synced: 27 days ago
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Social Network Analyzer is a Flask-powered app that combines AI-driven insights with social media links (e.g., Twitter, Instagram). Validate profiles, add user context, and receive intelligent personality summaries via OpenAI’s cutting-edge models. Perfect for exploring the power of AI in an interactive way!

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README 

          
# Social Network Analyzer



[![Pylint](https://github.com/VictoKu1/Social-Network-Analyzer/actions/workflows/pylint.yml/badge.svg)](https://github.com/VictoKu1/Social-Network-Analyzer/actions/workflows/pylint.yml)



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](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:



    ```env

    # 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](API_INTEGRATION_GUIDE.md).



### Quick Setup Summary



1. **Twitter/X**: Get API keys from [Twitter Developer Portal](https://developer.twitter.com/en/portal/dashboard)

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](https://developers.facebook.com/)

5. **Reddit**: Create app at [Reddit App Preferences](https://www.reddit.com/prefs/apps)



### 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](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.