🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its suspicious commit patterns and incomplete author information, despite having low risks in network, shell execution, obfuscation, and credential handling.

Suspicious commit patterns
Incomplete author information

Per-check LLM notes

Network: The network calls appear to be downloading resources required for the package's functionality.
Shell: The shell execution is likely related to video processing using ffmpeg, which seems integral to the package's intended use.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has suspicious commit patterns and an author with incomplete information, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

...") try: resp = requests.get(FRAMES2VIDEO_URL, timeout=30) resp.raise_for_status(
...") try: resp = requests.get(REPO_ZIP_URL, timeout=120, stream=True) resp.raise_f

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

th custom output filename subprocess.run([ 'ffmpeg\\bin\\ffmpeg.exe', '-y', '-framerat
], cwd='postprocessing', shell=True, check=True) print('Video creation completed succe

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: All 19 commits happened within 24 hours

All 19 commits happened within 24 hours

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with AnimatedWordCloud

Develop a social media sentiment analysis tool using the AnimatedWordCloud Python package. This tool will allow users to input a hashtag from popular social media platforms like Twitter or Instagram, analyze the sentiment of the recent posts associated with that hashtag, and visualize the changing sentiment over time through animated word clouds. Here’s a step-by-step guide on how to build this tool:

1. **Project Setup**: Start by setting up a virtual environment for your project. Install necessary packages including AnimatedWordCloud, tweepy (for fetching tweets), nltk (for sentiment analysis), and matplotlib (for visualization).
2. **Data Collection**: Use the Tweepy library to fetch recent tweets containing the specified hashtag. Ensure you handle API rate limits and authentication properly.
3. **Sentiment Analysis**: Utilize NLTK’s pre-trained models to perform sentiment analysis on each tweet. Classify tweets as positive, negative, or neutral based on their content.
4. **Text Processing**: Preprocess the text data by removing stop words, punctuation, and other non-alphanumeric characters. Tokenize the tweets to prepare them for word cloud generation.
5. **Generating Word Clouds**: For each time interval (e.g., every hour or day), generate a word cloud using the AnimatedWordCloud package. Customize the appearance of these word clouds based on the overall sentiment of the tweets during that interval (e.g., color schemes that reflect positivity or negativity).
6. **Creating Animation**: Combine the series of word clouds into an animation that reflects changes in sentiment over time. The animation should smoothly transition between different states, highlighting shifts in public opinion.
7. **User Interface**: Develop a simple web interface where users can enter a hashtag and view the resulting sentiment analysis animation. Consider using Flask or Django to create this interface.
8. **Deployment**: Once developed, deploy your application to a platform like Heroku or AWS so it can be accessed by others.
9. **Testing & Feedback**: Test the application thoroughly, ensuring it handles various hashtags correctly and efficiently. Gather feedback from initial users to identify areas for improvement.

Suggested Features:
- Ability to switch between different sentiment classification models.
- Option to filter out retweets and replies.
- Integration with multiple social media platforms beyond just Twitter.
- Exporting animations as GIF files for sharing or offline viewing.
- Real-time updates or scheduled refreshes for ongoing monitoring of trending topics.