autogluon.multimodal

v1.5.0 safe
2.0
Low Risk

Fast and Accurate ML in 3 Lines of Code

🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with minimal risks indicated across all categories. There are no significant signs of malicious intent or supply-chain attacks.

  • Low network, shell, obfuscation, credential, and metadata risks.
  • Common and legitimate patterns observed.
Per-check LLM notes
  • Network: The detected network pattern is likely for legitimate purposes such as downloading dependencies or resources during runtime.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed patterns suggest base64 decoding for image processing which is common in multimedia handling libraries and not indicative of malicious activity.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags were identified.

📦 Package Quality Overall: Medium (6.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://auto.gluon.ai
  • Detailed PyPI description (10024 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Contributing link: "Contribute!" -> https://github.com/autogluon/autogluon/blob/master/CONTRIBUT
  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 342 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in autogluon/autogluon
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • loading {fname}...") r = requests.get(url, timeout=(10, 1000)) with open(output_path, "wb") as
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • with PIL.Image.open(BytesIO(base64.b64decode(per_image))) as img: pass
  • lambda ele: [base64.b64decode(e) for e in ele] if isinstance(ele, list
  • st) else [base64.b64decode(ele)] ).tolist() elif col_type =
  • self.merged = False def eval(self): # def T(w): # return w.T if self.
  • _out else w nn.Linear.eval(self) if self.merge_weights and not self.merged:
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

Repository autogluon/autogluon appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AutoGluon Community" 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 autogluon.multimodal 
Create a simple yet powerful image captioning application using the 'autogluon.multimodal' Python package. This application will allow users to upload an image and receive a descriptive caption generated by the model. Here are the steps and features to include in your project:

1. **Setup**: Begin by installing the necessary packages including 'autogluon.multimodal'. Ensure you have a basic understanding of how this package simplifies multimodal machine learning tasks.
2. **Image Upload Interface**: Develop a user-friendly interface where users can upload their images. This could be a web-based interface using Flask or Django, or a command-line tool if simplicity is preferred.
3. **Model Integration**: Utilize 'autogluon.multimodal' to train or load a pre-trained model capable of generating captions from images. Explore the documentation to understand how to prepare data and integrate models effectively.
4. **Caption Generation**: Implement functionality within your application to process uploaded images through the model and generate captions. Display these captions back to the user in real-time.
5. **Evaluation & Feedback**: Allow users to provide feedback on the accuracy and relevance of the generated captions. Collect this data to improve the model over time.
6. **Documentation & Deployment**: Write clear documentation explaining how to use the application and deploy it either locally or on a cloud service like AWS or Google Cloud Platform.

This project not only showcases the power and simplicity of 'autogluon.multimodal', but also provides a practical application for users interested in AI-generated content.

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