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wildlife2_client

Verified Safe

by readcommitted

Overview

Organizing, identifying, and exploring wildlife imagery using AI models, geospatial analysis, and semantic search.

Installation

Run Command
streamlit run main.py

Environment Variables

  • MEDIA_ROOT
  • PREDICTIONS_JSON
  • ENVIRONMENT
  • DEBUG
  • DATABASE_URL
  • DEFAULT_CONFIDENCE_THRESHOLD
  • WIKI_API_URL
  • USER_AGENT
  • OPENAI_API_KEY
  • EMBED_MODEL
  • GPTMODEL
  • SPACE_NAME
  • REGION
  • ACCESS_KEY
  • SECRET_KEY
  • WATCHER_IMAGES
  • WATCHER_WAIT
  • APP_MODE

Security Notes

The project uses environment variables or Streamlit secrets for sensitive API keys (e.g., OpenAI, DigitalOcean Spaces, Database), which is good practice. External tools (`exiftool`, `ogr2ogr`) are invoked via `subprocess.run`; while using lists for commands reduces shell injection risk, reliance on external input for file paths or connection strings still poses a potential risk if not thoroughly sanitized. `torch.load` is used for model loading, which can be vulnerable to arbitrary code execution if model files are untrusted; the `load_speciesnet` function offers an optional SHA256 checksum validation to mitigate this. `ast.literal_eval` is used for parsing data, which is generally safer than `eval()` but should still be used with caution for untrusted inputs. Overall, common modern application security practices are followed for secrets, but external dependencies introduce typical supply-chain and runtime execution risks that require careful management of trusted sources.

Stats

Interest Score30
Security Score7
Cost ClassMedium
Avg Tokens1500
Stars1
Forks0
Last Update2025-12-15

Tags

wildlifeimage processingcomputer visionsemantic searchAI agents