SUBMISSION_CHECKLIST.md

FraudShield Submission Checklist

Updated: 2026-04-25

Submission links

• Hugging Face Space: https://huggingface.co/spaces/DevikaJ2005/fraudshield-1
• GitHub repository: https://github.com/DevikaJ2005/Fraudshield
• Colab notebook path committed: notebooks/fraudshield_trl_colab.ipynb
• Public Colab run verified end to end after GPU training
• Final Hugging Face blog post URL or video/slides URL added to README.md

OpenEnv environment requirements

• openenv.yaml present and aligned with the current environment
• OpenEnv API endpoints implemented:
  • /health
  • /reset
  • /step
  • /state
  • /info
  • /tasks
  • /metadata
  • /schema
  • /mcp
• Typed action, observation, reward, reset, step, and state models
• Frozen snapshot committed in data/fraudshield_cases.json
• Three graded tasks: easy, medium, hard
• Partial observability with progressive evidence reveals
• Investigation budget per case
• Workflow-shaped reward design
• Lightweight browser explorer at / for manual inspection

Local validation status

• python inference.py
• python validate_api.py
• python -m openenv.cli validate .
• python -m openenv.cli validate --url http://127.0.0.1:7860
  • local browser health checks passed, but the validator still timed out against the subprocess-hosted server on this machine
• docker build -t fraudshield .
  • blocked by the local Docker Desktop / WSL engine state, not by the project code

Current baseline

Heuristic baseline (rule-based, not trained):

• Easy: 0.9900
• Medium: 0.3500
• Hard: 0.7425
• Final: 0.6942

Training deliverables

• RL training notebook scaffold committed
• Notebook structured for Colab + GPU workflow
• Notebook supports LOCAL_MODEL_PATH evaluation after training
• Real Colab training run completed
• reward_curve.png committed
• loss_curve.png committed
• training_summary.json updated with trained results
• README results section updated with trained-vs-heuristic comparison

Presentation deliverables

• README rewritten around the RL environment story
• HF_BLOG_DRAFT.md present
• Deployment guide present
• Final blog published on Hugging Face
• Final screenshots captured from the explorer UI and/or API walkthrough

What is still needed from you

• Run the Colab notebook with a GPU runtime
• Add your Hugging Face token inside Colab so trained artifacts can be saved if you want
• Publish the final blog or video/slides link
• Share Space build logs with me if the HF deployment needs one more cleanup pass