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| # FraudShield Submission Checklist | |
| Updated: 2026-04-25 | |
| ## Submission links | |
| - [x] Hugging Face Space: `https://huggingface.co/spaces/DevikaJ2005/fraudshield-1` | |
| - [x] GitHub repository: `https://github.com/DevikaJ2005/Fraudshield` | |
| - [x] 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 | |
| - [x] `openenv.yaml` present and aligned with the current environment | |
| - [x] OpenEnv API endpoints implemented: | |
| - `/health` | |
| - `/reset` | |
| - `/step` | |
| - `/state` | |
| - `/info` | |
| - `/tasks` | |
| - `/metadata` | |
| - `/schema` | |
| - `/mcp` | |
| - [x] Typed action, observation, reward, reset, step, and state models | |
| - [x] Frozen snapshot committed in `data/fraudshield_cases.json` | |
| - [x] Three graded tasks: easy, medium, hard | |
| - [x] Partial observability with progressive evidence reveals | |
| - [x] Investigation budget per case | |
| - [x] Workflow-shaped reward design | |
| - [x] Lightweight browser explorer at `/` for manual inspection | |
| ## Local validation status | |
| - [x] `python inference.py` | |
| - [x] `python validate_api.py` | |
| - [x] `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): | |
| - [x] Easy: `0.9900` | |
| - [x] Medium: `0.3500` | |
| - [x] Hard: `0.7425` | |
| - [x] Final: `0.6942` | |
| ## Training deliverables | |
| - [x] RL training notebook scaffold committed | |
| - [x] Notebook structured for Colab + GPU workflow | |
| - [x] 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 | |
| - [x] README rewritten around the RL environment story | |
| - [x] `HF_BLOG_DRAFT.md` present | |
| - [x] 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 | |