# 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