# TorchReview Copilot Demo Script

## 60-90 Second Walkthrough

1. Open the Hugging Face Space and introduce TorchReview Copilot as an AI-powered code review and improvement system built with PyTorch.
2. Point to the problem statement: manual code review is slow, inconsistent, and hard to scale.
3. Select the `Fix the invoice total syntax regression` example to show the app loading a broken code sample together with the context window.
4. Highlight the **Live Triage Radar**, the ML quality score, and the RL-ready reward score.
5. Explain that the PyTorch layer uses CodeBERTa embeddings to compare the input against known code-quality patterns from the OpenEnv task catalog.
6. Scroll to the three-step improvement plan and call out the progression: syntax and bug fixes, edge cases, then scalability.
7. Switch to the performance example to show the confidence profile and reward changing for a different class of issue.
8. Close by noting that OpenEnv still powers deterministic validation under the hood, so the demo remains grounded in measurable task outcomes.