README.md

metadata

title: TorchReview Copilot
colorFrom: yellow
colorTo: red
sdk: docker
pinned: false
app_port: 8000
tags:
  - pytorch
  - gradio
  - fastapi
  - openenv
  - code-review
base_path: /web

TorchReview Copilot

TorchReview Copilot is an AI-powered code review and improvement system using PyTorch to analyze Python code, predict quality, generate structured improvement suggestions, and compute an RL-ready reward score.

It upgrades the original OpenEnv hackathon environment into a judge-friendly product demo: a polished Hugging Face Space on top, with the deterministic OpenEnv validation engine still preserved underneath.

Live demo: https://huggingface.co/spaces/uvpatel7271/final-python-env
Repository: https://github.com/uvpatel/final-python-env

Problem Statement

Engineering teams lose time during incident response and code review because broken Python snippets often arrive with noisy traces, partial test output, and unclear ownership. Before fixing anything, someone still has to answer:

• Is this a syntax issue, a logic bug, or a performance regression?
• How risky is the repair?
• What should be checked first?

That triage step is repetitive, error-prone, and often slows down the actual fix.

Solution

TorchReview Copilot turns code, traceback text, and a short context window into a practical code-review report:

• Issue classification: syntax, logic, or performance
• ML quality score: predicted code quality from PyTorch embeddings
• Reward score: RL-ready score from model quality, lint quality, and complexity penalty
• Live Triage Radar: confidence visualization for all issue classes
• Nearest known pattern: the closest OpenEnv task match
• Improvement plan: step 1 syntax/bug fixes, step 2 edge cases, step 3 scalability

Why PyTorch Matters

This project uses PyTorch for real inference, not placeholder branching:

• transformers + torch load huggingface/CodeBERTa-small-v1
• embeddings compare code with OpenEnv issue prototypes
• combines ML + static analysis signals

How It Works

Input → static checks → PyTorch embeddings → prediction → suggestions → reward

Reward Formula

reward = (0.5 x ML_quality_score) + (0.3 x lint_score) - (0.2 x complexity_penalty)