Update HF space sync URL to DeepParmar/code-review

.github/workflows/sync.yml

@@ -20,5 +20,5 @@ jobs:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
           # Push to Hugging Face Space
-          git push --force https://Ksiki:$HF_TOKEN@huggingface.co/spaces/Ksiki/code-test main
+          git push --force https://DeepParmar:$HF_TOKEN@huggingface.co/spaces/DeepParmar/code-review main
 

REQUIREMENTS_CHECKLIST.md

@@ -1,66 +0,0 @@
-# OpenEnv Submission — Requirements Checklist
-
-## ✅ Pre-Submission Gate (ALL MUST PASS)
-
-| # | Requirement | Status | Evidence |
-|---|-------------|:------:|----------|
-| 1 | HF Space deploys and responds to reset() | ✅ | https://deepparmar-code-review.hf.space returns 200 |
-| 2 | OpenEnv spec compliance (openenv.yaml, typed models, step/reset/state) | ✅ | openenv.yaml present, Pydantic models in models.py, endpoints in server.py |
-| 3 | Dockerfile builds | ✅ | `code-review-env/Dockerfile` — python:3.11-slim, uvicorn on port 7860 |
-| 4 | Baseline inference script reproduces scores | ✅ | `inference.py` in root of code-review-env, uses OpenAI client |
-| 5 | 3+ tasks with graders, scores in 0.0–1.0 | ✅ | easy/medium/hard with grader_easy/medium/hard.py, all scores clamped [0.001, 0.999] |
-
-## ✅ Mandatory Environment Variables
-
-| Variable | Status | Where Used |
-|----------|:------:|------------|
-| `API_BASE_URL` | ✅ | inference.py line 769 — `os.getenv("API_BASE_URL", ...)` |
-| `MODEL_NAME` | ✅ | inference.py line 770 — `os.getenv("MODEL_NAME", ...)` |
-| `HF_TOKEN` | ✅ | inference.py line 771 — `os.getenv("HF_TOKEN")` |
-
-## ✅ Mandatory Inference Script Requirements
-
-| Requirement | Status | Evidence |
-|-------------|:------:|----------|
-| Named `inference.py` in root directory | ✅ | `code-review-env/inference.py` |
-| Uses OpenAI Client for LLM calls | ✅ | `from openai import OpenAI` on line 20 |
-| Emits [START], [STEP], [END] stdout logs | ✅ | `_print_start()`, `_print_step()`, `_print_end()` functions |
-| Runtime < 20 minutes | ✅ | Full 3-task run completes in ~3-5 minutes |
-| Runs on vcpu=2, memory=8gb | ✅ | No GPU required, lightweight FastAPI server |
-
-## ✅ Functional Requirements
-
-| Requirement | Status | Evidence |
-|-------------|:------:|----------|
-| Real-world task simulation | ✅ | Code review — engineers do this daily |
-| Full OpenEnv interface (step/reset/state) | ✅ | server.py: POST /reset, POST /step, GET /state |
-| Typed Pydantic models | ✅ | CodeReviewAction, CodeReviewObservation in models.py |
-| 3 tasks (easy→medium→hard) | ✅ | task_easy.py (3 bugs), task_medium.py (4 bugs), task_hard.py (6 bugs + 1 trap) |
-| Programmatic graders (0.0–1.0) | ✅ | grader_easy/medium/hard.py → compute_weighted_f1 → [0.001, 0.999] |
-| Graders deterministic & reproducible | ✅ | No randomness in grading logic |
-| Meaningful reward (not just end-of-episode) | ✅ | Per-step rewards: +0.15 to +0.30 for TPs, -0.10 to -0.20 for FPs |
-| Penalizes undesirable behavior | ✅ | FP penalty, red herring -0.20, duplicate -0.05 |
-
-## ✅ Non-Functional Requirements
-
-| Requirement | Status | Evidence |
-|-------------|:------:|----------|
-| Deploys to HF Space (tagged openenv) | ✅ | Live at deepparmar-code-review.hf.space |
-| Working Dockerfile | ✅ | code-review-env/Dockerfile |
-| README with env description | ✅ | Updated README.md with motivation section |
-| README with action/observation spaces | ✅ | Full tables for both spaces |
-| README with task descriptions + difficulty | ✅ | 3-tier table with task details |
-| README with setup/usage instructions | ✅ | Docker, pip, inference commands |
-| README with baseline scores | ✅ | 5-model table with non-ceiling scores |
-
-## ✅ Scoring Criteria Coverage
-
-| Criterion | Weight | Our Coverage |
-|-----------|:------:|-------------|
-| Real-world utility | 30% | Code review is a genuine daily engineering task |
-| Task & grader quality | 25% | 3 tasks, difficulty progression, weighted F1 with 1-to-1 matching |
-| Environment design | 20% | Clean state, typed actions/obs, dense rewards, proper episode bounds |
-| Code quality & spec compliance | 15% | openenv.yaml, Dockerfile, typed models, 70 tests passing |
-| Creativity & novelty | 10% | Semantic "why" metric, red herring traps, adversarial injections, explanation tiers |
-
-## Summary: ALL REQUIREMENTS SATISFIED ✅

final_checklist.md

@@ -1,55 +0,0 @@
-# Code Review OpenEnv - Senior Reviewer Final Checklist
-
-## Overview
-This document serves as the comprehensive final audit report for the Code Review OpenEnv submission. It outlines the exhaustive checks performed across codebase integrity, adversarial security, remote deployment (Hugging Face), and Large Language Model (OpenRouter) integration.
-
-## 1. Codebase & Logic Audit
-As a Senior Reviewer, I conducted a deep dive into the environment's implementation:
-- **Environment Logic Tested (`environment.py`)**: Confirmed `CodeReviewEnv` accurately handles action parsing, terminal states, and step limits.
-- **Grader Mathematical Stability (`graders/base_grader.py`)**: Verified that ceilings operate correctly. All intermediate F1 scores and final rewards are rigorously clamped between `0.01` and `0.999`. The "Done" operation correctly terminates the episode without decoupling or falsely inflating the raw F1 score.
-- **Adversarial Resilience (`tasks/task_hard.py`)**: Confirmed the "Red Herring" trap exists and accurately applies a `-0.20` catastrophic penalty for models that erroneously flag it.
-- **Calibration Subsystem (`reward_engine.py`)**: Verified that the high-confidence calibration telemetry functions silently. Correct high-confidence bugs yield a `+0.05` bonus, while incorrect high-confidence flags are punished with `-0.10`. Models are scored on self-awareness.
-
-## 2. Testing Suite Validation
-- **Local Pytest (Pass Rate: 100%)**: Validated all 118 baseline and advanced tests.
-- **Extreme Constraints (`test_extreme_final.py`)**: Executed the rigorous 48-test suite testing multi-file constraints, logic handling, math clamping, load resistance, and cross-file capability (`CF-01` through `CF-08`, `ATK-01` through `ATK-15`). **Result: 48/48 Passing.**
-
-## 3. Remote Infrastructure (Hugging Face)
-- **Deployment Status**: Confirmed the GitHub repository synchronizes seamlessly to the `Ksiki/code-test` Hugging Face Space.
-- **Health Checks & Uptime**: Executed direct HTTP checks against the live environment. The `/health`, `/reset`, `/state`, and `/step` endpoints respond identically to the local container.
-- **Security Check**: Verified no API keys (`sk-or-*`, `sk_live_*`) or Hugging Face Tokens are hardcoded into standard tracking files, `Dockerfile`, or inference wrappers. The implementation uses secure environment variables (`HF_TOKEN`, `API_BASE_URL`).
-
-## 4. Multi-Model Benchmark Verification (OpenRouter)
-As per the mandatory requirements, five frontier models were tested directly against the Live Hugging Face Space to evaluate the environment's discriminative power under real-world LLM inference latency.
-
-**Tested Models & Baseline Scores:**
-| Model | Easy | Medium | Hard | Avg | Verdict |
-|-------|------|--------|------|-----|---------|
-| **DeepSeek-Chat** | 0.999 | 0.667 | 0.800 | **0.822** | Surgically precise, perfectly calibrated |
-| **Qwen-2.5-72B** | 0.727 | 0.824 | 0.500 | **0.684** | Solid answers, small hallucination rate |
-| **GPT-4o-Mini** | 0.999 | 0.588 | 0.323 | **0.637** | Crumbles on hard tasks |
-| **Llama-3.3-70B** | 0.556 | 0.625 | 0.375 | **0.519** | Dangerously overconfident |
-| **Mistral-Small** | 0.308 | 0.333 | 0.295 | **0.312** | Hit 34k token limit and crashed safely |
-
-**Benchmark Outcome:**
-- The sequential script reliably triggered inferences communicating between OpenRouter LLMs and the Hugging Face Code Review environment.
-- The metrics accurately penalize overconfident hallucinations and reward surgically precise multi-file traversing capabilities.
-- All OpenRouter benchmark logs have been explicitly piped to `final test-2last.txt`.
-
-## 5. Final Checklist Sign-Off
-
-| Item | Description | Status |
-|------|-------------|--------|
-| **C1** | All OpenEnv tasks (`easy`, `medium`, `hard`) load properly | ✅ PASS |
-| **C2** | Score clamping strictly prevents 1.0 gamification | ✅ PASS |
-| **C3** | Pytest executes flawlessly without warnings (118/118) | ✅ PASS |
-| **C4** | Hugging Face space `Ksiki/code-test` is online and synced | ✅ PASS |
-| **C5** | Inference scripts support OpenRouter override keys securely | ✅ PASS |
-| **C6** | 5-Model Benchmark completed via Live endpoints | ✅ PASS |
-| **C7** | Benchmark logs exported accurately to `final test-2last.txt` | ✅ PASS |
-| **C8** | Repository devoid of unmasked secrets / `__pycache__` | ✅ PASS |
-
-## Final Verdict
-Everything has been meticulously checked. The environment provides a stable, deterministic, and highly discriminative testing ground for Code Review Agents. **No missing components, broken pipelines, or unmasked secrets remain.**
-
-Ready for submission.