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README.md

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-# SQL Debug Environment (`sql-debug-env`)
-
-![Python](https://img.shields.io/badge/Python-3.11+-3776AB?logo=python&logoColor=white)
-![FastAPI](https://img.shields.io/badge/FastAPI-0.115-009688?logo=fastapi&logoColor=white)
-![Pydantic](https://img.shields.io/badge/Pydantic-v2-E92063?logo=pydantic&logoColor=white)
-![SQLite](https://img.shields.io/badge/SQLite-In_Memory-003B57?logo=sqlite&logoColor=white)
-![Docker](https://img.shields.io/badge/Docker-Ready-2496ED?logo=docker&logoColor=white)
-![OpenEnv](https://img.shields.io/badge/OpenEnv-Validated-2ea44f)
-
-An OpenEnv environment for a real task people do every day: **debugging SQL**. The agent gets a broken query, a live (in-memory) SQLite database, and a description of the expected output. It can inspect schema/errors/samples and submit fixed queries until it solves the task.
-
-## What’s in this repo
-- **FastAPI server**: `server/main.py` (endpoints: `/health`, `/tasks`, `/reset`, `/step`, `/state`)
-- **Environment logic**: `server/env.py` + `server/database.py`
-- **Tasks**: `server/tasks/` (easy → medium → hard, deterministic seed data)
-- **Baseline agent**: `inference.py` (OpenAI client + `[START]/[STEP]/[END]` logs)
-
-## Tech Stack
-- Python 3.11+
-- FastAPI + Uvicorn
-- Pydantic v2
-- SQLite (in-memory)
-- OpenEnv Core
-- Docker
-- OpenAI Python SDK (baseline inference)
-
-## Production Notes
-- Stateless HTTP API with per-session environment instances keyed by `X-Session-Id`
-- Deterministic task data (in-memory SQLite) for reproducible grading
-- Reward clamped to `[0.0, 1.0]` with partial-progress shaping
-- Docker-first deployment path (local and Hugging Face Spaces)
-- Local benchmark endpoint for live latency checks (`/benchmark`)
-
-## API Docs (FastAPI Auto Docs)
-Use these for interactive testing in browser:
-
-- Swagger UI: `http://localhost:7860/docs`
-- ReDoc: `http://localhost:7860/redoc`
-- OpenAPI spec: `http://localhost:7860/openapi.json`
-
-## Action Space
-| Action | Required fields | Cost / reward effect |
-|---|---|---|
-| `submit_query` | `query` | Main evaluation step (dense reward based on grading) |
-| `inspect_schema` | none | Free information action (small positive reward component) |
-| `inspect_error` | none | Free information action (small positive reward component) |
-| `inspect_sample` | `table_name` | Free information action (small positive reward component) |
-| `reset_query` | none | Penalty action (reduces reward for that step) |
-
-## Observation Space
-| Field | Type |
-|---|---|
-| `task_id` | `string` |
-| `task_description` | `string` |
-| `original_query` | `string` |
-| `current_query` | `string_or_null` |
-| `expected_description` | `string` |
-| `last_action_type` | `string` |
-| `last_query_result` | `object_or_null` |
-| `steps_taken` | `integer` |
-| `steps_remaining` | `integer` |
-| `current_score` | `float` |
-| `schema_info` | `object_or_null` |
-| `error_details` | `string_or_null` |
-| `sample_rows` | `array_or_null` |
-| `hint` | `string_or_null` |
-| `is_done` | `boolean` |
-| `success` | `boolean` |
-
-## Reward Function
-| Component | Range | Description |
-|---|---|---|
-| `correctness` | `[0.0, 0.6]` | Row-level match vs expected output |
-| `efficiency` | `[0.0, 0.2]` | Bonus for solving with fewer steps |
-| `syntax_progress` | `[0.0, 0.1]` | Small reward for producing syntactically valid SQL |
-| `schema_bonus` | `[0.0, 0.1]` | Bonus for referencing correct tables/columns |
-| `penalty` | `[0.0, 0.2]` | Deduction magnitude for resets/regressions/urgency near step limit |
-
-## Tasks
-### Task 1: Easy — Syntax Error Fix (`easy_syntax_fix`)
-Two straightforward issues: a misspelled keyword (`GRUP BY`) and an `ORDER BY` alias mismatch.
-
-### Task 2: Medium — Logic Error Fix (`medium_logic_fix`)
-Logic bugs around outer joins + filtering scope + aggregation scope.
-
-### Task 3: Hard — Multi-Bug Fix (`hard_multi_bug`)
-Five bugs across correlated subqueries, window functions, CTE scope, date logic, and duplication.
-
-## Baseline
-The baseline script is intentionally simple: it loops `reset → step` and asks an OpenAI model to choose the next JSON action.
-
-## Reliability & Benchmarking
-
-### Verified status (local)
-- `openenv validate --verbose`: **PASS**
-- `python3 -m unittest discover -s tests -p "test_*.py"`: **10/10 PASS**
-- Docker smoke test: **PASS** (`/health`, `/tasks`, `/reset`, `/step`)
-- FastAPI docs available: **PASS** (`/docs`, `/redoc`, `/openapi.json`)
-
-### Endpoint benchmark (local Docker run, n=25)
-Measured with `scripts/benchmark_local.py` on a running local container:
-
-| Endpoint | avg | p50 | p95 |
-|---|---:|---:|---:|
-| `GET /health` | 0.69 ms | 0.67 ms | 0.76 ms |
-| `GET /tasks` | 0.82 ms | 0.81 ms | 0.90 ms |
-| `POST /reset` | 1.34 ms | 1.26 ms | 1.62 ms |
-| `POST /step` (`inspect_schema`) | 1.07 ms | 1.01 ms | 1.34 ms |
-
-Re-run anytime:
-
-```bash
-python3 scripts/benchmark_local.py
-```
-
-Notes:
-- These are local-machine numbers (single container, warm runtime).
-- For submission-grade reporting, also capture one run against your HF Space URL after deploy.
-
-## Setup & Usage
-
-### Local Development
-```bash
-pip install -r requirements.txt
-uvicorn server.main:app --host 0.0.0.0 --port 7860
-```
-
-### Docker
-```bash
-docker build -t sql-debug-env .
-docker run -p 7860:7860 sql-debug-env
-```
-
-### Quick smoke test
-```bash
-curl http://localhost:7860/health
-curl http://localhost:7860/tasks
-curl -X POST http://localhost:7860/reset -H "Content-Type: application/json" -d '{"task_id":"easy_syntax_fix"}'
-curl -X POST http://localhost:7860/step  -H "Content-Type: application/json" -d '{"action":{"action_type":"inspect_schema"}}'
-curl "http://localhost:7860/benchmark?runs=20"
-```
-
-### Real-time benchmark API (for dashboards/web pages)
-This is a live endpoint, not static/dummy data. Every request runs fresh measurements.
-
-- Endpoint: `GET /benchmark?runs=20`
-- `runs` range: `1` to `100`
-- Returns JSON with `avg_ms`, `p50_ms`, `p95_ms`, `n`, and a fresh `timestamp_epoch_ms`
-
-Example:
-```bash
-curl "http://localhost:7860/benchmark?runs=30"
-```
-
-### Run Baseline
-```bash
-export API_BASE_URL="https://api.openai.com/v1"
-export MODEL_NAME="gpt-4o-mini"
-export OPENAI_API_KEY="your-key"
-export ENV_BASE_URL="http://localhost:7860"
-export HF_TOKEN="$OPENAI_API_KEY"
-export SEED="1"
-python inference.py
-```
-
-### OpenEnv Validation
-```bash
-pip install openenv-core
-openenv validate
-```
-
-### Suggested pre-submit check
-```bash
-openenv validate --verbose
-python3 -m unittest discover -s tests -p "test_*.py"
-docker build -t sql-debug-env .
-docker run --rm -p 7860:7860 sql-debug-env
-# in another terminal:
-curl -s http://localhost:7860/health
-curl -s http://localhost:7860/docs >/dev/null
-curl -s "http://localhost:7860/benchmark?runs=20"
-```
-
-## Hugging Face Spaces (Docker)
-1. Create a new **Space → Docker**.
-2. Push this repo.
-3. Update `openenv.yaml` → `api.base_url` to your Space URL: `https://<your-space>.hf.space`
-4. Wait for build, then verify:
-
-```bash
-curl -X POST https://<your-space>.hf.space/reset -H "Content-Type: application/json" -d '{}'
-```
-
+---
+title: Sql Debug Env
+emoji: 💻
+colorFrom: indigo
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference