| --- |
| title: Meta-Pytorch-Openenv |
| emoji: 🦀 |
| colorFrom: blue |
| colorTo: green |
| sdk: docker |
| app_port: 7860 |
| base_path: /web |
| tags: |
| - openenv |
| --- |
| # SQL / Data Cleaning Sandbox |
|
|
| An **OpenEnv**-compliant environment where AI agents clean messy SQLite databases |
| using SQL queries and Python code. |
|
|
| ## Overview |
|
|
| | Feature | Details | |
| |---|---| |
| | **Interface** | `step()` / `reset()` / `state()` | |
| | **Action space** | `{ tool: "sql" \| "python", command: "..." }` | |
| | **Observation** | `{ output, error, current_step, max_steps, task_description }` | |
| | **Reward** | 0.0 - 1.0 with **partial progress signals** | |
| | **Tasks** | 3 (easy, medium, hard) | |
|
|
| ## Tasks |
|
|
| ### Easy - Data Triage |
| > Find the total revenue from the `sales` table for January 2024. |
|
|
| **Grader**: Checks if the computed total matches the expected float value (1000.00). |
|
|
| ### Medium - Data Cleaning |
| > Fix duplicate emails, NULL ages, and uppercase emails in the `users` table. |
|
|
| **Grader**: Partial scoring: |
| - 0.3 for all emails lowercase |
| - 0.4 for no duplicate emails |
| - 0.3 for no NULL ages |
|
|
| ### Hard - Schema Migration |
| > Normalize `flat_orders` into `customers` + `orders` tables with foreign keys. |
| |
| **Grader**: Partial scoring: |
| - 0.2 for correct `customers` schema |
| - 0.2 for correct `orders` schema |
| - 0.2 for 4 unique customers |
| - 0.2 for 6 orders migrated |
| - 0.2 for valid FK integrity |
| |
| ## Quick Start |
| |
| ### Local Development |
| |
| 1. **Clone and Install** |
| ```bash |
| # Clone the repository |
| git clone https://github.com/shreyas231219/Meta-Pytorch-Openenv.git |
| cd Meta-Pytorch-Openenv |
| |
| # Install dependencies |
| pip install -e . |
| ``` |
| |
| 2. **Run the Server** |
| The server will default to port **7860**. |
| |
| **Bash (Linux/macOS):** |
| ```bash |
| TASK_ID=easy python -m server.app |
| ``` |
| |
| **PowerShell (Windows):** |
| ```powershell |
| $env:TASK_ID='easy' |
| python -m server.app |
| ``` |
| |
| ### Docker (Hugging Face Spaces Ready) |
| |
| ```bash |
| # Build |
| docker build -t sql-sandbox:latest . |
| |
| # Run on HF Spaces default port 7860 |
| docker run -p 7860:7860 sql-sandbox:latest |
| ``` |
| |
| ## Baseline Inference |
| |
| Runs GPT-4o on all three tasks and prints reproducible scores. |
| |
| ```powershell |
| # For local testing in PowerShell (Windows) |
| $env:HF_TOKEN='sk-...' |
| $env:MODEL_NAME='gpt-4o' |
| python inference.py --url http://localhost:7860 |
| ``` |
| |
| ## Project Structure |
| |
| ``` |
| . |
| ├── Dockerfile # Root Dockerfile for HF Spaces |
| ├── openenv.yaml # OpenEnv manifest (port 7860) |
| ├── pyproject.toml # Package dependencies |
| ├── inference.py # baseline inference script |
| ├── inference_groq.py # groq inference script |
| ├── README.md # This file |
| ├── client.py # EnvClient helper |
| ├── models.py # Action & Observation models |
| └── server/ |
| ├── app.py # FastAPI server entry point |
| └── environment.py # Core environment logic + graders |
| ``` |
| |
