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shreyas231219/Meta-Pytorch-Openenv

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App README

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 .

1.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