Update README.md

README.md

@@ -1,114 +1,116 @@
----
-title: Meta-Pytorch-Openenv
-emoji: 🦀
-colorFrom: blue
-colorTo: green
-sdk: docker
-app_port: 7860
-base_path: /web
----
-# 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
-```
+---
+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
+```