Upload folder using huggingface_hub

Dockerfile

@@ -1,26 +1,27 @@
-FROM python:3.10-slim
-
-# Creates a non-root user with an explicit UID and GID required by Hugging Face Spaces
-RUN groupadd -g 1000 user && \
-    useradd -m -u 1000 -g 1000 user
-USER 1000:1000
-
-# Set environment variables
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-# Create the working directory
-WORKDIR $HOME/app
-
-# Install dependencies first for Docker caching
-COPY --chown=user:user requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy the rest of the files
-COPY --chown=user:user . .
-
-# Expose the standard Hugging Face Space port
-EXPOSE 7860
-
-# Start the environment API server
-CMD ["uvicorn", "env:app", "--host", "0.0.0.0", "--port", "7860"]
+FROM python:3.10-slim
+
+# Creates a non-root user with an explicit UID and GID required by Hugging Face Spaces
+RUN groupadd -g 1000 user && \
+    useradd -m -u 1000 -g 1000 user
+USER 1000:1000
+
+# Set environment variables
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# Create the working directory
+WORKDIR $HOME/app
+
+# Install dependencies first for Docker caching
+COPY --chown=user:user requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the files
+COPY --chown=user:user . .
+
+# Expose the standard Hugging Face Space port
+EXPOSE 7860
+
+# Start the environment API server
+ENV ENABLE_WEB_INTERFACE=true
+CMD ["uvicorn", "env:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,122 +1,123 @@
----
-title: SQL Data Engineer Environment
-emoji: 🗄️
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
-app_port: 7860
----
-# OpenEnv: SQL Data Engineer Environment
-
-Welcome to the **SQL Data Engineer Environment**—a robust, fully-compliant baseline environment built for the Meta OpenEnv Hackathon. This project tests an AI agent's ability to natively interact with a live SQL engine to perform pragmatic data extraction, cleansing, and complex schema normalization tasks.
-
-## Environment Description & Motivation
-
-### Why SQL Data Engineering?
-The industry needs reliable agents that can act as backend developers, DB administrators, and data engineers. While many environments focus on web browsing or gaming, manipulating relational databases is a high-value, real-world task. This environment simulates authentic obstacles developers face:
-- Analyzing undocumented database schemas.
-- Cleansing noisy string data into strict scalar types.
-- Restructuring and normalizing flat tables into relational architectures while rigorously preserving foreign-key constraints.
-
-It presents an excellent metric to gauge an LLM's structured reasoning and precise SQL generation capabilities.
-
----
-
-## Space Definitions
-
-The environment adheres strictly to the OpenEnv Pydantic specification, enabling seamless API integration.
-
-### Observation Space
-The observation space is tailored to provide dense context while remaining token-efficient:
-- `goal` (string): The explicit task prompt/requirement dictating what the agent must achieve.
-- `schema_dump` (string | null): The current DDL representing all tables and views in the DB (schema definition). Sent back only when the schema dynamically changes or the state is stable.
-- `result` (string): The standard output of the previously executed query (capped to 10 rows for SELECTs) or a clear `rowcount` confirmation for INSERTs/UPDATEs.
-- `last_action_error` (boolean): Flag indicating if the previous SQL Action threw a syntax or logic engine error.
-- `step` (integer): The current episode step tally.
-
-### Action Space
-- `action_str` (string): The agent must return a JSON dictionary containing a single, syntactically correct SQLite query to be executed against the backend state.
-
----
-
-## Tasks & Graders
-
-Each episode challenges the agent with one of 3 tasks featuring deterministic OpenEnv graders scoring between `0.0` and `1.0`.
-
-#### 1. Easy: Data Extraction (View Creation)
-- **Goal**: Read a `customers` table, filter out metrics > 1000.0, and construct a targeted `high_value_customers` SQL View.
-- **Difficulty**: Easy. Tests basic SELECT syntax and DDL proficiency.
-- **Grader**: Validates if the correct view exists in `sqlite_master`, assigns `0.5` points. Exact row and content matching grants the remaining `+0.5`.
-
-#### 2. Medium: Data Cleaning
-- **Goal**: Coerce a messy `products` table. The agent must standardize categorical string sizes (e.g., converting 'ELEC' to 'ELECTRONICS') and extract numeric floats from dirty string pricing (e.g., '$85.00' -> `85.0`) into a new generated float column.
-- **Difficulty**: Medium. Tests native string pattern matching and targeted UPDATE pipelines.
-- **Grader**: Adding the column yields `0.3` points. Correct categorical string mapping grants up to `0.3`, and correctly extracted float prices yield `0.4` respectively.
-
-#### 3. Hard: Schema Normalization
-- **Goal**: Normalize a completely flat `hospital_records` repository into a structured 3-table format (`patients`, `doctors`, `appointments`). Data must be completely migrated and bound by Primary/Foreign key constraints.
-- **Difficulty**: Hard. Tests multi-step schema architectural reasoning and safe data-migration pipelines.
-- **Grader**: Validating table signatures issues `0.1` per table. Proper data counts yield `0.1` each, and if a relational JOIN across the new DB perfectly rebuilds the original flat map, the final `0.3` is awarded.
-
-### The Dense Reward Function
-Scores are completely dense over the episode lifecycle. 
-At `step(action)`, the grader executes. The mathematical reward signal is continuous: 
-`Reward = (Current_Score - Previous_Score)`. 
-*Note: A `-0.05` penalty is actively applied when `last_action_error` triggers, strongly discouraging hallucinated or malformed SQL loops.*
-
----
-
-## Local Setup & Usage
-
-To validate the OpenEnv schema, install the framework, and run the OpenAI-compatible baseline script locally:
-
-```bash
-# 1. Clone the repository and navigate inside
-git clone <your-repo-url>
-cd OpenEnv-SQL-Data-Engineer
-
-# 2. Setup standard Python virtual environment
-python -m venv venv
-source venv/bin/activate  # Or `venv\Scripts\activate` on Windows
-
-# 3. Install core dependencies (FastAPI, Pydantic, OpenAI, OpenEnv)
-pip install openenv openenv-core openai pydantic fastapi uvicorn requests
-
-# 4. Verify OpenEnv schema compliance locally
-openenv validate
-
-# 5. Execute the baseline AI Agent (make sure to set your key)
-export OPENAI_API_KEY="your-api-key"
-export MODEL_NAME="gpt-4o"
-python inference.py
-```
-
----
-
-## Deployment Instructions
-
-### Docker Container Build
-The environment provides a native Hugging Face structured `Dockerfile` configured to launch on port 7860 as an unprivileged user.
-
-```bash
-docker build -t openenv-sql .
-docker run -p 7860:7860 openenv-sql
-```
-
-### Deploying to Hugging Face Spaces
-To finalize your Hackathon deployment and spin up the live inference API:
-1. First, create a new minimal **Docker Space** inside Hugging Face.
-2. Ensure you add `HF_TOKEN` globally inside your HF Space Repository secrets.
-3. Push this directory to the Space via git:
-   ```bash
-   git remote add space https://huggingface.co/spaces/<your-username>/<your-space-name>
-   git push space main
-   ```
-4. The environment URL will naturally respond to ping checks and `/reset` on HF endpoints.
-
-
-### Baseline Scores (Llama-3-8B-Instruct)
-- **Easy Task:** 1.0 (Passed)
-- **Medium Task:** 0.62 (Partial Success - struggled with complex string casting)
+---
+title: SQL Data Engineer Environment
+emoji: 🗄️
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+app_port: 7860
+base_path: /web
+---
+# OpenEnv: SQL Data Engineer Environment
+
+Welcome to the **SQL Data Engineer Environment**—a robust, fully-compliant baseline environment built for the Meta OpenEnv Hackathon. This project tests an AI agent's ability to natively interact with a live SQL engine to perform pragmatic data extraction, cleansing, and complex schema normalization tasks.
+
+## Environment Description & Motivation
+
+### Why SQL Data Engineering?
+The industry needs reliable agents that can act as backend developers, DB administrators, and data engineers. While many environments focus on web browsing or gaming, manipulating relational databases is a high-value, real-world task. This environment simulates authentic obstacles developers face:
+- Analyzing undocumented database schemas.
+- Cleansing noisy string data into strict scalar types.
+- Restructuring and normalizing flat tables into relational architectures while rigorously preserving foreign-key constraints.
+
+It presents an excellent metric to gauge an LLM's structured reasoning and precise SQL generation capabilities.
+
+---
+
+## Space Definitions
+
+The environment adheres strictly to the OpenEnv Pydantic specification, enabling seamless API integration.
+
+### Observation Space
+The observation space is tailored to provide dense context while remaining token-efficient:
+- `goal` (string): The explicit task prompt/requirement dictating what the agent must achieve.
+- `schema_dump` (string | null): The current DDL representing all tables and views in the DB (schema definition). Sent back only when the schema dynamically changes or the state is stable.
+- `result` (string): The standard output of the previously executed query (capped to 10 rows for SELECTs) or a clear `rowcount` confirmation for INSERTs/UPDATEs.
+- `last_action_error` (boolean): Flag indicating if the previous SQL Action threw a syntax or logic engine error.
+- `step` (integer): The current episode step tally.
+
+### Action Space
+- `action_str` (string): The agent must return a JSON dictionary containing a single, syntactically correct SQLite query to be executed against the backend state.
+
+---
+
+## Tasks & Graders
+
+Each episode challenges the agent with one of 3 tasks featuring deterministic OpenEnv graders scoring between `0.0` and `1.0`.
+
+#### 1. Easy: Data Extraction (View Creation)
+- **Goal**: Read a `customers` table, filter out metrics > 1000.0, and construct a targeted `high_value_customers` SQL View.
+- **Difficulty**: Easy. Tests basic SELECT syntax and DDL proficiency.
+- **Grader**: Validates if the correct view exists in `sqlite_master`, assigns `0.5` points. Exact row and content matching grants the remaining `+0.5`.
+
+#### 2. Medium: Data Cleaning
+- **Goal**: Coerce a messy `products` table. The agent must standardize categorical string sizes (e.g., converting 'ELEC' to 'ELECTRONICS') and extract numeric floats from dirty string pricing (e.g., '$85.00' -> `85.0`) into a new generated float column.
+- **Difficulty**: Medium. Tests native string pattern matching and targeted UPDATE pipelines.
+- **Grader**: Adding the column yields `0.3` points. Correct categorical string mapping grants up to `0.3`, and correctly extracted float prices yield `0.4` respectively.
+
+#### 3. Hard: Schema Normalization
+- **Goal**: Normalize a completely flat `hospital_records` repository into a structured 3-table format (`patients`, `doctors`, `appointments`). Data must be completely migrated and bound by Primary/Foreign key constraints.
+- **Difficulty**: Hard. Tests multi-step schema architectural reasoning and safe data-migration pipelines.
+- **Grader**: Validating table signatures issues `0.1` per table. Proper data counts yield `0.1` each, and if a relational JOIN across the new DB perfectly rebuilds the original flat map, the final `0.3` is awarded.
+
+### The Dense Reward Function
+Scores are completely dense over the episode lifecycle. 
+At `step(action)`, the grader executes. The mathematical reward signal is continuous: 
+`Reward = (Current_Score - Previous_Score)`. 
+*Note: A `-0.05` penalty is actively applied when `last_action_error` triggers, strongly discouraging hallucinated or malformed SQL loops.*
+
+---
+
+## Local Setup & Usage
+
+To validate the OpenEnv schema, install the framework, and run the OpenAI-compatible baseline script locally:
+
+```bash
+# 1. Clone the repository and navigate inside
+git clone <your-repo-url>
+cd OpenEnv-SQL-Data-Engineer
+
+# 2. Setup standard Python virtual environment
+python -m venv venv
+source venv/bin/activate  # Or `venv\Scripts\activate` on Windows
+
+# 3. Install core dependencies (FastAPI, Pydantic, OpenAI, OpenEnv)
+pip install openenv openenv-core openai pydantic fastapi uvicorn requests
+
+# 4. Verify OpenEnv schema compliance locally
+openenv validate
+
+# 5. Execute the baseline AI Agent (make sure to set your key)
+export OPENAI_API_KEY="your-api-key"
+export MODEL_NAME="gpt-4o"
+python inference.py
+```
+
+---
+
+## Deployment Instructions
+
+### Docker Container Build
+The environment provides a native Hugging Face structured `Dockerfile` configured to launch on port 7860 as an unprivileged user.
+
+```bash
+docker build -t openenv-sql .
+docker run -p 7860:7860 openenv-sql
+```
+
+### Deploying to Hugging Face Spaces
+To finalize your Hackathon deployment and spin up the live inference API:
+1. First, create a new minimal **Docker Space** inside Hugging Face.
+2. Ensure you add `HF_TOKEN` globally inside your HF Space Repository secrets.
+3. Push this directory to the Space via git:
+   ```bash
+   git remote add space https://huggingface.co/spaces/<your-username>/<your-space-name>
+   git push space main
+   ```
+4. The environment URL will naturally respond to ping checks and `/reset` on HF endpoints.
+
+
+### Baseline Scores (Llama-3-8B-Instruct)
+- **Easy Task:** 1.0 (Passed)
+- **Medium Task:** 0.62 (Partial Success - struggled with complex string casting)
 - **Hard Task:** 0.2 (Challenging - requires higher reasoning/longer context)

pyproject.toml

@@ -1,25 +1,25 @@
-[build-system]
-requires = ["setuptools>=61.0"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "sql-data-engineer-env"
-version = "0.1.0"
-description = "A real-world SQL data engineering environment for agent evaluation."
-readme = "README.md"
-requires-python = ">=3.10"
-dependencies = [
-    "openenv-core",
-    "fastapi",
-    "uvicorn",
-    "pydantic",
-    "openai",
-    "pandas"
-]
-
-[tool.setuptools.packages.find]
-where = ["."]
-include = ["server*"]
-
-[project.scripts]
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "sql-data-engineer-env"
+version = "0.1.0"
+description = "A real-world SQL data engineering environment for agent evaluation."
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "openenv-core",
+    "fastapi",
+    "uvicorn",
+    "pydantic",
+    "openai",
+    "pandas"
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["server*"]
+
+[project.scripts]
 server = "server.app:main"