Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Multi-stage build using openenv-base
+# This Dockerfile is flexible and works for both:
+# - In-repo environments (with local OpenEnv sources)
+# - Standalone environments (with openenv from PyPI/Git)
+# The build script (openenv build) handles context detection and sets appropriate build args.
+
+ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
+FROM ${BASE_IMAGE} AS builder
+
+WORKDIR /app
+
+# Ensure git is available (required for installing dependencies from VCS)
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends git && \
+    rm -rf /var/lib/apt/lists/*
+
+# Build argument to control whether we're building standalone or in-repo
+ARG BUILD_MODE=in-repo
+ARG ENV_NAME=meta_env
+
+# Copy environment code (always at root of build context)
+COPY . /app/env
+
+# For in-repo builds, openenv is already vendored in the build context
+# For standalone builds, openenv will be installed via pyproject.toml
+WORKDIR /app/env
+
+# Ensure uv is available (for local builds where base image lacks it)
+RUN if ! command -v uv >/dev/null 2>&1; then \
+        curl -LsSf https://astral.sh/uv/install.sh | sh && \
+        mv /root/.local/bin/uv /usr/local/bin/uv && \
+        mv /root/.local/bin/uvx /usr/local/bin/uvx; \
+    fi
+    
+# Install dependencies using uv sync
+# If uv.lock exists, use it; otherwise resolve on the fly
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-install-project --no-editable; \
+    else \
+        uv sync --no-install-project --no-editable; \
+    fi
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ -f uv.lock ]; then \
+        uv sync --frozen --no-editable; \
+    else \
+        uv sync --no-editable; \
+    fi
+
+# Final runtime stage
+FROM ${BASE_IMAGE}
+
+WORKDIR /app
+
+# Copy the virtual environment from builder
+COPY --from=builder /app/env/.venv /app/.venv
+
+# Copy the environment code
+COPY --from=builder /app/env /app/env
+
+# Set PATH to use the virtual environment
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Set PYTHONPATH so imports work correctly
+ENV PYTHONPATH="/app/env:$PYTHONPATH"
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/health || exit 1
+
+# Run the FastAPI server
+# The module path is constructed to work with the /app/env structure
+ENV ENABLE_WEB_INTERFACE=true
+CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"]

README.md

@@ -1,10 +1,255 @@
 ---
-title: Cognitive Primitives Meta
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
+title: Meta Env Environment Server
+emoji: 🎭
+colorFrom: gray
+colorTo: red
 sdk: docker
 pinned: false
+app_port: 8000
+base_path: /web
+tags:
+  - openenv
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Meta Env Environment
+
+A simple test environment that echoes back messages. Perfect for testing the env APIs as well as demonstrating environment usage patterns.
+
+## Quick Start
+
+The simplest way to use the Meta Env environment is through the `MetaEnv` class:
+
+```python
+from meta_env import MetaAction, MetaEnv
+
+try:
+    # Create environment from Docker image
+    meta_envenv = MetaEnv.from_docker_image("meta_env-env:latest")
+
+    # Reset
+    result = meta_envenv.reset()
+    print(f"Reset: {result.observation.echoed_message}")
+
+    # Send multiple messages
+    messages = ["Hello, World!", "Testing echo", "Final message"]
+
+    for msg in messages:
+        result = meta_envenv.step(MetaAction(message=msg))
+        print(f"Sent: '{msg}'")
+        print(f"  → Echoed: '{result.observation.echoed_message}'")
+        print(f"  → Length: {result.observation.message_length}")
+        print(f"  → Reward: {result.reward}")
+
+finally:
+    # Always clean up
+    meta_envenv.close()
+```
+
+That's it! The `MetaEnv.from_docker_image()` method handles:
+- Starting the Docker container
+- Waiting for the server to be ready
+- Connecting to the environment
+- Container cleanup when you call `close()`
+
+## Building the Docker Image
+
+Before using the environment, you need to build the Docker image:
+
+```bash
+# From project root
+docker build -t meta_env-env:latest -f server/Dockerfile .
+```
+
+## Deploying to Hugging Face Spaces
+
+You can easily deploy your OpenEnv environment to Hugging Face Spaces using the `openenv push` command:
+
+```bash
+# From the environment directory (where openenv.yaml is located)
+openenv push
+
+# Or specify options
+openenv push --namespace my-org --private
+```
+
+The `openenv push` command will:
+1. Validate that the directory is an OpenEnv environment (checks for `openenv.yaml`)
+2. Prepare a custom build for Hugging Face Docker space (enables web interface)
+3. Upload to Hugging Face (ensuring you're logged in)
+
+### Prerequisites
+
+- Authenticate with Hugging Face: The command will prompt for login if not already authenticated
+
+### Options
+
+- `--directory`, `-d`: Directory containing the OpenEnv environment (defaults to current directory)
+- `--repo-id`, `-r`: Repository ID in format 'username/repo-name' (defaults to 'username/env-name' from openenv.yaml)
+- `--base-image`, `-b`: Base Docker image to use (overrides Dockerfile FROM)
+- `--private`: Deploy the space as private (default: public)
+
+### Examples
+
+```bash
+# Push to your personal namespace (defaults to username/env-name from openenv.yaml)
+openenv push
+
+# Push to a specific repository
+openenv push --repo-id my-org/my-env
+
+# Push with a custom base image
+openenv push --base-image ghcr.io/meta-pytorch/openenv-base:latest
+
+# Push as a private space
+openenv push --private
+
+# Combine options
+openenv push --repo-id my-org/my-env --base-image custom-base:latest --private
+```
+
+After deployment, your space will be available at:
+`https://huggingface.co/spaces/<repo-id>`
+
+The deployed space includes:
+- **Web Interface** at `/web` - Interactive UI for exploring the environment
+- **API Documentation** at `/docs` - Full OpenAPI/Swagger interface
+- **Health Check** at `/health` - Container health monitoring
+- **WebSocket** at `/ws` - Persistent session endpoint for low-latency interactions
+
+## Environment Details
+
+### Action
+**MetaAction**: Contains a single field
+- `message` (str) - The message to echo back
+
+### Observation
+**MetaObservation**: Contains the echo response and metadata
+- `echoed_message` (str) - The message echoed back
+- `message_length` (int) - Length of the message
+- `reward` (float) - Reward based on message length (length × 0.1)
+- `done` (bool) - Always False for echo environment
+- `metadata` (dict) - Additional info like step count
+
+### Reward
+The reward is calculated as: `message_length × 0.1`
+- "Hi" → reward: 0.2
+- "Hello, World!" → reward: 1.3
+- Empty message → reward: 0.0
+
+## Advanced Usage
+
+### Connecting to an Existing Server
+
+If you already have a Meta Env environment server running, you can connect directly:
+
+```python
+from meta_env import MetaEnv
+
+# Connect to existing server
+meta_envenv = MetaEnv(base_url="<ENV_HTTP_URL_HERE>")
+
+# Use as normal
+result = meta_envenv.reset()
+result = meta_envenv.step(MetaAction(message="Hello!"))
+```
+
+Note: When connecting to an existing server, `meta_envenv.close()` will NOT stop the server.
+
+### Using the Context Manager
+
+The client supports context manager usage for automatic connection management:
+
+```python
+from meta_env import MetaAction, MetaEnv
+
+# Connect with context manager (auto-connects and closes)
+with MetaEnv(base_url="http://localhost:8000") as env:
+    result = env.reset()
+    print(f"Reset: {result.observation.echoed_message}")
+    # Multiple steps with low latency
+    for msg in ["Hello", "World", "!"]:
+        result = env.step(MetaAction(message=msg))
+        print(f"Echoed: {result.observation.echoed_message}")
+```
+
+The client uses WebSocket connections for:
+- **Lower latency**: No HTTP connection overhead per request
+- **Persistent session**: Server maintains your environment state
+- **Efficient for episodes**: Better for many sequential steps
+
+### Concurrent WebSocket Sessions
+
+The server supports multiple concurrent WebSocket connections. To enable this,
+modify `server/app.py` to use factory mode:
+
+```python
+# In server/app.py - use factory mode for concurrent sessions
+app = create_app(
+    MetaEnvironment,  # Pass class, not instance
+    MetaAction,
+    MetaObservation,
+    max_concurrent_envs=4,  # Allow 4 concurrent sessions
+)
+```
+
+Then multiple clients can connect simultaneously:
+
+```python
+from meta_env import MetaAction, MetaEnv
+from concurrent.futures import ThreadPoolExecutor
+
+def run_episode(client_id: int):
+    with MetaEnv(base_url="http://localhost:8000") as env:
+        result = env.reset()
+        for i in range(10):
+            result = env.step(MetaAction(message=f"Client {client_id}, step {i}"))
+        return client_id, result.observation.message_length
+
+# Run 4 episodes concurrently
+with ThreadPoolExecutor(max_workers=4) as executor:
+    results = list(executor.map(run_episode, range(4)))
+```
+
+## Development & Testing
+
+### Direct Environment Testing
+
+Test the environment logic directly without starting the HTTP server:
+
+```bash
+# From the server directory
+python3 server/meta_env_environment.py
+```
+
+This verifies that:
+- Environment resets correctly
+- Step executes actions properly
+- State tracking works
+- Rewards are calculated correctly
+
+### Running Locally
+
+Run the server locally for development:
+
+```bash
+uvicorn server.app:app --reload
+```
+
+## Project Structure
+
+```
+meta_env/
+├── .dockerignore         # Docker build exclusions
+├── __init__.py            # Module exports
+├── README.md              # This file
+├── openenv.yaml           # OpenEnv manifest
+├── pyproject.toml         # Project metadata and dependencies
+├── uv.lock                # Locked dependencies (generated)
+├── client.py              # MetaEnv client
+├── models.py              # Action and Observation models
+└── server/
+    ├── __init__.py        # Server module exports
+    ├── meta_env_environment.py  # Core environment logic
+    ├── app.py             # FastAPI application (HTTP + WebSocket endpoints)
+    └── Dockerfile         # Container image definition
+```

__init__.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Meta Env Environment."""
+
+from .client import MetaEnv
+from .models import MetaAction, MetaObservation
+
+__all__ = [
+    "MetaAction",
+    "MetaObservation",
+    "MetaEnv",
+]

client.py

@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Meta Environment Client."""
+
+from typing import Dict
+
+from openenv.core import EnvClient
+from openenv.core.client_types import StepResult
+from openenv.core.env_server.types import State
+
+from .models import MetaAction, MetaObservation
+
+
+class MetaEnv(EnvClient[MetaAction, MetaObservation]):
+    """
+    Client for the Meta Environment.
+
+    Metacognition quiz. Answer questions with calibrated confidence.
+    """
+
+    def _step_payload(self, action: MetaAction) -> Dict:
+        """Convert MetaAction to JSON payload for step message."""
+        return {
+            "confidence": action.confidence,
+            "answer": action.answer,
+        }
+
+    def _parse_result(self, payload: Dict) -> StepResult[MetaObservation]:
+        """Parse server response into StepResult[MetaObservation]."""
+        obs_data = payload.get("observation", {})
+        observation = MetaObservation(
+            question=obs_data.get("question", ""),
+            options=obs_data.get("options", []),
+            round=obs_data.get("round", 0),
+            total_rounds=obs_data.get("total_rounds", 8),
+            last_correct=obs_data.get("last_correct"),
+            last_points=obs_data.get("last_points"),
+            total_points=obs_data.get("total_points", 0),
+            done=payload.get("done", False),
+            metadata=obs_data.get("metadata", {}),
+        )
+
+        return StepResult(
+            observation=observation,
+            reward=payload.get("reward"),
+            done=payload.get("done", False),
+        )
+
+    def _parse_state(self, payload: Dict) -> State:
+        """Parse server response into State object."""
+        return State(
+            episode_id=payload.get("episode_id"),
+            step_count=payload.get("step_count", 0),
+        )

models.py

@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Data models for the Meta Environment.
+
+Metacognition. Answer questions with confidence predictions.
+"""
+
+from typing import List, Optional
+
+from openenv.core.env_server.types import Action, Observation
+from pydantic import Field
+
+
+class MetaAction(Action):
+    """Action for the Meta environment - answer with confidence."""
+
+    confidence: int = Field(
+        ...,
+        description="Confidence level (25, 50, 75, or 100)"
+    )
+    answer: int = Field(
+        ...,
+        description="Answer index (0-3)",
+        ge=0,
+        le=3
+    )
+
+
+class MetaObservation(Observation):
+    """Observation from the Meta environment."""
+
+    question: str = Field(default="", description="Current question")
+    options: List[str] = Field(default_factory=list, description="Answer options (4 choices)")
+    round: int = Field(default=0, description="Current round (1-8)")
+    total_rounds: int = Field(default=8, description="Total number of rounds")
+    last_correct: Optional[bool] = Field(default=None, description="Was last answer correct")
+    last_points: Optional[int] = Field(default=None, description="Points from last answer")
+    total_points: int = Field(default=0, description="Cumulative points")

openenv.yaml

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+spec_version: 1
+name: meta_env
+type: space
+runtime: fastapi
+app: server.app:app
+port: 8000
+

pyproject.toml

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+[build-system]
+requires = ["setuptools>=45", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "openenv-meta_env"
+version = "0.1.0"
+description = "Meta Env environment for OpenEnv"
+requires-python = ">=3.10"
+dependencies = [
+    # Core OpenEnv runtime (provides FastAPI server + HTTP client types)
+    # install from github
+    # "openenv-core[core] @ git+https://github.com/meta-pytorch/OpenEnv.git",
+    "openenv-core[core]>=0.2.1",
+    # Environment-specific dependencies
+    # Add all dependencies needed for your environment here
+    # Examples:
+    # "numpy>=1.19.0",
+    # "torch>=2.0.0",
+    # "gymnasium>=0.29.0",
+    # "openspiel>=1.0.0",
+    # "smolagents>=1.22.0,<2",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0.0",
+    "pytest-cov>=4.0.0",
+]
+
+[project.scripts]
+# Server entry point - enables running via: uv run --project . server
+# or: python -m meta_env.server.app
+server = "meta_env.server.app:main"
+
+[tool.setuptools]
+include-package-data = true
+packages = ["meta_env", "meta_env.server"]
+package-dir = { "meta_env" = ".", "meta_env.server" = "server" }

server/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Meta Env environment server components."""
+
+from .meta_env_environment import MetaEnvironment
+
+__all__ = ["MetaEnvironment"]

server/app.py

@@ -0,0 +1,82 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+FastAPI application for the Meta Env Environment.
+
+This module creates an HTTP server that exposes the MetaEnvironment
+over HTTP and WebSocket endpoints, compatible with EnvClient.
+
+Endpoints:
+    - POST /reset: Reset the environment
+    - POST /step: Execute an action
+    - GET /state: Get current environment state
+    - GET /schema: Get action/observation schemas
+    - WS /ws: WebSocket endpoint for persistent sessions
+
+Usage:
+    # Development (with auto-reload):
+    uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
+
+    # Production:
+    uvicorn server.app:app --host 0.0.0.0 --port 8000 --workers 4
+
+    # Or run directly:
+    python -m server.app
+"""
+
+try:
+    from openenv.core.env_server.http_server import create_app
+except Exception as e:  # pragma: no cover
+    raise ImportError(
+        "openenv is required for the web interface. Install dependencies with '\n    uv sync\n'"
+    ) from e
+
+# Import from local models.py (PYTHONPATH includes /app/env in Docker)
+from models import MetaAction, MetaObservation
+
+from .meta_env_environment import MetaEnvironment
+
+
+# Create the app with web interface and README integration
+app = create_app(
+    MetaEnvironment,
+    MetaAction,
+    MetaObservation,
+    env_name="meta_env",
+    max_concurrent_envs=1,  # increase this number to allow more concurrent WebSocket sessions
+)
+
+
+def main(host: str = "0.0.0.0", port: int = 8000):
+    """
+    Entry point for direct execution via uv run or python -m.
+
+    This function enables running the server without Docker:
+        uv run --project . server
+        uv run --project . server --port 8001
+        python -m meta_env.server.app
+
+    Args:
+        host: Host address to bind to (default: "0.0.0.0")
+        port: Port number to listen on (default: 8000)
+
+    For production deployments, consider using uvicorn directly with
+    multiple workers:
+        uvicorn meta_env.server.app:app --workers 4
+    """
+    import uvicorn
+
+    uvicorn.run(app, host=host, port=port)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=8000)
+    args = parser.parse_args()
+    main(port=args.port)

server/meta_env_environment.py

@@ -0,0 +1,215 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Meta Environment Implementation.
+
+Metacognition. Answer questions with confidence predictions.
+Tests the agent's ability to calibrate confidence with accuracy.
+"""
+
+import random
+from uuid import uuid4
+
+from models import MetaAction, MetaObservation
+from openenv.core.env_server.interfaces import Environment
+from openenv.core.env_server.types import State
+
+
+class MetaEnvironment(Environment):
+    """
+    Metacognition environment.
+
+    8 quiz scenarios covering pattern, causation, probability, strategy, etc.
+    Agent must answer and predict confidence (25, 50, 75, or 100).
+
+    Scoring:
+    - Correct answer: +confidence points
+    - Wrong answer: -confidence/2 points
+
+    Final reward: total_points / 800 (perfect = all correct at 100% confidence)
+    """
+
+    SUPPORTS_CONCURRENT_SESSIONS: bool = True
+
+    TOTAL_ROUNDS = 8
+    MAX_POINTS = 800  # 8 rounds * 100 confidence
+
+    # Quiz scenarios with varying difficulty
+    QUIZ_BANK = [
+        {
+            "type": "pattern",
+            "question": "What comes next in the sequence: 2, 6, 12, 20, ?",
+            "options": ["28", "30", "32", "36"],
+            "correct": 1,  # 30 (difference increases by 2 each time: 4, 6, 8, 10)
+        },
+        {
+            "type": "causation",
+            "question": "A study finds cities with more ice cream sales have higher crime rates. This likely means:",
+            "options": [
+                "Ice cream causes crime",
+                "Crime causes ice cream sales",
+                "A third factor (like heat) affects both",
+                "The correlation is coincidental"
+            ],
+            "correct": 2,
+        },
+        {
+            "type": "probability",
+            "question": "You flip a fair coin 5 times and get heads each time. What's the probability of heads on the 6th flip?",
+            "options": ["Less than 50%", "Exactly 50%", "More than 50%", "Depends on previous flips"],
+            "correct": 1,
+        },
+        {
+            "type": "strategy",
+            "question": "In a game where you can cooperate or defect, your opponent will copy your last move. Best long-term strategy?",
+            "options": ["Always defect", "Always cooperate", "Alternate", "Random"],
+            "correct": 1,
+        },
+        {
+            "type": "logic",
+            "question": "All roses are flowers. Some flowers fade quickly. Therefore:",
+            "options": [
+                "All roses fade quickly",
+                "Some roses fade quickly",
+                "No roses fade quickly",
+                "Cannot be determined"
+            ],
+            "correct": 3,
+        },
+        {
+            "type": "estimation",
+            "question": "Approximately how many piano tuners are in a city of 1 million people?",
+            "options": ["About 10", "About 100", "About 1,000", "About 10,000"],
+            "correct": 1,  # ~100 (rough Fermi estimate)
+        },
+        {
+            "type": "inversion",
+            "question": "If all squares are rectangles, what can we conclude?",
+            "options": [
+                "All rectangles are squares",
+                "Some rectangles are not squares",
+                "No rectangles are squares",
+                "Cannot be determined from this alone"
+            ],
+            "correct": 3,
+        },
+        {
+            "type": "base_rate",
+            "question": "A disease affects 1% of population. A test is 90% accurate. If you test positive, what's your approximate chance of having the disease?",
+            "options": ["About 90%", "About 50%", "About 10%", "About 1%"],
+            "correct": 2,  # ~9% (base rate neglect)
+        },
+    ]
+
+    def __init__(self):
+        """Initialize the meta environment."""
+        self._state = State(episode_id=str(uuid4()), step_count=0)
+        self._questions: list[dict] = []
+        self._round: int = 0
+        self._total_points: int = 0
+        self._last_correct: bool | None = None
+        self._last_points: int | None = None
+
+    def reset(self) -> MetaObservation:
+        """
+        Reset the environment.
+
+        Shuffle and select 8 quiz scenarios.
+        """
+        self._state = State(episode_id=str(uuid4()), step_count=0)
+
+        # Shuffle questions
+        self._questions = self.QUIZ_BANK.copy()
+        random.shuffle(self._questions)
+
+        self._round = 0
+        self._total_points = 0
+        self._last_correct = None
+        self._last_points = None
+
+        # Return first question
+        first_q = self._questions[0]
+        return MetaObservation(
+            question=first_q["question"],
+            options=first_q["options"],
+            round=1,
+            total_rounds=self.TOTAL_ROUNDS,
+            last_correct=None,
+            last_points=None,
+            total_points=0,
+            done=False,
+        )
+
+    def step(self, action: MetaAction) -> MetaObservation:
+        """
+        Execute an answer.
+
+        Args:
+            action: MetaAction with confidence (25/50/75/100) and answer (0-3)
+
+        Returns:
+            MetaObservation with result and next question
+        """
+        self._state.step_count += 1
+
+        # Validate confidence
+        confidence = action.confidence
+        if confidence not in [25, 50, 75, 100]:
+            confidence = 25  # Default to lowest if invalid
+
+        # Check answer
+        current_q = self._questions[self._round]
+        correct = action.answer == current_q["correct"]
+        self._last_correct = correct
+
+        # Calculate points
+        if correct:
+            points = confidence
+        else:
+            points = -confidence // 2
+
+        self._last_points = points
+        self._total_points += points
+        self._round += 1
+
+        done = self._round >= self.TOTAL_ROUNDS
+
+        # Calculate reward at end
+        reward = 0.0
+        if done:
+            reward = max(0, self._total_points) / self.MAX_POINTS
+
+        # Prepare next question or final state
+        if done:
+            return MetaObservation(
+                question="",
+                options=[],
+                round=self._round,
+                total_rounds=self.TOTAL_ROUNDS,
+                last_correct=correct,
+                last_points=points,
+                total_points=self._total_points,
+                done=True,
+                reward=reward,
+            )
+        else:
+            next_q = self._questions[self._round]
+            return MetaObservation(
+                question=next_q["question"],
+                options=next_q["options"],
+                round=self._round + 1,
+                total_rounds=self.TOTAL_ROUNDS,
+                last_correct=correct,
+                last_points=points,
+                total_points=self._total_points,
+                done=False,
+            )
+
+    @property
+    def state(self) -> State:
+        """Get the current environment state."""
+        return self._state

server/requirements.txt

@@ -0,0 +1,6 @@
+openenv[core]>=0.2.0
+fastapi>=0.115.0
+uvicorn>=0.24.0
+
+
+