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6bfc362 fb9e30c 6bfc362 fb9e30c 6bfc362 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 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 Prana Env Environment.
This module creates an HTTP server that exposes the PranaEnvironment
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 PranaAction, PranaObservation
from .prana_env_environment import PranaEnvironment
# Create the app with web interface and README integration
app = create_app(
PranaEnvironment,
PranaAction,
PranaObservation,
env_name="prana_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 prana_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 prana_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)
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