Add scaffold files, fix compliance gaps for submission

- Add __init__.py: exports Action, ServerState, EnvClient
- Add client.py: Python EnvClient for programmatic environment access
- Add pyproject.toml: package metadata and dependencies
- Fix inference.py [START] log: remove non-standard capacity field
- Update openenv.yaml: document configurable environment parameters
- Update README: add configurable environment docs, update project structure

README.md

@@ -90,6 +90,27 @@ The environment simulates a backend server receiving variable traffic. The agent
 
 ---
 
+## Configurable Environment
+
+The environment is fully configurable via the `/reset` endpoint. Pass a `config` object to simulate different server profiles:
+
+```bash
+curl -X POST localhost:7860/reset -H "Content-Type: application/json" \
+     -d '{"task_id": "task_easy", "config": {"server_capacity": 200, "base_latency": 30}}'
+```
+
+| Parameter | Default | Description |
+|---|---|---|
+| `server_capacity` | 100.0 | Max requests/sec the server can handle |
+| `base_latency` | 50.0 | Response time at zero load (ms) |
+| `crash_load_ratio` | 1.3 | Server crashes at this multiple of capacity |
+| `max_queue` | 500 | Maximum pending request queue size |
+| `traffic_scale` | 1.0 | Multiplier for traffic patterns (2.0 = double traffic) |
+
+The LLM agent adapts automatically — the system prompt includes the configured capacity so the model knows the server's limits.
+
+---
+
 ## Setup
 
 ### Local (Python)
@@ -97,10 +118,10 @@ The environment simulates a backend server receiving variable traffic. The agent
 ```bash
 pip install -r requirements.txt
 
-# Start the environment server
-uvicorn environment:app --host 0.0.0.0 --port 7860
+# Start the environment + Gradio UI
+python app.py
 
-# In another terminal, run a quick smoke test
+# Smoke tests
 curl -s localhost:7860/health
 curl -s -X POST localhost:7860/reset -H "Content-Type: application/json" \
      -d '{"task_id": "task_easy"}' | python -m json.tool
@@ -115,8 +136,6 @@ curl -s localhost:7860/openenv.yaml
 ```bash
 docker build -t traffic-controller .
 docker run -p 7860:7860 traffic-controller
-
-# Same smoke tests work on localhost:7860
 ```
 
 ---
@@ -189,13 +208,17 @@ Measured on the deterministic simulator. Scores are in **0.0 – 1.0**.
 
 ```
 .
+├── app.py           # Gradio UI + mounts FastAPI endpoints
 ├── environment.py   # FastAPI app + episode logic
-├── tasks.py         # Traffic patterns + task metadata
-├── graders.py       # Per-task scoring functions
 ├── simulator.py     # Backend physics (latency, CPU, memory, crash)
-├── models.py        # Pydantic models (state, action, request/response)
-├── inference.py     # LLM agent runner
+├── models.py        # Pydantic models (state, action, config, request/response)
+├── tasks.py         # Traffic patterns + task metadata
+├── graders.py       # Per-task scoring functions (0.0–1.0)
+├── inference.py     # LLM agent runner (OpenAI client)
+├── client.py        # Python EnvClient for programmatic access
+├── __init__.py      # Exports Action, ServerState, EnvClient
 ├── openenv.yaml     # OpenEnv spec
+├── pyproject.toml   # Package metadata
 ├── Dockerfile
 ├── requirements.txt
 └── README.md

__init__.py

@@ -0,0 +1,6 @@
+"""Adaptive Backend Traffic Controller — OpenEnv Environment."""
+
+from models import Action, ServerState
+from client import EnvClient
+
+__all__ = ["Action", "ServerState", "EnvClient"]

client.py

@@ -0,0 +1,67 @@
+"""
+OpenEnv client for the Adaptive Traffic Controller.
+
+Provides a Python API to interact with the environment server
+without needing to make raw HTTP calls.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import httpx
+
+from models import Action, EnvConfig, ServerState
+
+
+class EnvClient:
+    """Client for the Adaptive Traffic Controller environment."""
+
+    def __init__(self, base_url: str = "http://localhost:7860", timeout: float = 30.0):
+        self.http = httpx.Client(base_url=base_url, timeout=timeout)
+
+    def health(self) -> bool:
+        resp = self.http.get("/health")
+        return resp.status_code == 200
+
+    def reset(
+        self,
+        task_id: str = "task_easy",
+        config: EnvConfig | None = None,
+    ) -> dict[str, Any]:
+        """Reset environment. Returns {state, task_id, max_steps, config}."""
+        payload: dict[str, Any] = {"task_id": task_id}
+        if config is not None:
+            payload["config"] = config.model_dump()
+        resp = self.http.post("/reset", json=payload)
+        resp.raise_for_status()
+        return resp.json()
+
+    def step(self, action: str | Action) -> dict[str, Any]:
+        """Take one step. Returns {state, reward, done, info}."""
+        if isinstance(action, Action):
+            action = action.value
+        resp = self.http.post("/step", json={"action": action})
+        resp.raise_for_status()
+        return resp.json()
+
+    def state(self) -> dict[str, Any]:
+        """Get current server state."""
+        resp = self.http.get("/state")
+        resp.raise_for_status()
+        return resp.json()
+
+    def tasks(self) -> list[dict[str, Any]]:
+        """List available tasks."""
+        resp = self.http.get("/tasks")
+        resp.raise_for_status()
+        return resp.json()["tasks"]
+
+    def close(self) -> None:
+        self.http.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()

inference.py

@@ -128,7 +128,7 @@ def run_task(task_id: str, env_url: str) -> float:
     capacity = data.get("config", {}).get("server_capacity", 100.0)
     system_prompt = SYSTEM_PROMPT_TEMPLATE.format(capacity=capacity)
 
-    print(f"[START] task={task_id} max_steps={max_steps} model={MODEL_NAME} capacity={capacity}")
+    print(f"[START] task={task_id} max_steps={max_steps} model={MODEL_NAME}")
 
     total_reward = 0.0
     final_score = 0.0

openenv.yaml

@@ -84,11 +84,37 @@ tasks:
       stability_bonus: "crash zeroes out primary score (partial credit * 0.3)"
       queue_factor: "fraction of steps with queue_length < 100"
 
+configuration:
+  description: >
+    The environment is fully configurable via the /reset endpoint.
+    Pass a config object to simulate different server profiles.
+  parameters:
+    server_capacity:
+      type: float
+      default: 100.0
+      description: Maximum requests/sec the server can handle
+    base_latency:
+      type: float
+      default: 50.0
+      description: Baseline latency in ms at zero load
+    crash_load_ratio:
+      type: float
+      default: 1.3
+      description: Load ratio that causes a crash (1.3 = 130% of capacity)
+    max_queue:
+      type: int
+      default: 500
+      description: Maximum queue size
+    traffic_scale:
+      type: float
+      default: 1.0
+      description: Multiplier for traffic patterns (2.0 = double traffic)
+
 endpoints:
   reset:
     method: POST
     path: /reset
-    description: Reset environment, returns initial state
+    description: Reset environment, returns initial state. Accepts optional config object.
   step:
     method: POST
     path: /step

pyproject.toml

@@ -0,0 +1,20 @@
+[project]
+name = "adaptive-traffic-controller"
+version = "1.0.0"
+description = "OpenEnv environment — LLM agent controls backend traffic throttling to prevent server crashes"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.111.0",
+    "uvicorn[standard]>=0.29.0",
+    "pydantic>=2.7.0",
+    "openai>=1.30.0",
+    "httpx>=0.27.0",
+    "numpy>=1.26.0",
+    "pyyaml>=6.0.1",
+    "gradio>=4.30.0",
+    "plotly>=5.22.0",
+]
+
+[build-system]
+requires = ["setuptools>=68.0"]
+build-backend = "setuptools.build_meta"