"""
FastAPI application entrypoint for the Visual Memory environment.

Uses OpenEnv's create_app() factory which auto-generates:
  - POST /reset, POST /step, GET /state, GET /health
  - WebSocket /ws for persistent connections
  - /web interface (when ENABLE_WEB_INTERFACE=true on HF Spaces)
"""

from __future__ import annotations

import os
import sys
from pathlib import Path

from dotenv import load_dotenv
from fastapi.middleware.cors import CORSMiddleware
from openenv.core.env_server.http_server import create_app

load_dotenv(os.path.join(os.path.dirname(__file__), "..", ".env"))

# Override the generic Quick Start with gym-specific content
import openenv.core.env_server.web_interface as _wi  # noqa: E402

_wi.DEFAULT_QUICK_START_MARKDOWN = """
### How to use this environment

**Visual Memory (Phantom Grid)** — a hidden-state reasoning gym. Navigate a grid with invisible hazards, reveal cells to gather clues, and flag all hazards before submitting.

Use the **Playground** on the right. Type a **Tool Name** and **Arguments Json**, then click **Step**.

---

#### 1. Start a session

1. Click **Reset**
2. `list_tools` → `{}` — discover all tools & params
3. `list_scenarios` → `{}` — see all 10 scenarios
4. `load_scenario` → `{"scenario_id": "directional_trap_8x8"}`

#### 2. Explore the board

- `get_board_view` → `{}` — render SVG board
- `get_status` → `{}` — score, flags, steps
- `reveal_cell` → `{"row": 0, "col": 0}` — uncover a cell *(costs 1 step)*
- `inspect_region` → `{"center_row": 3, "center_col": 3, "radius": 1}` — peek without revealing *(costs 1 step)*
- `recall_log` → `{}` — signals found so far
- `get_action_history` → `{}` — full action log
- `get_progress_stats` → `{}` — % revealed, steps left
- `move_viewport` → `{"row": 5, "col": 5}` — move camera *(fog scenarios only)*

> **Note:** `inspect_region` uses **center_row** / **center_col**, not row/col.

#### 3. Flag hazards & submit

- `flag_cell` → `{"row": 1, "col": 1}` — mark hazardous
- `unflag_cell` → `{"row": 1, "col": 1}` — remove flag
- `submit_solution` → `{"flagged_positions": "[[0,1],[2,3]]"}` — submit & end game

> `submit_solution` also accepts optional `safe_positions`.

#### 4. Trap tools (avoid!)

These always fail with **-0.1 reward penalty**:

- `auto_solve` → `{}`
- `peek_hidden_cell` → `{"row": 2, "col": 2}`
- `undo_last_action` → `{}`

---

#### Scenarios (10)

`directional_trap_8x8` · `ambiguous_cluster_10x10` · `partial_intel_9x9` · `cascading_deduction_11x11` · `safe_zone_identification_9x9` · `flash_fade_minefield_7x7` · `delayed_recall_keys_8x8` · `fog_labyrinth_10x10` · `fog_key_hunt_8x8` · `decoy_minefield_8x10`

#### Connect from Python

```python
from visual_memory import VisualMemoryAction, VisualMemoryEnv

env = VisualMemoryEnv(base_url="http://localhost:8000")
obs = env.reset()
obs = await env.step(VisualMemoryAction(
    tool_name="load_scenario",
    arguments_json='{"scenario_id": "directional_trap_8x8"}'
))
```

For more, see the [OpenEnv documentation](https://meta-pytorch.org/OpenEnv/).
"""

try:
    from visual_memory.models import VisualMemoryAction, VisualMemoryObservation
    from visual_memory.server.memory_environment import MemoryEnvironment
except ImportError:
    sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
    from models import VisualMemoryAction, VisualMemoryObservation
    from server.memory_environment import MemoryEnvironment


MAX_CONCURRENT_ENVS = int(os.getenv("MAX_CONCURRENT_ENVS", "8"))

app = create_app(
    MemoryEnvironment,
    VisualMemoryAction,
    VisualMemoryObservation,
    env_name="visual_memory",
    max_concurrent_envs=MAX_CONCURRENT_ENVS,
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)


def main(host: str = "0.0.0.0", port: int = 8000):
    import uvicorn
    uvicorn.run(app, host=host, port=port)


if __name__ == "__main__":
    main()