"""
FastAPI server for the Data Cleaning RL Environment.

Uses openenv's create_app() to get the standard /reset, /step, /state,
/health, /schema, /ws endpoints automatically. Adds custom hackathon
endpoints: /tasks, /grader, /baseline.
"""

from __future__ import annotations

import os

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

from openenv.core.env_server.http_server import create_app

from data_cleaning_env.action_registry import build_action_schema
from data_cleaning_env.models import CleaningAction, Observation
from data_cleaning_env.server.environment import DataCleaningEnvironment

# ---------------
# Configuration
# ---------------
SERVER_PORT: int = int(os.environ.get("SERVER_PORT", "8000"))

# ---------------
# Create the app via openenv — gives us /reset, /step, /state, /health, /ws
# ---------------
app: FastAPI = create_app(
    DataCleaningEnvironment,       # factory callable
    CleaningAction,                # Action class
    Observation,                   # Observation class
    env_name="data-cleaning-env",
    max_concurrent_envs=4,
)


# ---------------
# Custom hackathon endpoints (mounted on the same app)
# ---------------


class GraderRequest(BaseModel):
    episode_id: str


@app.get("/tasks", summary="List tasks and action schema")
async def tasks():
    return {
        "tasks": [
            {
                "id": "easy",
                "description": "Fix missing values in the Iris dataset (15% numeric missing).",
                "dataset": "iris (OpenML ID 61)",
                "max_steps": 20,
                "noise_types": ["missing_values"],
            },
            {
                "id": "medium",
                "description": "Fix missing values, type errors, and duplicates in Adult Income (2k sample).",
                "dataset": "adult (OpenML ID 1590, 2k sample)",
                "max_steps": 40,
                "noise_types": ["missing_values", "type_errors", "duplicates"],
            },
            {
                "id": "hard",
                "description": "Fix missing values, type errors, duplicates, outliers, and schema violations in Credit-G.",
                "dataset": "credit-g (OpenML ID 31)",
                "max_steps": 60,
                "noise_types": ["missing_values", "type_errors", "duplicates", "outliers", "schema_violations"],
            },
            {
                "id": "expert",
                "description": "Clean ML metadata: mislabels, corrupted paths, inconsistent labels, duplicates, invalid dimensions.",
                "dataset": "ml-metadata (synthetic, 2000 rows)",
                "max_steps": 80,
                "noise_types": [
                    "mislabels", "corrupted_paths", "inconsistent_labels",
                    "duplicates", "missing_values", "invalid_dimensions", "format_inconsistency",
                ],
            },
        ],
        "action_schema": build_action_schema(),
    }


@app.post("/grader", summary="Grade a completed episode")
async def grader(req: GraderRequest):
    """
    Compute the grader score for an episode. Requires a REST-based workflow
    where the environment instance is long-lived. For WebSocket-based
    GenericEnvClient usage, grading happens via the reward in the observation.
    """
    # The openenv server creates fresh instances per session, so this endpoint
    # is only useful in our REST workflow. Return a generic message if not found.
    raise HTTPException(
        status_code=501,
        detail="Grading is computed via observation rewards in the openenv protocol. "
               "Use the per-step reward and done signal to evaluate agent performance.",
    )


@app.post("/baseline", summary="Run the baseline heuristic agent")
async def baseline():
    from data_cleaning_env.baseline import run_baseline_all
    scores = run_baseline_all()
    return {"baseline_scores": scores}


def main(host: str = "0.0.0.0", port: int = 8000) -> None:
    import uvicorn
    uvicorn.run(app, host=host, port=port)


if __name__ == "__main__":
    main()