"""
Chronos-2 Zero-Shot Demo - FastAPI Backend

Standalone version for HF Spaces deployment.
Run locally: uvicorn server:app --reload --port 7860
"""

from __future__ import annotations

from pathlib import Path

import pandas as pd
import torch

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
from pydantic import BaseModel, Field

# Chronos-2 imports
try:
    from chronos import Chronos2Pipeline
except ImportError:
    raise ImportError(
        "Please install chronos-forecasting>=2.0: pip install 'chronos-forecasting[scripts]>=2.0'"
    )

DEMO_DIR = Path(__file__).resolve().parent
STATIC_DIR = DEMO_DIR / "static"

# Model configuration
MODEL_NAME = "amazon/chronos-2"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"


# =============================================================================
# Chronos-2 Forecaster (standalone)
# =============================================================================

class Chronos2Forecaster:
    """Wrapper for Chronos-2 time series forecasting."""

    def __init__(self, model_name: str = MODEL_NAME, device: str = DEVICE):
        self.model_name = model_name
        self.device = device
        self.pipeline = None

    def load_model(self) -> None:
        """Load the Chronos-2 model pipeline."""
        print(f"Loading Chronos-2 model: {self.model_name}")
        print(f"Device: {self.device}")
        self.pipeline = Chronos2Pipeline.from_pretrained(
            self.model_name,
            device_map=self.device,
        )
        print("Model loaded successfully!")

    def forecast(
        self,
        context_df: pd.DataFrame,
        prediction_length: int = 12,
        quantile_levels: list[float] | None = None,
    ) -> dict:
        """Generate probabilistic forecasts."""
        if self.pipeline is None:
            self.load_model()

        if quantile_levels is None:
            quantile_levels = [0.1, 0.5, 0.9]

        pred_df = self.pipeline.predict_df(
            context_df,
            prediction_length=prediction_length,
            quantile_levels=quantile_levels,
            id_column="item_id",
            timestamp_column="timestamp",
            target="target",
        )

        return {
            "median": pred_df["0.5"].values,
            "low": pred_df["0.1"].values,
            "high": pred_df["0.9"].values,
            "pred_df": pred_df,
        }


def to_chronos2_context(
    df: pd.DataFrame,
    target_col: str = "sale_qty",
    item_id: str = "gfk_sales",
) -> pd.DataFrame:
    """Convert DataFrame to Chronos-2 long-format context."""
    context = df[["period", target_col]].copy()
    context = context.rename(columns={"period": "timestamp", target_col: "target"})
    context["item_id"] = item_id
    return context[["item_id", "timestamp", "target"]]


# =============================================================================
# FastAPI App
# =============================================================================

_forecaster: Chronos2Forecaster | None = None


def get_forecaster() -> Chronos2Forecaster:
    global _forecaster
    if _forecaster is None:
        _forecaster = Chronos2Forecaster()
        _forecaster.load_model()
    return _forecaster


class ForecastRequest(BaseModel):
    values: list[float] = Field(..., description="Time series values")
    prediction_length: int = Field(1, ge=1, le=24, description="Steps to forecast")


class ForecastPoint(BaseModel):
    index: int
    median: float
    low: float
    high: float


class ForecastResponse(BaseModel):
    historical: list[dict]
    forecast: list[ForecastPoint]


def values_to_context_df(values: list[float]) -> pd.DataFrame:
    if not values:
        raise ValueError("values cannot be empty")
    n = len(values)
    periods = pd.date_range(start="2020-01-01", periods=n, freq="MS")
    df = pd.DataFrame({"period": periods, "sale_qty": values})
    return df


app = FastAPI(title="Chronos-2 Zero-Shot Demo", version="1.0.0")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.post("/api/forecast", response_model=ForecastResponse)
def forecast(req: ForecastRequest) -> ForecastResponse:
    if not req.values:
        raise HTTPException(status_code=400, detail="values cannot be empty")
    try:
        df = values_to_context_df(req.values)
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))
    context_df = to_chronos2_context(df, target_col="sale_qty", item_id="ts1")
    forecaster = get_forecaster()
    result = forecaster.forecast(context_df=context_df, prediction_length=req.prediction_length)
    historical = [{"index": i, "value": float(v)} for i, v in enumerate(req.values)]
    forecast_points = [
        ForecastPoint(
            index=len(req.values) + i,
            median=float(result["median"][i]),
            low=float(result["low"][i]),
            high=float(result["high"][i]),
        )
        for i in range(req.prediction_length)
    ]
    return ForecastResponse(historical=historical, forecast=forecast_points)


@app.get("/")
def index():
    index_path = STATIC_DIR / "index.html"
    if not index_path.exists():
        raise HTTPException(status_code=404, detail="index.html not found")
    return FileResponse(index_path)


app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")