"""
Crystal-Embedder-Orb Microservice

Computes Orb-v3 force field embeddings (1792-dim) for crystal structures.
Deployed on Hugging Face Spaces (CPU-only).

API:
    POST /embed
    Request: {"cif": "<CIF content>"}
    Response: {
        "vectors": {"orb": [...]},
        "dims": {"orb": 1792}
    }
"""

import os

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import numpy as np
import pandas as pd
from sklearn.preprocessing import normalize
from pymatgen.core import Structure

app = FastAPI(
    title="Crystal-Embedder-Orb",
    description="Orb-v3 force field embeddings for crystal structures",
    version="2.0.0",
)

# Global model instance (loaded at startup)
orb_model = None
models_loaded = False

ORB_DIM = 1792


class CifRequest(BaseModel):
    cif: str


class EmbedResponse(BaseModel):
    vectors: dict[str, list[float]]
    dims: dict[str, int]


class HealthResponse(BaseModel):
    status: str
    models_loaded: bool
    vector_dims: dict[str, int]


def load_models():
    """Load Orb-v3 featurizer at startup."""
    global orb_model, models_loaded

    print("Loading Orb-v3 (1792-dim)...")
    try:
        from mattervial.featurizers import ORBFeaturizer

        orb_model = ORBFeaturizer(model_name="ORB_v3", device="cpu")
        models_loaded = True
        print("Orb-v3 loaded successfully!")
    except Exception as e:
        print(f"Error loading Orb-v3: {e}")
        raise


@app.on_event("startup")
async def startup_event():
    """Load models when the server starts."""
    load_models()


@app.get("/health", response_model=HealthResponse)
async def health_check():
    """Health check endpoint."""
    return HealthResponse(
        status="healthy" if models_loaded else "loading",
        models_loaded=models_loaded,
        vector_dims={"orb": ORB_DIM},
    )


@app.post("/embed", response_model=EmbedResponse)
async def embed_structure(req: CifRequest):
    """
    Compute Orb-v3 embedding for a CIF structure.

    Returns an L2-normalized 1792-dim force field embedding as a named vector.
    """
    if not models_loaded:
        raise HTTPException(status_code=503, detail="Model still loading, please retry")

    try:
        struct = Structure.from_str(req.cif, fmt="cif")
        s_series = pd.Series([struct])

        print("Computing Orb-v3 features...")
        vec_orb = orb_model.get_features(s_series).values[0]

        vec_orb = np.nan_to_num(vec_orb, nan=0.0)
        vec_orb = normalize([vec_orb])[0]

        print(f"Embedding complete: orb={len(vec_orb)}")

        return EmbedResponse(
            vectors={"orb": vec_orb.tolist()},
            dims={"orb": ORB_DIM},
        )

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Embedding failed: {str(e)}")


@app.get("/")
async def root():
    """Root endpoint with API info."""
    return {
        "service": "Crystal-Embedder-Orb",
        "version": "2.0.0",
        "description": "Orb-v3 force field embeddings for crystal structures",
        "endpoints": {
            "/embed": "POST - Compute Orb-v3 embedding from CIF",
            "/health": "GET - Health check",
        },
        "vector_dimensions": {"orb": ORB_DIM},
    }