"""
Root-level FastAPI entrypoint (Hugging Face Spaces convention expects an
app.py at the repo root defining `app`).

This is a thin wrapper: the actual extraction logic lives in src/ (rules
extractor, trained ML model, ensemble predictor) so the core pipeline stays
testable and importable on its own; this file just wires it up as the
Space's entrypoint and defines the HTTP endpoints.

Run locally from the repo root:
    uvicorn app:app --reload --port 8000

On Hugging Face Docker Spaces, the Dockerfile's CMD runs this same module
on port 7860.
"""
import os
import sys

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
SRC_DIR = os.path.join(BASE_DIR, "src")
sys.path.insert(0, SRC_DIR)

MODEL_PATH = os.path.join(SRC_DIR, "model.joblib")

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

from rules_extractor import extract_attributes_rules
from predict import predict_ensemble, predict_ml

app = FastAPI(
    title="Product Attribute Extraction API",
    description="Converts unstructured product/fashion descriptions into structured attributes.",
    version="1.0.0",
)


class ExtractRequest(BaseModel):
    text: str = Field(..., min_length=1, description="Raw product description text")
    mode: str = Field(
        "ensemble",
        description="Extraction mode: 'ensemble' (default, rules+ml), 'ml', or 'rules'",
    )


class ExtractResponse(BaseModel):
    text: str
    attributes: dict
    mode: str


from fastapi.responses import RedirectResponse

@app.get("/")
def root():
    return RedirectResponse(url="/docs")


@app.get("/health")
def health():
    return {"status": "ok"}


@app.post("/extract", response_model=ExtractResponse)
def extract(req: ExtractRequest):
    text = req.text.strip()
    if not text:
        raise HTTPException(status_code=400, detail="text must not be empty")

    if req.mode == "rules":
        attrs = extract_attributes_rules(text)
    elif req.mode == "ml":
        attrs = predict_ml(text, model_path=MODEL_PATH)
    elif req.mode == "ensemble":
        attrs = predict_ensemble(text, model_path=MODEL_PATH)
    else:
        raise HTTPException(status_code=400, detail="mode must be one of: ensemble, ml, rules")

    return ExtractResponse(text=text, attributes=attrs, mode=req.mode)