"""
app.py — Web backend for the Afaan Oromo grammar checker.

Serves a single unified list of issues (rule-based + deep-learning), each
with absolute character offsets into the submitted text, so the frontend
can underline exact spans and show click-to-accept suggestion cards,
Grammarly-style.

Run:
    pip install -r requirements.txt
    uvicorn app:app --reload --port 8000
Then open http://localhost:8000
"""

from __future__ import annotations

import os
from typing import List, Optional

from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel

from rule_checker import OromoGrammarChecker, split_sentences_with_offsets
from dl_checker import DeepLearningChecker, DEFAULT_MODEL

app = FastAPI(title="Afaan Oromo Grammar Checker")

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
app.mount("/static", StaticFiles(directory=os.path.join(BASE_DIR, "static")), name="static")
templates = Jinja2Templates(directory=os.path.join(BASE_DIR, "templates"))


@app.middleware("http")
async def no_cache_static(request: Request, call_next):
    response = await call_next(request)
    if request.url.path.startswith("/static/"):
        response.headers["Cache-Control"] = "no-store, no-cache, must-revalidate"
    return response

rule_checker = OromoGrammarChecker()

_dl_checker: Optional[DeepLearningChecker] = None


def get_dl_checker() -> DeepLearningChecker:
    global _dl_checker
    if _dl_checker is None:
        model_name = os.environ.get("OROMO_DL_MODEL", DEFAULT_MODEL)
        _dl_checker = DeepLearningChecker(model_name=model_name)
    return _dl_checker


class CheckRequest(BaseModel):
    text: str
    use_dl: bool = True


class IssueOut(BaseModel):
    id: str
    start: int
    end: int
    text: str
    category: str
    severity: str
    message: str
    suggestions: List[str] = []
    source: str  # "rule" or "model"


class CheckResponse(BaseModel):
    issues: List[IssueOut]
    dl_available: bool
    dl_error: Optional[str] = None


@app.get("/", response_class=HTMLResponse)
async def index(request: Request):
    return templates.TemplateResponse(request, "index.html", {})


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


@app.post("/api/check", response_model=CheckResponse)
async def check(req: CheckRequest):
    text = req.text
    issues: List[IssueOut] = []

    # --- Rule-based layer ---
    rule_issues = rule_checker.check_text(text)
    for i, issue in enumerate(rule_issues):
        issues.append(
            IssueOut(
                id=f"rule-{i}",
                start=issue.start,
                end=issue.end,
                text=issue.text,
                category=issue.category,
                severity=issue.severity.value,
                message=issue.message,
                suggestions=issue.suggestions,
                source="rule",
            )
        )

    # --- Deep learning layer ---
    dl_available = False
    dl_error = None
    if req.use_dl:
        checker = get_dl_checker()
        dl_available = checker.available
        dl_error = checker.load_error
        if dl_available:
            sentences_with_offsets = split_sentences_with_offsets(text)
            scored = checker.check_sentences_with_offsets(sentences_with_offsets)
            for j, (sent_score, base_offset) in enumerate(scored):
                for k, tok in enumerate(sent_score.tokens):
                    if not tok.is_suspicious:
                        continue
                    abs_start = base_offset + tok.start
                    abs_end = base_offset + tok.end
                    # Skip if a rule-based issue already covers this exact span
                    # to avoid redundant duplicate cards for the same word.
                    if any(iss.start == abs_start and iss.end == abs_end for iss in issues):
                        continue
                    issues.append(
                        IssueOut(
                            id=f"dl-{j}-{k}",
                            start=abs_start,
                            end=abs_end,
                            text=tok.word,
                            category="model-flagged",
                            severity="warning",
                            message=(
                                f"The language model finds this word statistically "
                                f"unusual in context (probability {tok.original_prob*100:.1f}%, "
                                f"rank #{tok.original_rank})."
                            ),
                            suggestions=tok.top_suggestions,
                            source="model",
                        )
                    )

    issues.sort(key=lambda x: x.start)

    return CheckResponse(issues=issues, dl_available=dl_available, dl_error=dl_error)