"""
main.py — FastAPI server for zai-org/GLM-OCR

Endpoints:
  GET  /         → Serves the frontend HTML
  GET  /health   → Liveness probe + model info
  POST /ocr      → Run OCR on uploaded image
  GET  /metrics  → Session-level stats
"""

import logging
import time
from contextlib import asynccontextmanager
from pathlib import Path

import uvicorn
from fastapi import FastAPI, File, Form, HTTPException, UploadFile, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse, JSONResponse
from pydantic import BaseModel
from typing import Annotated

from ocr_engine import engine, OcrResult, OcrMode

# ── Logging ─────────────────────────────────────────────────────────────────

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s | %(levelname)-8s | %(name)s — %(message)s",
    datefmt="%H:%M:%S",
)
logger = logging.getLogger(__name__)

# ── Session metrics ─────────────────────────────────────────────────────────

class SessionMetrics:
    def __init__(self):
        self.total_requests = 0
        self.total_words    = 0
        self.total_chars    = 0
        self.total_ms       = 0.0
        self.errors         = 0
        self.started_at     = time.time()

    def record(self, result: OcrResult):
        self.total_requests += 1
        self.total_words    += result.word_count
        self.total_chars    += result.char_count
        self.total_ms       += result.latency_ms

    def to_dict(self) -> dict:
        avg = self.total_ms / self.total_requests if self.total_requests else 0
        return {
            "total_requests":        self.total_requests,
            "total_words_extracted": self.total_words,
            "total_chars_extracted": self.total_chars,
            "avg_latency_ms":        round(avg, 1),
            "error_count":           self.errors,
            "uptime_seconds":        round(time.time() - self.started_at, 1),
        }

metrics = SessionMetrics()

# ── Lifespan ─────────────────────────────────────────────────────────────────

@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.info("🚀 Starting up — loading GLM-OCR model …")
    engine.load()
    logger.info("✅ Model ready.")
    yield
    logger.info("🛑 Shutting down …")
    engine.unload()

# ── App ──────────────────────────────────────────────────────────────────────

app = FastAPI(
    title="GLM-OCR API",
    description="Self-hosted OCR backend powered by zai-org/GLM-OCR",
    version="1.0.0",
    lifespan=lifespan,
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["GET", "POST"],
    allow_headers=["*"],
)

# ── Schemas ───────────────────────────────────────────────────────────────────

class OcrResponse(BaseModel):
    success:    bool
    text:       str
    word_count: int
    char_count: int
    latency_ms: float
    mode:       str
    model_id:   str
    device:     str

# ── Routes ────────────────────────────────────────────────────────────────────

@app.get("/", include_in_schema=False)
async def serve_frontend():
    frontend = Path(__file__).parent / "frontend" / "index.html"
    if not frontend.exists():
        return JSONResponse({"message": "Frontend not found."}, 404)
    return FileResponse(str(frontend))


@app.get("/health")
async def health():
    return {
        "status": "ok" if engine.loaded else "loading",
        "model":  engine.info,
    }


@app.post("/ocr", response_model=OcrResponse)
async def run_ocr(
    file: Annotated[UploadFile, File(description="Image file (PNG, JPG, WEBP, BMP, TIFF)")],
    mode: Annotated[OcrMode,    Form(description="'recognize' for plain text · 'parse' for structured markdown")] = "recognize",
):
    """
    Run GLM-OCR on an uploaded image.

    **mode options:**
    - `recognize` — extracts raw text, preserves layout (default)
    - `parse` — returns structured markdown (headers, tables, lists)
    """
    allowed = {"image/png", "image/jpeg", "image/webp", "image/gif", "image/bmp", "image/tiff"}
    if file.content_type and file.content_type not in allowed:
        raise HTTPException(status_code=415, detail=f"Unsupported file type: {file.content_type}")

    image_bytes = await file.read()
    if not image_bytes:
        raise HTTPException(status_code=400, detail="Empty file.")
    if len(image_bytes) > 20 * 1024 * 1024:
        raise HTTPException(status_code=413, detail="File too large. Max 20 MB.")

    logger.info(f"OCR | file={file.filename} size={len(image_bytes)/1024:.1f}KB mode={mode}")

    try:
        result = engine.run(image_bytes, mode=mode)
    except ValueError as e:
        metrics.errors += 1
        raise HTTPException(status_code=422, detail=str(e))
    except Exception as e:
        metrics.errors += 1
        logger.exception("Inference error")
        raise HTTPException(status_code=500, detail=f"Inference failed: {e}")

    metrics.record(result)
    logger.info(f"Done | {result.word_count} words | {result.latency_ms:.0f}ms")

    return OcrResponse(
        success    = True,
        text       = result.text,
        word_count = result.word_count,
        char_count = result.char_count,
        latency_ms = result.latency_ms,
        mode       = result.mode,
        model_id   = result.model_id,
        device     = result.device,
    )


@app.get("/metrics")
async def get_metrics():
    return metrics.to_dict()


@app.exception_handler(Exception)
async def global_handler(request: Request, exc: Exception):
    logger.exception(f"Unhandled: {request.url}")
    return JSONResponse(status_code=500, content={"detail": "Internal server error"})


if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=False)