"""Flask web app for crack detection and measurement."""

from __future__ import annotations

import base64
import io
import os

import cv2
import numpy as np
from flask import Flask, jsonify, render_template, request, send_file
from PIL import Image

import aruco_scale
import crack_pipeline

app = Flask(__name__)
app.config["MAX_CONTENT_LENGTH"] = 25 * 1024 * 1024  # 25 MB upload cap


def _read_image(file_storage) -> np.ndarray:
    """Decode an uploaded file into an RGB numpy array."""
    image = Image.open(io.BytesIO(file_storage.read())).convert("RGB")
    return np.array(image)


def _encode_png(image_rgb: np.ndarray) -> str:
    """Encode an RGB array as a base64 PNG data URI."""
    bgr = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)
    ok, buf = cv2.imencode(".png", bgr)
    if not ok:
        raise RuntimeError("PNG encoding failed")
    return "data:image/png;base64," + base64.b64encode(buf).decode("ascii")


@app.route("/")
def index():
    return render_template("index.html")


@app.route("/health")
def health():
    return jsonify(status="ok")


@app.route("/marker")
def marker():
    """Serve the printable ArUco marker PDF (generated by make_marker.py)."""
    size = int(aruco_scale.DEFAULT_MARKER_LENGTH_MM)
    pdf = os.path.join(os.path.dirname(__file__), f"aruco_marker_{size}mm_A4.pdf")
    if not os.path.exists(pdf):
        return jsonify(error="Marker PDF missing - run: python make_marker.py"), 404
    return send_file(pdf, mimetype="application/pdf")


@app.route("/analyze", methods=["POST"])
def analyze():
    """Analyse an upload and return only the computer-vision result.

    Scale always comes from the ArUco marker (no manual options); the model
    threshold is fixed. The response carries the CV overlay and the CV
    measurement (intensity-based, refined within the ML-detected region).
    """
    if "image" not in request.files or request.files["image"].filename == "":
        return jsonify(error="No image uploaded"), 400

    try:
        image_rgb = _read_image(request.files["image"])
    except Exception:
        return jsonify(error="Could not read the uploaded image"), 400

    result = crack_pipeline.analyze(
        image_rgb, use_aruco=True,
        marker_length_mm=aruco_scale.DEFAULT_MARKER_LENGTH_MM,
    )
    overlay = result["overlay"]

    return jsonify(
        overlay=_encode_png(overlay),
        measurements=result["measurements"],
        aruco=result["aruco"],
        image_size={"width": overlay.shape[1], "height": overlay.shape[0]},
    )


if __name__ == "__main__":
    # Port 5000 is taken by macOS AirPlay Receiver, so default to 5001.
    port = int(os.environ.get("PORT", 5001))
    print("Loading crack segmentation model...")
    crack_pipeline.get_model()
    print(f"Model ready. Open http://127.0.0.1:{port}")
    app.run(host="127.0.0.1", port=port, debug=False)