"""General media ingest and honest Gaussian-preview workbench."""

from __future__ import annotations

import hashlib
import json
import shutil
import subprocess
import sys
import tempfile
import time
from pathlib import Path

for _stream in (sys.stdout, sys.stderr):
    if hasattr(_stream, "reconfigure"):
        _stream.reconfigure(encoding="utf-8", errors="replace")

import gradio as gr
import numpy as np
from PIL import Image

from core import image_billboard_points, write_preview_ply


def _paths(items):
    if not items:
        return []
    if isinstance(items, (str, Path)):
        return [str(items)]
    return [str(getattr(item, "path", None) or getattr(item, "name", item)) for item in items]


def _hash_file(path):
    digest = hashlib.sha256()
    size = 0
    with Path(path).open("rb") as handle:
        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
            digest.update(chunk)
            size += len(chunk)
    return {"name": Path(path).name, "bytes": size, "sha256": digest.hexdigest()}


def _sample_video(video_path: str, max_frames: int = 8) -> list[Path]:
    """Extract a bounded set of frames using the FFmpeg bundled by Spaces."""
    frame_dir = Path(tempfile.mkdtemp(prefix="simamanything2gs_"))
    pattern = frame_dir / "frame_%02d.jpg"
    try:
        subprocess.run(
            ["ffmpeg", "-hide_banner", "-loglevel", "error", "-i", str(video_path),
             "-vf", "fps=10", "-frames:v", str(max_frames), "-q:v", "3", str(pattern)],
            check=True,
            capture_output=True,
            text=True,
            timeout=120,
        )
    except FileNotFoundError as exc:
        raise gr.Error("FFmpeg is unavailable; this Space cannot sample video frames yet.") from exc
    except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as exc:
        detail = getattr(exc, "stderr", "") or "invalid or unreadable video"
        raise gr.Error(f"Video frame extraction failed: {str(detail).strip()[-300:]}") from exc
    frames = sorted(frame_dir.glob("frame_*.jpg"))
    if not frames:
        raise gr.Error("No readable frames were found in the uploaded video.")
    return frames


def ingest(images, videos, mode):
    image_paths, video_paths = _paths(images), _paths(videos)
    if not image_paths and not video_paths:
        raise gr.Error("Add at least one image or video.")
    sampled_frames = []
    if image_paths:
        first_path = image_paths[0]
    else:
        sampled_frames = _sample_video(video_paths[0])
        first_path = sampled_frames[0]
    first = Image.open(first_path).convert("RGB")
    if sampled_frames:
        shutil.rmtree(sampled_frames[0].parent, ignore_errors=True)
    points, colors = image_billboard_points(first)
    out_dir = Path("output")
    out_dir.mkdir(exist_ok=True)
    ply_path = write_preview_ply(out_dir / "simamanything2gs_preview.ply", points, colors)
    manifest = {
        "project": "SimamAnything2GS",
        "version": "0.1.0",
        "created_at_unix": int(time.time()),
        "mode": mode,
        "images": [_hash_file(path) for path in image_paths],
        "videos": [_hash_file(path) for path in video_paths],
        "sampled_frame_count": len(sampled_frames),
        "sampling": "FFmpeg fps=10, max 8 frames" if sampled_frames else "not needed",
        "preview_points": int(len(points)),
        "preview_type": "2.5D image billboard; not a learned 3D reconstruction",
        "limitations": [
            "The preview places pixels from the first image or sampled video frame on a single plane at z=1.",
            "No depth, camera pose, occlusion completion, or Gaussian optimization is claimed.",
            "Real image/video-to-Gaussian backends must be evaluated separately on held-out views.",
        ],
    }
    manifest_path = out_dir / "simamanything2gs_manifest.json"
    manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
    return str(ply_path), str(manifest_path), json.dumps(manifest, indent=2), "Preview exported; use it only as an ingest/export smoke test."


with gr.Blocks(title="SimamAnything2GS") as demo:
    gr.Markdown("# SimamAnything2GS\nImage/video ingest toward Gaussian reconstruction")
    gr.Markdown(
        "This first slice accepts arbitrary media and exports a deterministic 2.5D Gaussian-style preview. "
        "Depth, pose estimation, and learned Gaussian optimization are explicit future adapters."
    )
    with gr.Row():
        with gr.Column():
            images = gr.Files(label="Images", file_types=["image"], file_count="multiple", type="filepath")
            videos = gr.Files(label="Optional videos", file_types=["video"], file_count="multiple", type="filepath")
            mode = gr.Dropdown(["CPU ingest preview", "Future depth + pose backend"], value="CPU ingest preview", label="Pipeline mode")
            run = gr.Button("Build Gaussian preview", variant="primary")
        with gr.Column():
            ply = gr.File(label="Preview PLY")
            manifest = gr.File(label="Manifest JSON")
            report = gr.Code(label="Run report", language="json")
            status = gr.Markdown()
    run.click(ingest, [images, videos, mode], [ply, manifest, report, status])


if __name__ == "__main__":
    demo.launch(show_error=True)