server.py

"""
SURPRISE backend — FastAPI server.

Endpoints:
  GET  /          → service info
  GET  /health    → health check
  POST /analyze   → analyze a video for AI-generated content

The model backend is selected via env var MODEL_BACKEND (default: "mock").
Set MODEL_BACKEND=lewm and provide CHECKPOINT_PATH when you have a trained model.
"""

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

from fastapi import FastAPI, File, HTTPException, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse

from config import settings
from inference import get_inference_engine
from video_utils import extract_frames, validate_video

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
)
log = logging.getLogger("surprise")


@asynccontextmanager
async def lifespan(app: FastAPI):
    log.info("Starting SURPRISE backend")
    log.info(f"Backend: {settings.model_backend}")
    app.state.engine = get_inference_engine()
    log.info(f"Engine ready: {app.state.engine.__class__.__name__}")
    yield
    log.info("Shutting down")


app = FastAPI(
    title="SURPRISE",
    description="Deepfake detection via latent surprise (LeWorldModel)",
    version="0.1.0",
    lifespan=lifespan,
)

# CORS — allows the Vercel frontend to call this backend
app.add_middleware(
    CORSMiddleware,
    allow_origins=settings.allowed_origins,
    allow_origin_regex=settings.allowed_origin_regex,
    allow_methods=["GET", "POST"],
    allow_headers=["*"],
)


@app.get("/")
def root():
    return {
        "service": "surprise",
        "version": "0.1.0",
        "status": "ok",
        "backend": settings.model_backend,
        "endpoints": ["/health", "/analyze"],
    }


@app.get("/health")
def health():
    return {"status": "healthy", "backend": settings.model_backend}


@app.post("/analyze")
async def analyze(video: UploadFile = File(...)):
    """
    Analyze an uploaded video for AI-generated content.

    Request: multipart/form-data with field `video` (a video file).

    Response shape (JSON):
        {
          "verdict": "real" | "fake" | "uncertain",
          "label": "AUTHENTIC" | "AI-GENERATED" | "INCONCLUSIVE",
          "confidence": float [0,100],
          "surprise_score": float,
          "straightness": float,
          "per_frame_surprise": [float, ...],
          "flagged_frames": [int, ...],
          "flag_reason": str,
          "frame_count": int,
          "duration_seconds": float,
          "processing_time_ms": int,
          "backend": "mock" | "lewm"
        }
    """
    t0 = time.time()

    # Validate content type OR file extension (some clients omit content-type)
    video_exts = {".mp4", ".webm", ".mov", ".mkv", ".avi", ".m4v"}
    ext = Path(video.filename or "").suffix.lower()
    is_video_mime = video.content_type and video.content_type.startswith("video/")
    is_video_ext = ext in video_exts
    if not (is_video_mime or is_video_ext):
        raise HTTPException(
            400,
            f"File must be a video. Got content-type={video.content_type!r}, ext={ext!r}",
        )

    # Read & size check
    contents = await video.read()
    size_mb = len(contents) / (1024 * 1024)
    if size_mb > settings.max_file_size_mb:
        raise HTTPException(
            413,
            f"File too large: {size_mb:.1f}MB (max {settings.max_file_size_mb}MB)",
        )

    log.info(f"Received: {video.filename} ({size_mb:.2f}MB, {video.content_type})")

    # Save to temp & process
    suffix = Path(video.filename or "vid.mp4").suffix or ".mp4"
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(contents)
        tmp_path = tmp.name

    try:
        meta = validate_video(tmp_path)
        if meta["duration"] > settings.max_duration_seconds:
            raise HTTPException(
                413,
                f"Video too long: {meta['duration']:.1f}s "
                f"(max {settings.max_duration_seconds}s)",
            )

        frames = extract_frames(
            tmp_path,
            target_size=settings.input_resolution,
            max_frames=settings.max_frames,
            target_fps=settings.target_fps,
        )
        log.info(
            f"Extracted {len(frames)} frames "
            f"({meta['fps']:.1f}fps src, {meta['duration']:.1f}s)"
        )

        result = app.state.engine.analyze(frames)

        elapsed_ms = int((time.time() - t0) * 1000)
        return JSONResponse(
            {
                **result,
                "frame_count": len(frames),
                "duration_seconds": round(meta["duration"], 2),
                "processing_time_ms": elapsed_ms,
            }
        )

    except HTTPException:
        raise
    except ValueError as e:
        raise HTTPException(400, str(e))
    finally:
        try:
            os.unlink(tmp_path)
        except OSError:
            pass


@app.exception_handler(Exception)
async def general_exception_handler(request, exc):
    log.exception("Unhandled exception")
    return JSONResponse(
        status_code=500,
        content={"error": "internal_error", "message": str(exc)},
    )


if __name__ == "__main__":
    import uvicorn
    port = int(os.getenv("PORT", "8000"))
    uvicorn.run("server:app", host="0.0.0.0", port=port, reload=True)