| from fastapi import FastAPI, File, UploadFile, HTTPException |
| from fastapi.staticfiles import StaticFiles |
| from fastapi.responses import FileResponse, JSONResponse |
| from fastapi.middleware.cors import CORSMiddleware |
| import uvicorn |
| from datetime import datetime |
| from pathlib import Path |
| from backend.detectors.image_detector import analyze_image_models |
| from backend.detectors.metadata_detector import analyze_metadata |
| from backend.detectors.scoring_engine import calculate_final_score |
| from backend.detectors.style_detector import analyze_visual_style |
| from PIL import Image |
| import io |
|
|
| app = FastAPI(title="ImgAuth AI API") |
| app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]) |
|
|
| BASE_DIR = Path(__file__).parent |
| FRONTEND = BASE_DIR.parent / "frontend" |
| PUBLIC = BASE_DIR.parent / "public" |
| app.mount("/static", StaticFiles(directory=str(FRONTEND / "static")), name="static") |
| app.mount("/public", StaticFiles(directory=str(PUBLIC)), name="public") |
|
|
|
|
| @app.on_event("startup") |
| async def startup(): |
| print("ImgAuth AI started -> http://localhost:5000") |
| try: |
| print("[ImgAuth] Preloading models on startup...") |
| from backend.detectors.image_detector import load_models |
| load_models() |
| print("[ImgAuth] Models preloaded successfully.") |
| except Exception as e: |
| print(f"[ImgAuth] Warning: failed to preload models: {e}") |
|
|
|
|
| @app.get("/") |
| async def root(): |
| return FileResponse(str(FRONTEND / "index.html")) |
|
|
|
|
| @app.post("/api/detect") |
| async def detect(file: UploadFile = File(...)): |
| if not file.content_type.startswith("image/"): |
| raise HTTPException(400, "Only image files are accepted.") |
| contents = await file.read() |
| if len(contents) > 10 * 1024 * 1024: |
| raise HTTPException(400, "File too large. Max 10 MB.") |
|
|
| fn = {} |
| md = analyze_metadata(contents) |
| ml = analyze_image_models(contents) |
|
|
| try: |
| img_full = Image.open(io.BytesIO(contents)).convert("RGB") |
| st = analyze_visual_style(img_full) |
| except Exception as e: |
| st = {"ai_points": 0, "real_points": 0, "signals": [f"[ERROR] Style loading: {str(e)[:50]}"]} |
|
|
| ml["style"] = st |
| if "signals" in ml and "signals" in st: |
| ml["signals"].extend(st["signals"]) |
|
|
| res = calculate_final_score(fn, md, ml) |
|
|
| return JSONResponse({ |
| "filename": file.filename, |
| "verdict": res["verdict"], |
| "ai_score": res["ai_score"], |
| "real_score": res["real_score"], |
| "confidence": res["confidence"], |
| "color": res["color"], |
| "layers": {"filename": fn, "metadata": md, "models": ml}, |
| "breakdown": res["breakdown"], |
| "summary": res["summary"], |
| "timestamp": datetime.now().isoformat() |
| }) |
|
|
