Delete app.py

app.py

@@ -1,284 +0,0 @@
-"""
-DeepShield AI — Full-Stack FastAPI Backend
-Serves the frontend UI + deepfake detection API from one HF Space.
-
-Routes:
-  GET  /          → Serves index.html (the web UI)
-  GET  /health    → JSON health check
-  POST /predict   → Video upload → REAL/FAKE prediction
-"""
-
-import os
-import sys
-import uuid
-import shutil
-import logging
-import tempfile
-from pathlib import Path
-from functools import lru_cache
-
-import cv2
-import torch
-import torch.nn as nn
-import numpy as np
-from PIL import Image, ImageFile
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, FileResponse
-from fastapi.staticfiles import StaticFiles
-import torchvision.transforms as T
-
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
-logger = logging.getLogger(__name__)
-
-# ─────────────────────────────────────────────
-# Model Definition (self-contained)
-# ─────────────────────────────────────────────
-
-class DINOv2Extractor(nn.Module):
-    def __init__(self, variant: str = "dinov2_vitb14"):
-        super().__init__()
-        logger.info(f"Loading {variant} from torch.hub...")
-        self.backbone = torch.hub.load(
-            "facebookresearch/dinov2", variant, pretrained=True
-        )
-        self.feature_dim = 768
-        for p in self.backbone.parameters():
-            p.requires_grad = False
-        logger.info("DINOv2 backbone loaded (frozen).")
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.backbone(x)
-
-
-class MLPClassifier(nn.Module):
-    def __init__(self, input_dim: int = 1536, num_classes: int = 2, dropout: float = 0.3):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, 512),
-            nn.LayerNorm(512),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Linear(512, 256),
-            nn.LayerNorm(256),
-            nn.GELU(),
-            nn.Dropout(dropout / 2),
-            nn.Linear(256, num_classes),
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.net(x)
-
-
-class DeepfakeDetector(nn.Module):
-    def __init__(self, dual_input: bool = True):
-        super().__init__()
-        self.dual_input = dual_input
-        self.extractor = DINOv2Extractor()
-        feat_dim = 1536 if dual_input else 768
-        self.classifier = MLPClassifier(input_dim=feat_dim)
-
-    def forward(self, full_img: torch.Tensor, face_img: torch.Tensor = None) -> torch.Tensor:
-        full_feat = self.extractor(full_img)
-        if self.dual_input and face_img is not None:
-            face_feat = self.extractor(face_img)
-            feats = torch.cat([full_feat, face_feat], dim=1)
-        else:
-            feats = full_feat
-        return self.classifier(feats)
-
-
-# ─────────────────────────────────────────────
-# App Setup
-# ─────────────────────────────────────────────
-
-app = FastAPI(
-    title="DeepShield AI",
-    description="DINO-G50 deepfake detector — full-stack web app",
-    version="2.0.0",
-)
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-CHECKPOINT_PATH = Path("best_model.pth")
-MAX_FRAMES = 20
-MAX_FILE_MB = 30
-MAX_DURATION_SEC = 60
-
-TRANSFORM = T.Compose([
-    T.Resize((224, 224)),
-    T.ToTensor(),
-    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
-
-
-@lru_cache(maxsize=1)
-def load_model() -> DeepfakeDetector:
-    if not CHECKPOINT_PATH.exists():
-        raise RuntimeError("best_model.pth not found. Upload it to this HF Space.")
-
-    logger.info(f"Loading checkpoint on {DEVICE}...")
-    ckpt = torch.load(CHECKPOINT_PATH, map_location=DEVICE)
-    state = ckpt.get("model_state_dict", ckpt)
-
-    mlp_w = state.get("classifier.net.0.weight", None)
-    dual = (mlp_w.shape[1] == 1536) if mlp_w is not None else True
-
-    model = DeepfakeDetector(dual_input=dual).to(DEVICE)
-    model.load_state_dict(state, strict=False)
-    model.eval()
-    logger.info(f"Model ready. dual_input={dual}, device={DEVICE}")
-    return model
-
-
-def extract_frames(video_path: str, output_dir: str, num_frames: int = MAX_FRAMES) -> list:
-    cap = cv2.VideoCapture(video_path)
-    if not cap.isOpened():
-        raise ValueError("Cannot open video file.")
-
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = cap.get(cv2.CAP_PROP_FPS) or 25
-    duration = total_frames / fps if fps > 0 else 0
-
-    if duration > MAX_DURATION_SEC:
-        cap.release()
-        raise ValueError(f"Video too long ({duration:.0f}s). Max: {MAX_DURATION_SEC}s.")
-
-    if total_frames <= 0:
-        total_frames = int(fps * MAX_DURATION_SEC)
-
-    step = max(1, total_frames // num_frames)
-    target_indices = set(range(0, total_frames, step))
-    saved_paths = []
-    frame_idx = 0
-
-    while len(saved_paths) < num_frames:
-        ret, frame = cap.read()
-        if not ret:
-            break
-        if frame_idx in target_indices:
-            rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            path = os.path.join(output_dir, f"frame_{len(saved_paths):04d}.jpg")
-            Image.fromarray(rgb).save(path, quality=90)
-            saved_paths.append(path)
-        frame_idx += 1
-
-    cap.release()
-    return saved_paths
-
-
-def run_inference(model: DeepfakeDetector, frame_paths: list) -> dict:
-    fake_probs = []
-    with torch.no_grad():
-        for fpath in frame_paths:
-            try:
-                img = Image.open(fpath).convert("RGB")
-                t_img = TRANSFORM(img).unsqueeze(0).to(DEVICE)
-                t_face = t_img if model.dual_input else None
-                logits = model(t_img, t_face)
-                prob = torch.softmax(logits, dim=1)[0, 1].item()
-                fake_probs.append(prob)
-            except Exception as e:
-                logger.warning(f"Skipping frame {fpath}: {e}")
-
-    if not fake_probs:
-        raise ValueError("No frames could be processed.")
-
-    avg_fake = float(np.mean(fake_probs))
-    avg_real = 1.0 - avg_fake
-
-    return {
-        "verdict": "FAKE" if avg_fake > 0.5 else "REAL",
-        "fake_probability": round(avg_fake * 100, 1),
-        "real_probability": round(avg_real * 100, 1),
-        "frame_count": len(fake_probs),
-        "confidence": round(max(avg_fake, avg_real) * 100, 1),
-        "per_frame_scores": [round(p * 100, 1) for p in fake_probs],
-    }
-
-
-# ─────────────────────────────────────────────
-# API Routes (must be defined BEFORE static mount)
-# ─────────────────────────────────────────────
-
-@app.on_event("startup")
-async def startup_event():
-    try:
-        load_model()
-    except Exception as e:
-        logger.error(f"Startup model load failed: {e}")
-
-
-@app.get("/health")
-def health_check():
-    return {
-        "status": "ok",
-        "model": "DINO-G50 Deepfake Detector",
-        "device": str(DEVICE),
-        "model_loaded": CHECKPOINT_PATH.exists(),
-    }
-
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    allowed_exts = {".mp4", ".mov", ".avi", ".mkv"}
-    ext = Path(file.filename).suffix.lower() if file.filename else ""
-
-    if ext not in allowed_exts:
-        raise HTTPException(400, f"Unsupported type '{ext}'. Use: {allowed_exts}")
-
-    content = await file.read()
-    size_mb = len(content) / (1024 * 1024)
-    if size_mb > MAX_FILE_MB:
-        raise HTTPException(413, f"File too large ({size_mb:.1f} MB). Max: {MAX_FILE_MB} MB.")
-
-    job_id = str(uuid.uuid4())[:8]
-    temp_dir = Path(tempfile.gettempdir()) / f"deepshield_{job_id}"
-    frames_dir = temp_dir / "frames"
-    frames_dir.mkdir(parents=True, exist_ok=True)
-    video_path = temp_dir / f"input{ext}"
-
-    try:
-        with open(video_path, "wb") as f:
-            f.write(content)
-        del content
-
-        model = load_model()
-        logger.info(f"[{job_id}] Processing: {file.filename} ({size_mb:.1f} MB)")
-
-        frame_paths = extract_frames(str(video_path), str(frames_dir))
-        if not frame_paths:
-            raise HTTPException(422, "No frames could be extracted from video.")
-
-        result = run_inference(model, frame_paths)
-        result["filename"] = file.filename
-        result["file_size_mb"] = round(size_mb, 2)
-        result["job_id"] = job_id
-
-        logger.info(f"[{job_id}] Result: {result['verdict']} ({result['fake_probability']}% fake)")
-        return JSONResponse(content=result)
-
-    except HTTPException:
-        raise
-    except ValueError as e:
-        raise HTTPException(422, str(e))
-    except Exception as e:
-        logger.error(f"[{job_id}] Error: {e}", exc_info=True)
-        raise HTTPException(500, f"Internal error: {str(e)}")
-    finally:
-        shutil.rmtree(temp_dir, ignore_errors=True)
-        logger.info(f"[{job_id}] Cleanup done.")
-
-
-# ─────────────────────────────────────────────
-# Static Frontend (mounted LAST — serves index.html at /)
-# ─────────────────────────────────────────────
-app.mount("/", StaticFiles(directory="static", html=True), name="static")