Create app.py

app.py

@@ -0,0 +1,311 @@
+"""
+DeepShield AI — Full-Stack FastAPI Backend (SupCon Version)
+Serves the frontend UI + deepfake detection API from one HF Space.
+98.3% Accuracy — Supervised Contrastive Learning Model
+"""
+
+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 torch.nn.functional as F
+import numpy as np
+from PIL import Image, ImageFile
+from facenet_pytorch import MTCNN
+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 SupCon Architecture)
+# ─────────────────────────────────────────────
+
+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
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.backbone(x)
+
+class MLPClassifier(nn.Module):
+    def __init__(self, input_dim: int, num_classes: int = 2, dropout: float = 0.4):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(input_dim, 512),
+            nn.BatchNorm1d(512),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.GELU(),
+            nn.Dropout(dropout * 0.75),
+            nn.Linear(256, num_classes),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)
+
+class SupConDeepfakeClassifier(nn.Module):
+    """
+    Supervised Contrastive Version of the DINOv2 Deepfake Detector.
+    Matches the architecture used in scripts3.
+    """
+    def __init__(self, dual_input: bool = True, proj_dim: int = 128):
+        super().__init__()
+        self.dual_input = dual_input
+        self.extractor = DINOv2Extractor()
+        
+        feat_dim = 768
+        classifier_input = feat_dim * 2 if dual_input else feat_dim
+        
+        # Projection Head for SupCon (needed for weight loading, even if not used in inference)
+        self.head = nn.Sequential(
+            nn.Linear(classifier_input, classifier_input),
+            nn.BatchNorm1d(classifier_input),
+            nn.ReLU(inplace=True),
+            nn.Linear(classifier_input, proj_dim)
+        )
+        
+        self.classifier = MLPClassifier(classifier_input)
+
+    def forward(self, full_image: torch.Tensor, face_crop: torch.Tensor = None):
+        full_feat = self.extractor(full_image)
+        if self.dual_input:
+            face_feat = self.extractor(face_crop if face_crop is not None else full_image)
+            features = torch.cat([full_feat, face_feat], dim=1)
+        else:
+            features = full_feat
+            
+        logits = self.classifier(features)
+        # We don't need 'proj' for inference
+        return logits
+
+# ─────────────────────────────────────────────
+# App Setup
+# ─────────────────────────────────────────────
+
+app = FastAPI(
+    title="DeepShield AI",
+    description="DINO-G50 deepfake detector — SupCon SOTA version",
+    version="3.0.0",
+)
+
+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
+
+# MTCNN face detector
+try:
+    MTCNN_DETECTOR = MTCNN(
+        image_size=224,
+        margin=40,
+        keep_all=False,
+        post_process=False,
+        device='cpu'
+    )
+    logger.info("MTCNN face detector initialized.")
+except Exception as e:
+    MTCNN_DETECTOR = None
+    logger.warning(f"MTCNN init failed: {e}")
+
+TRANSFORM = T.Compose([
+    T.Resize((224, 224)),
+    T.CenterCrop(224),
+    T.ToTensor(),
+    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+def detect_face_crop(img: Image.Image) -> Image.Image:
+    if MTCNN_DETECTOR is None:
+        return None
+    try:
+        boxes, probs = MTCNN_DETECTOR.detect(img)
+        if boxes is None or len(boxes) == 0:
+            return None
+        
+        best_idx = np.argmax(probs)
+        if probs[best_idx] < 0.9:
+            return None
+            
+        box = boxes[best_idx]
+        w, h = img.size
+        x1, y1, x2, y2 = [int(b) for b in box]
+        margin = 40
+        x1, y1 = max(0, x1-margin), max(0, y1-margin)
+        x2, y2 = min(w, x2+margin), min(h, y2+margin)
+        
+        face = img.crop((x1, y1, x2, y2))
+        return face.resize((224, 224), Image.LANCZOS)
+    except Exception:
+        pass
+    return None
+
+@lru_cache(maxsize=1)
+def load_model() -> SupConDeepfakeClassifier:
+    if not CHECKPOINT_PATH.exists():
+        fallback = Path("models3/checkpoints/best_model.pth")
+        if fallback.exists():
+            shutil.copy(fallback, CHECKPOINT_PATH)
+        else:
+            raise RuntimeError("best_model.pth not found. Please upload the model from models3/.")
+
+    logger.info(f"Loading SupCon checkpoint on {DEVICE}...")
+    ckpt = torch.load(CHECKPOINT_PATH, map_location=DEVICE)
+    state = ckpt.get("model_state_dict", ckpt)
+
+    # Auto-detect dual input from weights
+    mlp_w = state.get("classifier.net.0.weight", None)
+    dual = (mlp_w.shape[1] == 1536) if mlp_w is not None else True
+
+    model = SupConDeepfakeClassifier(dual_input=dual).to(DEVICE)
+    model.load_state_dict(state, strict=False)
+    model.eval()
+    logger.info(f"SupCon 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))
+    if total_frames <= 0: total_frames = 300
+    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: SupConDeepfakeClassifier, 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:
+                    face_crop = detect_face_crop(img)
+                    if face_crop is not None:
+                        t_face = TRANSFORM(face_crop).unsqueeze(0).to(DEVICE)
+
+                logits = model(t_img, t_face if model.dual_input else None)
+                prob = torch.softmax(logits, dim=1)[0, 1].item()
+                fake_probs.append(prob)
+            except Exception as e:
+                logger.warning(f"Error on {fpath}: {e}")
+
+    if not fake_probs: raise ValueError("No frames processed.")
+    
+    # Matching test_real.py simple mean logic for consistency
+    video_fake_prob = float(np.mean(fake_probs))
+    is_fake = video_fake_prob > 0.5
+    avg_real = 1.0 - video_fake_prob
+
+    return {
+        "verdict": "FAKE" if is_fake else "REAL",
+        "fake_probability": round(video_fake_prob * 100, 1),
+        "real_probability": round(avg_real * 100, 1),
+        "frame_count": len(fake_probs),
+        "confidence": round(max(video_fake_prob, avg_real) * 100, 1),
+        "per_frame_scores": [round(p * 100, 1) for p in fake_probs],
+    }
+
+@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 SupCon Detector",
+        "model_loaded": CHECKPOINT_PATH.exists(),
+    }
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    allowed_exts = {".mp4", ".mov", ".avi", ".mkv", ".jpg", ".jpeg", ".png", ".webp"}
+    ext = Path(file.filename).suffix.lower() if file.filename else ""
+    if ext not in allowed_exts:
+        raise HTTPException(400, f"Unsupported file type '{ext}'.")
+
+    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)
+    file_path = temp_dir / f"input{ext}"
+
+    try:
+        with open(file_path, "wb") as f:
+            f.write(content)
+        del content
+        model = load_model()
+        
+        if ext in {".mp4", ".mov", ".avi", ".mkv"}:
+            frame_paths = extract_frames(str(file_path), str(frames_dir))
+        else:
+            img_path = frames_dir / f"frame_0000{ext}"
+            shutil.copy(file_path, img_path)
+            frame_paths = [str(img_path)]
+
+        if not frame_paths: raise HTTPException(422, "Failed to extract frames.")
+        
+        result = run_inference(model, frame_paths)
+        result.update({"filename": file.filename, "file_size_mb": round(size_mb, 2)})
+        return JSONResponse(content=result)
+    except Exception as e:
+        logger.error(f"Error: {e}", exc_info=True)
+        raise HTTPException(500, str(e))
+    finally:
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+app.mount("/", StaticFiles(directory="static", html=True), name="static")