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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "-b", "0.0.0.0:7860", "main:app"]

app.py

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+
+from fastapi import FastAPI, UploadFile, Form, File
+from fastapi.responses import JSONResponse
+from PIL import Image
+import io
+import torch
+import torch.nn.functional as F
+from facenet_pytorch import MTCNN, InceptionResnetV1
+import numpy as np
+import cv2
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+
+app = FastAPI()
+
+DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+mtcnn = MTCNN(
+    select_largest=False,
+    post_process=False,
+    device=DEVICE
+).to(DEVICE).eval()
+
+model = InceptionResnetV1(
+    pretrained="vggface2",
+    classify=True,
+    num_classes=1,
+    device=DEVICE
+)
+
+checkpoint = torch.load("resnetinceptionv1_epoch_32.pth", map_location=torch.device('cpu'))
+model.load_state_dict(checkpoint['model_state_dict'])
+model.to(DEVICE)
+model.eval()
+
+@app.get("/")
+async def read_root():
+    return {"message": "Welcome to DeepFake Detection API"}
+
+@app.post("/predict/")
+async def predict(image: UploadFile = File(...), true_label: str = Form(...)):
+    try:
+        contents = await image.read()
+        pil_image = Image.open(io.BytesIO(contents))
+        
+        face = mtcnn(pil_image)
+        if face is None:
+            return JSONResponse(status_code=400, content={"message": "No face detected"})
+
+        face = face.unsqueeze(0)
+        face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)
+        face = face.to(DEVICE, dtype=torch.float32) / 255.0
+
+        target_layers=[model.block8.branch1[-1]]
+        cam = GradCAM(model=model, target_layers=target_layers)
+        targets = [ClassifierOutputTarget(0)]
+
+        grayscale_cam = cam(input_tensor=face, targets=targets, eigen_smooth=True)
+        grayscale_cam = grayscale_cam[0, :]
+        visualization = show_cam_on_image(face.squeeze(0).permute(1, 2, 0).cpu().detach().numpy(), grayscale_cam, use_rgb=True)
+        face_with_mask = cv2.addWeighted(face.squeeze(0).permute(1, 2, 0).cpu().detach().numpy().astype('uint8'), 1, visualization, 0.5, 0)
+
+        with torch.no_grad():
+            output = torch.sigmoid(model(face).squeeze(0))
+            prediction = "real" if output.item() < 0.5 else "fake"
+            
+            real_prediction = 1 - output.item()
+            fake_prediction = output.item()
+            
+            confidences = {
+                'real': real_prediction,
+                'fake': fake_prediction
+            }
+
+            # Determine final prediction based on confidence scores
+            final_prediction = "real" if real_prediction > fake_prediction else "fake"
+        
+        return {
+            'confidences': confidences,
+            'true_label': true_label,
+            'final_prediction': final_prediction,
+            'face_with_mask': face_with_mask.tolist()  # Convert numpy array to list for JSON serialization
+        }
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"message": str(e)})
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+gradio
+Pillow
+facenet-pytorch
+torch
+opencv-python
+grad-cam
+fastapi

resnetinceptionv1_epoch_32.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:794ebe83c6a7d7959c30c175030b4885e2b9fa175f1cc3e582236595d119f52b
+size 282395989