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.gitignore

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+*.pth

app.py

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+# from google.colab import drive
+# drive.mount('/content/drive')
+
+# import gradio as gr
+import gradio as gr
+import webbrowser
+from threading import Timer
+import torch
+import torch.nn.functional as F
+from facenet_pytorch import InceptionResnetV1
+import cv2
+from PIL import Image
+import numpy as np
+import warnings
+warnings.filterwarnings("ignore")
+
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model = InceptionResnetV1(pretrained="vggface2", classify=True, num_classes=1).to(DEVICE).eval()
+
+# checkpoint_path = "/content/drive/MyDrive/resnetinceptionv1_epoch_32.pth"
+checkpoint_path = "resnetinceptionv1_epoch_32.pth"
+checkpoint = torch.load(checkpoint_path, map_location=DEVICE)
+if 'model_state_dict' in checkpoint:
+    state_dict = checkpoint['model_state_dict']
+else:
+    state_dict = checkpoint
+
+try:
+    model.load_state_dict(state_dict)
+    print("Model weights loaded successfully.")
+except RuntimeError as e:
+    print(f"Error loading model weights: {e}")
+
+def create_montage(frames, size=(512, 512)):
+    """Create a montage from selected frames."""
+    montage = Image.new('RGB', size)
+    num_images = len(frames)
+    montage_grid = int(np.ceil(np.sqrt(num_images)))
+    thumb_size = (size[0] // montage_grid, size[1] // montage_grid)
+
+    for i, frame in enumerate(frames):
+        thumbnail = frame.resize(thumb_size, Image.ANTIALIAS)
+        x_offset = (i % montage_grid) * thumb_size[0]
+        y_offset = (i // montage_grid) * thumb_size[1]
+        montage.paste(thumbnail, (x_offset, y_offset))
+
+    return montage
+
+def predict(input_video):
+    cap = cv2.VideoCapture(input_video)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    selected_frames = []
+    frame_indices = np.linspace(0, total_frames - 1, 9, dtype=int)
+
+    for i in range(total_frames):
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if i in frame_indices:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            selected_frames.append(Image.fromarray(frame))
+
+    cap.release()
+
+    video_label = "Fake" if np.random.rand() > 0.5 else "Real"
+    fake_ratio = np.random.rand()
+    detail = f"Placeholder ratio: {fake_ratio*100:.2f}%"
+
+    montage = create_montage(selected_frames)
+
+    return video_label, detail, montage
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Video(label="Input Video"),
+    outputs=[
+        gr.Text(label="Classification"),
+        gr.Text(label="Details"),
+        gr.Image(label="Montage of Selected Frames")
+    ],
+).launch(debug=True, share=True)