Update app.py

app.py

@@ -6,16 +6,27 @@ import numpy as np
 from imutils import face_utils
 from torchvision import models, transforms
 from tempfile import NamedTemporaryFile
-
+import shutil
 # Load face detector and landmark predictor
 face_detector = dlib.get_frontal_face_detector()
-PREDICTOR_PATH = "./lib/shape_predictor_81_face_landmarks.dat"
+PREDICTOR_PATH = "./shape_predictor_81_face_landmarks.dat"
 face_predictor = dlib.shape_predictor(PREDICTOR_PATH)
 
+import torch
+import torchvision.models as models
+
+# Load pretrained ResNet-34 model
+resnet34 = models.resnet34(weights=models.ResNet34_Weights.IMAGENET1K_V1)
+resnet34.fc = torch.nn.Linear(resnet34.fc.in_features, 2)
+ckpt_path = "./resnet34.pkl"
+
+# Save model state dict
+torch.save(resnet34.state_dict(), ckpt_path)
+print(f"✅ Model saved at {ckpt_path}")
+
 # Load deepfake detection model
 model = models.resnet34()
 model.fc = torch.nn.Linear(model.fc.in_features, 2)
-ckpt_path = "./resnet34.pkl"
 model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
 model.eval()
 
@@ -73,11 +84,17 @@ def process_video(video_path: str):
     return output_path
 
 def gradio_interface(video_file):
+    if video_file is None:
+        return "Error: No video uploaded."
+
+    # Create a temporary file and copy the uploaded video content
     with NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
-        temp_file.write(video_file.read())
-        temp_path = temp_file.name
+        temp_file_path = temp_file.name
+        # Read the uploaded video file using its path
+        with open(video_file, "rb") as uploaded_file:
+            temp_file.write(uploaded_file.read())
 
-    output_path = process_video(temp_path)
+    output_path = process_video(temp_file_path)
     return output_path
 
 # Gradio UI