Update app.py

app.py

@@ -18,6 +18,7 @@ zip_ref.close()
 # Load face detector
 mtcnn = MTCNN(margin=14, keep_all=True, factor=0.7, device='cpu')
 
+#Facial Detection function, Reference: (Timesler, 2020); Source link: https://www.kaggle.com/timesler/facial-recognition-model-in-pytorch
 class DetectionPipeline:
     """Pipeline class for detecting faces in the frames of a video file."""
 
@@ -138,24 +139,22 @@ def deepfakespredict(input_video):
     else:
         text = "The video is REAL."
 
-    face_frames = []
-
-    for face in faces:
-        face_frame = Image.fromarray(face.astype('uint8'), 'RGB')
-        face_frames.append(face_frame)
+    face_frames = faces.astype(np.uint8)
 
     face_frames[0].save('results.gif', save_all=True, append_images=face_frames[1:], duration = 250, loop = 100 )
     clip = mp.VideoFileClip("results.gif")
-    clip.write_videofile("myvideo.mp4")
+    clip.write_videofile("video.mp4")
 
-    return text, text2, "myvideo.mp4"
+    return text, text2, "video.mp4"
 
 
 title="EfficientNetV2 Deepfakes Video Detector"
-description="This is a demo implementation of Deepfakes Video Detector by using EfficientNetV2 on frame-by-frame detection. The accuracy may not be accurate. To use it, simply upload your video, or click one of the examples to load them."
-
-examples = [
-               
+description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector by using frame-by-frame detection. \
+            To use it, simply upload your video, or click one of the examples to load them.\
+            This demo and model represent the work of \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by Lee Sheng Yeh. \
+            The samples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \
+            "
+examples = [              
                 ['Video1-fake-1-ff.mp4'],
                 ['Video6-real-1-ff.mp4'],
                 ['Video3-fake-3-ff.mp4'],
@@ -164,11 +163,10 @@ examples = [
                 ['fake-1.mp4'],
            ]
            
-demo = gr.Interface(deepfakespredict,
+gr.Interface(deepfakespredict,
                      inputs = ["video"],
                      outputs=["text","text", gr.outputs.Video(label="Detected face sequence")],
                      title=title,
                      description=description,
                      examples=examples
-                     )
-demo.launch()
+                     ).launch()