Update app.py

app.py

@@ -10,11 +10,6 @@ import moviepy.editor as mp
 import os
 import zipfile
 
-# local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
-# zip_ref = zipfile.ZipFile(local_zip, 'r')
-# zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
-# zip_ref.close()
-
 # Load face detector
 mtcnn = MTCNN(margin=14, keep_all=True, factor=0.7, device='cpu')
 
@@ -24,15 +19,6 @@ class DetectionPipeline:
 
     def __init__(self, detector, n_frames=None, batch_size=60, resize=None):
         """Constructor for DetectionPipeline class.
-
-        Keyword Arguments:
-            n_frames {int} -- Total number of frames to load. These will be evenly spaced
-                throughout the video. If not specified (i.e., None), all frames will be loaded.
-                (default: {None})
-            batch_size {int} -- Batch size to use with MTCNN face detector. (default: {32})
-            resize {float} -- Fraction by which to resize frames from original prior to face
-                detection. A value less than 1 results in downsampling and a value greater than
-                1 result in upsampling. (default: {None})
         """
         self.detector = detector
         self.n_frames = n_frames
@@ -153,22 +139,14 @@ def deepfakespredict(input_video):
 
 
 
-title="EfficientNetV2 Deepfakes Video Detector"
-description="Please upload videos responsibly and await the results in a gif "
+title="Group 2- EfficientNetV2 based Deepfake Video Detector"
+description='''Please upload videos responsibly and await the results in a gif. The approach in place includes breaking down the video into several frames followed by collecting
+the frames that contain a face. Once these frames are collected the trained model attempts to predict if the face is fake or real and contribute to a deepfake confidence. This confidence level eventually 
+determines if the video can be considered a fake or not.'''
             
-examples = [              
-                ['Video1-fake-1-ff.mp4'],
-                ['Video6-real-1-ff.mp4'],
-                ['Video3-fake-3-ff.mp4'],
-                ['Video8-real-3-ff.mp4'],
-                ['real-1.mp4'],
-                ['fake-1.mp4'],
-           ]
-           
 gr.Interface(deepfakespredict,
                      inputs = ["video"],
                      outputs=["text","text", gr.Video(label="Detected face sequence")],
                      title=title,
-                     description=description,
-                     examples=examples
+                     description=description
                      ).launch()