Update app.py

app.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 from facenet_pytorch import MTCNN, InceptionResnetV1
 import os
 import numpy as np
-from PIL import Image
+from PIL import Image as PILImage
 import zipfile
 import cv2
 from pytorch_grad_cam import GradCAM
@@ -104,8 +104,8 @@ def analyze_video(input_video: str):
     cap.release()
     
     # Calculate metrics
-    fake_percentage = (sum(frame_predictions) / len(frame_predictions)) * 100
-    avg_confidence = np.mean(frame_confidences)
+    fake_percentage = (sum(frame_predictions) / len(frame_predictions)) * 100 if frame_predictions else 0
+    avg_confidence = np.mean(frame_confidences) if frame_confidences else 0
     
     # Create graphs
     fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
@@ -159,12 +159,16 @@ def format_results(results):
     - Total Frames: {results['total_frames']}
     - Processed Frames: {results['processed_frames']}
     """
-
 @spaces.GPU
 def analyze_and_format(input_video):
     results = analyze_video(input_video)
     text_results = format_results(results)
-    return text_results, results['analysis_plot'], results['progress_bar']
+    
+    # Convert BytesIO to PIL Images
+    analysis_plot = PILImage.open(results['analysis_plot'])
+    progress_bar = PILImage.open(results['progress_bar'])
+    
+    return text_results, analysis_plot, progress_bar
 
 interface = gr.Interface(
     fn=analyze_and_format,