Update vit_model_test.py

vit_model_test.py

@@ -7,24 +7,35 @@ from PIL import Image
 import os
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, f1_score, average_precision_score
+from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, f1_score, average_precision_score, recall_score
 import matplotlib.pyplot as plt
 import seaborn as sns
-from sklearn.metrics import recall_score
-from vit_model_traning import labeling,CustomDataset
+import cv2  # ספריית OpenCV להצגת הוידאו
+from vit_model_traning import labeling, CustomDataset
 
 
-    
 def shuffle_and_split_data(dataframe, test_size=0.2, random_state=59):
-    # Shuffle the DataFrame
     shuffled_df = dataframe.sample(frac=1, random_state=random_state).reset_index(drop=True)
-    
-    # Split the DataFrame into train and validation sets
     train_df, val_df = train_test_split(shuffled_df, test_size=test_size, random_state=random_state)
-    
     return train_df, val_df    
 
 
+def play_animation(video_path):
+    cap = cv2.VideoCapture(video_path)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        cv2.imshow('Processing Animation', frame)
+
+        # Press 'q' to exit early
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
 if __name__ == "__main__":
     # Check for GPU availability
     device = torch.device('cuda')
@@ -32,9 +43,8 @@ if __name__ == "__main__":
     # Load the pre-trained ViT model and move it to GPU
     model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224').to(device)
 
-
-
     model.classifier = nn.Linear(model.config.hidden_size, 2).to(device)
+    
     # Define the image preprocessing pipeline
     preprocess = transforms.Compose([
         transforms.Resize((224, 224)),
@@ -57,6 +67,9 @@ if __name__ == "__main__":
     true_labels = []
     predicted_labels = []
 
+    # Play animation while processing
+    play_animation('https://huggingface.co/DataScienceProject/Vit/blob/main/0001-0120.mp4')  # מסלול לסרטון האנימציה שלך
+
     with torch.no_grad():
         for images, labels in test_loader:
             images, labels = images.to(device), labels.to(device)
@@ -74,7 +87,6 @@ if __name__ == "__main__":
     ap = average_precision_score(true_labels, predicted_labels)
     recall = recall_score(true_labels, predicted_labels)
 
-
     print(f"Test Accuracy: {accuracy:.2%}")
     print(f"Precision: {precision:.2%}")
     print(f"F1 Score: {f1:.2%}")
@@ -87,4 +99,7 @@ if __name__ == "__main__":
     plt.xlabel('Predicted Labels')
     plt.ylabel('True Labels')
     plt.title('Confusion Matrix')
-    plt.show()
+    plt.show()
+
+    # Play animation again if needed
+    # play_animation('path_to_your_animation.mp4')