Update vit_Training.py

vit_Training.py

@@ -64,16 +64,27 @@ class Custom_VIT_Model:
         else:
             self.df = pd.DataFrame(columns=['image_path', 'label'])
 
-    def add_data(self, image_path: str, label: int):
-        new_entry = pd.DataFrame({'image_path': [image_path], 'label': [label]})
-        self.df = pd.concat([self.df, new_entry], ignore_index=True)
-        self.df.to_csv(self.data_file, index=False)
 
-        print(self.training_data)
+def add_data(self, image_path: str, label: int):
+    # Create a new DataFrame entry
+    new_entry = pd.DataFrame({'image_path': [image_path], 'label': [label]})
+    
+    # Append the new entry to the existing DataFrame
+    self.df = pd.concat([self.df, new_entry], ignore_index=True)
+    
+    # Save the updated DataFrame to the specified CSV file
+    self.df.to_csv(self.data_file, index=False)
+
+    # Print the current state of the training data for debugging
+    print("Current training data:")
+    print(self.df)
+
+    # Check if we have 100 images for retraining
+    if len(self.df) >= 100:
+        print("Retraining the model as we have enough data.")
+        self.retrain_model()
 
-        # Check if we have 100 images for retraining
-        if len(self.df) >= 100:
-            self.retrain_model()
+        
 
     def retrain_model(self):
         # Shuffle and split the data