added show buttons and ai auto labeler

.gitignore

@@ -6,8 +6,8 @@ test.python
 output/
 *.csv
 *.spec
-*.txt
 *.imtag
+label*.txt
 compile.py
 __old_code.py
 test.py

auto_labeler.py

@@ -0,0 +1,51 @@
+import sys
+from PySide6.QtCore import QThread, Signal, Slot
+from PIL import Image
+import torch
+class Auto_Labbeler(QThread):
+    result_signal = Signal(str)  # Signal to send data back to the main thread
+    finished_signal = Signal()  # Signal to indicate that processing is finished
+
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        self.model = None
+        self.feature_extractor = None
+        self.image_path = ""
+
+    def load_model(self) :
+
+        from transformers import ViTFeatureExtractor, ViTForImageClassification
+
+        self.model =  ViTForImageClassification.from_pretrained('google/vit-base-patch16-224', 
+                                                #   num_labels=100,
+                                                #   ignore_mismatched_sizes=True,
+                                                #   map_location=torch.device('cpu') 
+                                                  )
+
+        self.feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
+
+
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model.to(self.device)
+        self.model.eval()
+
+    def set_image_path(self, image_path):
+        self.image_path = image_path
+
+    def run(self):
+        # Load and process the image
+        if not self.model:
+            self.load_model()
+
+        if self.image_path:
+            image = Image.open(self.image_path).convert('RGB').resize((244,244))
+            processed_image = self.feature_extractor(images=image, return_tensors="pt")
+            processed_image = {k: v.to(self.device) for k, v in processed_image.items()}
+            with torch.no_grad():
+                outputs = self.model(**processed_image)
+
+            predicted_class_idx = torch.argmax(outputs.logits, dim=-1).item()
+            predicted_class =  self.model.config.id2label[predicted_class_idx]
+
+            self.result_signal.emit(predicted_class)
+        self.finished_signal.emit()