Update app.py

app.py

@@ -3,6 +3,9 @@ from gradio_bbox_annotator import BBoxAnnotator
 import json
 import os
 from pathlib import Path
+from PIL import Image
+from io import BytesIO
+import tempfile
 
 # Define categories and their limits
 CATEGORY_LIMITS = {
@@ -10,10 +13,35 @@ CATEGORY_LIMITS = {
     "text": 2          # Maximum 2 text annotations per image
 }
 CATEGORIES = list(CATEGORY_LIMITS.keys())
+MAX_SIZE = [1024, 1024]  # Maximum width and height for resized images
 
 class AnnotationManager:
     def __init__(self):
         self.annotations = {}
+        self.temp_dir = tempfile.mkdtemp()  # Create temporary directory for resized images
+    
+    def resize_image(self, image_path):
+        """Resize image to maximum dimensions while maintaining aspect ratio"""
+        try:
+            # Read and resize image
+            with open(image_path, "rb") as f:
+                img = Image.open(BytesIO(f.read()))
+                img.thumbnail(MAX_SIZE, Image.Resampling.LANCZOS)
+            
+            # Save resized image to temporary file
+            filename = os.path.basename(image_path)
+            temp_path = os.path.join(self.temp_dir, f"resized_{filename}")
+            img.save(temp_path)
+            
+            return temp_path
+        except Exception as e:
+            raise ValueError(f"Error processing image: {str(e)}")
+    
+    def process_image_upload(self, image_path):
+        """Process uploaded image and return path to resized version"""
+        if not image_path:
+            return None
+        return self.resize_image(image_path)
     
     def validate_annotations(self, bbox_data):
         """Validate the annotation data and return (is_valid, error_message)"""
@@ -60,7 +88,11 @@ class AnnotationManager:
             return self.get_json_annotations(), f"❌ Error: {error_msg}"
             
         image_path, annotations = bbox_data
+        # Use original filename (remove 'resized_' prefix)
         filename = os.path.basename(image_path)
+        if filename.startswith("resized_"):
+            filename = filename[8:]
+            
         formatted_annotations = []
         for ann in annotations:
             y1, y2, x1, x2, label = ann
@@ -90,11 +122,11 @@ def create_interface():
     annotation_mgr = AnnotationManager()
     
     with gr.Blocks() as demo:
-        gr.Markdown("""
+        gr.Markdown(f"""
         # Advertisement and Text Annotation Tool
         
         **Instructions:**
-        1. Upload an image using the upload button in the annotator
+        1. Upload an image (will be automatically resized to max {MAX_SIZE[0]}x{MAX_SIZE[1]})
         2. Draw bounding boxes and select the appropriate label
         3. Click 'Save Annotations' to add to the collection
         4. Repeat for all images
@@ -127,10 +159,26 @@ def create_interface():
             save_btn = gr.Button("Save Current Image Annotations", variant="primary")
             clear_btn = gr.Button("Clear All Annotations", variant="secondary")
 
-        # Add status message with different styling based on message type
+        # Add status message
         status_msg = gr.Markdown(label="Status")
         
         # Event handlers
+        def update_image(image_path):
+            if not image_path:
+                return None
+            try:
+                resized_path = annotation_mgr.process_image_upload(image_path)
+                return resized_path
+            except Exception as e:
+                return None
+        
+        # Handle image upload and resizing
+        bbox_input.upload(
+            fn=update_image,
+            inputs=[bbox_input],
+            outputs=[bbox_input]
+        )
+        
         save_btn.click(
             fn=annotation_mgr.add_annotation,
             inputs=[bbox_input],