Update app.py

app.py

@@ -27,13 +27,34 @@ COCO_INSTANCE_CATEGORY_NAMES = [
     'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 
     'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' 
 ] 
- 
+
 # Gradio-compatible detection function 
 def detect_objects(image, threshold=0.5): 
     if image is None:
-        return None
+        print("Image is None, returning empty output", file=sys.stderr)
+        # Create a blank image as output
+        blank_img = Image.new('RGB', (400, 400), color='white')
+        plt.figure(figsize=(10, 10))
+        plt.imshow(blank_img)
+        plt.text(0.5, 0.5, "No image provided", 
+                 horizontalalignment='center', verticalalignment='center',
+                 transform=plt.gca().transAxes, fontsize=20)
+        plt.axis('off')
+        output_path = "blank_output.png"
+        plt.savefig(output_path)
+        plt.close()
+        return output_path
         
     try:
+        print(f"Processing image of type {type(image)} and threshold {threshold}", file=sys.stderr)
+        # Make sure threshold is a valid number
+        if threshold is None:
+            threshold = 0.5
+            print("Threshold was None, using default 0.5", file=sys.stderr)
+        
+        # Convert threshold to float if it's not already
+        threshold = float(threshold)
+        
         transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms() 
         image_tensor = transform(image).unsqueeze(0) 
      
@@ -50,6 +71,9 @@ def detect_objects(image, threshold=0.5):
         ax = plt.gca() 
      
         for box, label, score in zip(boxes, labels, scores): 
+            # Explicit debug prints to trace the comparison issue
+            print(f"Score: {score}, Threshold: {threshold}, Type: {type(score)}/{type(threshold)}", file=sys.stderr)
+            
             if score >= threshold: 
                 x1, y1, x2, y2 = box 
                 ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, 
@@ -68,89 +92,52 @@ def detect_objects(image, threshold=0.5):
         return output_path
     except Exception as e:
         print(f"Error in detect_objects: {e}", file=sys.stderr)
-        return None
-
-# Function to check if a file exists
-def file_exists(filepath):
-    return os.path.isfile(filepath)
-
-# Find base directory for examples
-# For Hugging Face Spaces, this is typically the root directory of the repository
-BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-
-# Check all possible locations for the example images
-possible_dirs = [
-    BASE_DIR,  # Root directory
-    os.path.join(BASE_DIR, "Object-Detection"),  # Subdirectory
-    os.path.join(BASE_DIR, "images"),  # Common image directory name
-    os.path.join(os.path.dirname(BASE_DIR), "Object-Detection")  # Parent/sibling directory
-]
+        import traceback
+        traceback.print_exc(file=sys.stderr)
+        
+        # Create an error image
+        error_img = Image.new('RGB', (400, 400), color='white')
+        plt.figure(figsize=(10, 10))
+        plt.imshow(error_img)
+        plt.text(0.5, 0.5, f"Error: {str(e)}", 
+                 horizontalalignment='center', verticalalignment='center',
+                 transform=plt.gca().transAxes, fontsize=12, wrap=True)
+        plt.axis('off')
+        error_path = "error_output.png"
+        plt.savefig(error_path)
+        plt.close()
+        return error_path
 
-# Test image filenames with different case combinations
-test_image_variations = [
-    ["TEST_IMG_1.jpg"], 
-    ["TEST_IMG_1.JPG"],
-    ["test_img_1.jpg"],
-    ["Test_Img_1.jpg"]
+# Create direct file paths for examples
+# These exact filenames match what's visible in your repository
+examples = [
+    os.path.join("/home/user/app", "TEST_IMG_1.jpg"),
+    os.path.join("/home/user/app", "TEST_IMG_2.JPG"),
+    os.path.join("/home/user/app", "TEST_IMG_3.jpg"),
+    os.path.join("/home/user/app", "TEST_IMG_4.jpg")
 ]
 
-# Find working examples by testing different combinations
-working_examples = []
-
-# Check all possible combinations of directories and filenames
-for directory in possible_dirs:
-    print(f"Checking directory: {directory}", file=sys.stderr)
-    if os.path.isdir(directory):
-        for variation in test_image_variations:
-            filepath = os.path.join(directory, variation[0])
-            if file_exists(filepath):
-                print(f"Found example image: {filepath}", file=sys.stderr)
-                working_examples.append([filepath])
-                # If we found the first image, try the others with the same pattern
-                base_pattern = variation[0].split("1")[0]
-                ext = variation[0].split(".")[-1]
-                for i in range(2, 5):  # Test images 2-4
-                    test_path = os.path.join(directory, f"{base_pattern}{i}.{ext}")
-                    if file_exists(test_path):
-                        print(f"Found additional example: {test_path}", file=sys.stderr)
-                        working_examples.append([test_path])
-                
-                # If we found all 4 examples, break the loop
-                if len(working_examples) >= 4:
-                    break
-        
-        # If we found examples in this directory, no need to check others
-        if working_examples:
-            break
-
-# If no working examples found, try hard-coded paths
-if not working_examples:
-    print("No examples found automatically. Using hard-coded paths.", file=sys.stderr)
-    example_images = [
-        ["TEST_IMG_1.jpg"],
-        ["TEST_IMG_2.JPG"],
-        ["TEST_IMG_3.jpg"],
-        ["TEST_IMG_4.jpg"]
-    ]
-else:
-    example_images = working_examples[:4]  # Use first 4 found examples
+# Create Gradio interface
+# Important: For Gradio examples, we need to create a list of lists
+example_list = [[path] for path in examples if os.path.exists(path)]
 
-print(f"Final example images: {example_images}", file=sys.stderr)
+print(f"Found {len(example_list)} valid examples: {example_list}", file=sys.stderr)
 
-# Create Gradio interface 
+# Create Gradio interface with a simplified approach
 interface = gr.Interface( 
     fn=detect_objects, 
-    inputs=[ 
-        gr.Image(type="pil"), 
-        gr.Slider(0, 1, value=0.5, label="Confidence Threshold") 
-    ], 
-    outputs=gr.Image(type="filepath"), 
-    examples=example_images, 
-    title="Faster R-CNN Object Detection", 
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="filepath", label="Detected Objects"),
+    title="Faster R-CNN Object Detection",
     description="Upload an image to detect objects using a pretrained Faster R-CNN model.",
-    allow_flagging="never"  # Disable flagging to avoid potential issues
-) 
+    examples=example_list,
+    cache_examples=False  # Disable caching to avoid potential issues
+)
 
 # Launch with specific configuration for Hugging Face
 if __name__ == "__main__":
+    # Launch with debug mode enabled
     interface.launch(debug=True)