Updated app.py

app.py

@@ -9,6 +9,9 @@ import os
 # Suppress TensorFlow logging for cleaner logs
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
+# Disable GPU usage explicitly to prevent TensorFlow from attempting to access GPU libraries
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
 # Load labels
 with open('tensorflow/labels.txt', 'r') as f:
     labels = f.read().splitlines()
@@ -36,9 +39,8 @@ detected_classes = graph.get_tensor_by_name('detected_classes:0')
 detected_scores = graph.get_tensor_by_name('detected_scores:0')
 
 # Define the target size based on your model's expected input
-# You may need to adjust these values based on your model's requirements
-TARGET_WIDTH = 224
-TARGET_HEIGHT = 224
+TARGET_WIDTH = 320
+TARGET_HEIGHT = 320
 
 def preprocess_image(image):
     """
@@ -89,12 +91,16 @@ def draw_boxes(image, boxes, classes, scores, threshold=0.5):
         # Prepare label
         label = f"{labels[int(cls) - 1]}: {score:.2f}"
         
+        # Calculate text size using textbbox
+        text_bbox = draw.textbbox((0, 0), label, font=font)
+        text_width = text_bbox[2] - text_bbox[0]
+        text_height = text_bbox[3] - text_bbox[1]
+        
         # Draw label background
-        text_size = draw.textsize(label, font=font)
-        draw.rectangle([(left, top - text_size[1]), (left + text_size[0], top)], fill="red")
+        draw.rectangle([(left, top - text_height - 4), (left + text_width + 4, top)], fill="red")
         
         # Draw text
-        draw.text((left, top - text_size[1]), label, fill="white", font=font)
+        draw.text((left + 2, top - text_height - 2), label, fill="white", font=font)
     
     return image
 
@@ -122,7 +128,7 @@ def predict(image):
         return annotated_image
     
     except Exception as e:
-        # Return a red image indicating an error
+        # Return an error image with the error message
         error_image = Image.new('RGB', (500, 500), color=(255, 0, 0))
         draw = ImageDraw.Draw(error_image)
         try:
@@ -130,26 +136,46 @@ def predict(image):
         except IOError:
             font = ImageFont.load_default()
         error_text = "Error during prediction."
-        text_size = draw.textsize(error_text, font=font)
-        draw.text(((500 - text_size[0]) / 2, (500 - text_size[1]) / 2), error_text, fill="white", font=font)
+        
+        # Calculate text size using textbbox
+        text_bbox = draw.textbbox((0, 0), error_text, font=font)
+        text_width = text_bbox[2] - text_bbox[0]
+        text_height = text_bbox[3] - text_bbox[1]
+        
+        # Center the text
+        draw.rectangle(
+            [
+                ((500 - text_width) / 2 - 10, (500 - text_height) / 2 - 10),
+                ((500 + text_width) / 2 + 10, (500 + text_height) / 2 + 10)
+            ],
+            fill="black"
+        )
+        draw.text(
+            ((500 - text_width) / 2, (500 - text_height) / 2),
+            error_text,
+            fill="white",
+            font=font
+        )
         return error_image
 
 # Define Gradio interface using the new API
 title = "JunkWaxHero 🦸‍♂️ - Baseball Card Set Identifier"
 description = "Upload an image of a baseball card, and JunkWaxHero will identify the set it belongs to with high accuracy."
 
+# Verify that example images exist to prevent FileNotFoundError
+example_images = ["examples/card1.jpg", "examples/card2.jpg", "examples/card3.jpg"]
+valid_examples = [img for img in example_images if os.path.exists(img)]
+if not valid_examples:
+    valid_examples = None  # Remove examples if none exist
+
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
     outputs=gr.Image(type="pil"),
     title=title,
     description=description,
-    examples=[
-        ["examples/card1.jpg"],
-        ["examples/card2.jpg"],
-        ["examples/card3.jpg"]
-    ],
-    allow_flagging="never"
+    examples=valid_examples,
+    flagging_mode="never"  # Updated parameter
 )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,4 +1,4 @@
-gradio
+gradio>=3.0.0
 tensorflow==2.11.0  # Ensure this matches the TensorFlow version used during model training
 Pillow
-numpy<2.0.0
+numpy<2.0.0