Fix example paths

app.py

@@ -1,83 +1,89 @@
-import torch
-import torchvision
-from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-import gradio as gr
-import os
+import torch 
+import torchvision 
+from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights 
+from PIL import Image 
+import numpy as np 
+import matplotlib.pyplot as plt 
+import gradio as gr 
+import os 
+ 
+# Load the pre-trained model once 
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT) 
+model.eval() 
+ 
+# COCO class names 
+COCO_INSTANCE_CATEGORY_NAMES = [ 
+    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign', 
+    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
+    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A', 
+    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 
+    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 
+    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 
+    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 
+    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 
+    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 
+    '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): 
+    transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms() 
+    image_tensor = transform(image).unsqueeze(0) 
+ 
+    with torch.no_grad(): 
+        prediction = model(image_tensor)[0] 
+ 
+    boxes = prediction['boxes'].cpu().numpy() 
+    labels = prediction['labels'].cpu().numpy() 
+    scores = prediction['scores'].cpu().numpy() 
+ 
+    image_np = np.array(image) 
+    plt.figure(figsize=(10, 10)) 
+    plt.imshow(image_np) 
+    ax = plt.gca() 
+ 
+    for box, label, score in zip(boxes, labels, scores): 
+        if score >= threshold: 
+            x1, y1, x2, y2 = box 
+            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, 
+                         fill=False, color='red', linewidth=2)) 
+            class_name = COCO_INSTANCE_CATEGORY_NAMES[label] 
+            ax.text(x1, y1, f'{class_name}: {score:.2f}', bbox=dict(facecolor='yellow', alpha=0.5), 
+                    fontsize=12, color='black') 
+ 
+    plt.axis('off') 
+    plt.tight_layout() 
+     
+    # Save the figure to return 
+    plt.savefig("output.png") 
+    plt.close() 
+    return "output.png" 
 
-# Load the pre-trained model once
-model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
-model.eval()
-
-# COCO class names
-COCO_INSTANCE_CATEGORY_NAMES = [
-    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
-    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
-    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
-    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
-    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
-    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
-    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
-    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
-    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-    '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):
-    transform = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.transforms()
-    image_tensor = transform(image).unsqueeze(0)
-
-    with torch.no_grad():
-        prediction = model(image_tensor)[0]
-
-    boxes = prediction['boxes'].cpu().numpy()
-    labels = prediction['labels'].cpu().numpy()
-    scores = prediction['scores'].cpu().numpy()
-
-    image_np = np.array(image)
-    plt.figure(figsize=(10, 10))
-    plt.imshow(image_np)
-    ax = plt.gca()
-
-    for box, label, score in zip(boxes, labels, scores):
-        if score >= threshold:
-            x1, y1, x2, y2 = box
-            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1,
-                         fill=False, color='red', linewidth=2))
-            class_name = COCO_INSTANCE_CATEGORY_NAMES[label]
-            ax.text(x1, y1, f'{class_name}: {score:.2f}', bbox=dict(facecolor='yellow', alpha=0.5),
-                    fontsize=12, color='black')
-
-    plt.axis('off')
-    plt.tight_layout()
-    
-    # Save the figure to return
-    plt.savefig("output.png")
-    plt.close()
-    return "output.png"
-
-# List the example images
+# Define the example image paths correctly for Hugging Face Spaces
+# Images are in the Object-Detection subdirectory
+examples_dir = os.path.join(os.path.dirname(__file__), "Object-Detection")
 example_images = [
-    ["TEST_IMG_1.jpg"],
-    ["TEST_IMG_2.jpg"],
-    ["TEST_IMG_3.jpg"],
-    ["TEST_IMG_4.jpg"]
+    [os.path.join(examples_dir, "TEST_IMG_1.jpg")], 
+    [os.path.join(examples_dir, "TEST_IMG_2.JPG")],  # Note: preserving the uppercase JPG extension
+    [os.path.join(examples_dir, "TEST_IMG_3.jpg")], 
+    [os.path.join(examples_dir, "TEST_IMG_4.jpg")]
 ]
 
-# Create Gradio 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",
-    description="Upload an image to detect objects using a pretrained Faster R-CNN model."
-).launch()
+# Create Gradio interface 
+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", 
+    description="Upload an image to detect objects using a pretrained Faster R-CNN model." 
+)
+
+# Launch with specific configuration for Hugging Face
+if __name__ == "__main__":
+    interface.launch()