app.py

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 sys
 
# 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): 
    if image is None:
        return None
        
    try:
        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 
        output_path = "output.png"
        plt.savefig(output_path) 
        plt.close() 
        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
]

# 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"]
]

# 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

print(f"Final example images: {example_images}", file=sys.stderr)

# 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.",
    allow_flagging="never"  # Disable flagging to avoid potential issues
) 

# Launch with specific configuration for Hugging Face
if __name__ == "__main__":
    interface.launch(debug=True)