import torch
from transformers import DetrImageProcessor, DetrForObjectDetection
from PIL import Image
import gradio as gr
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import io

# Load the processor and model
processor = DetrImageProcessor.from_pretrained('facebook/detr-resnet-101')
model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')

def object_detection(image):
    # Preprocess the image
    inputs = processor(images=image, return_tensors="pt")

    # Perform object detection
    outputs = model(**inputs)

    # Extract bounding boxes and labels
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    # Plot the image with bounding boxes
    plt.figure(figsize=(16, 10))
    plt.imshow(image)
    ax = plt.gca()

    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        xmin, ymin, xmax, ymax = box
        width, height = xmax - xmin, ymax - ymin

        ax.add_patch(plt.Rectangle((xmin, ymin), width, height, fill=False, color='red', linewidth=3))
        text = f'{model.config.id2label[label.item()]}: {round(score.item(), 3)}'
        ax.text(xmin, ymin, text, fontsize=15, bbox=dict(facecolor='yellow', alpha=0.5))

    plt.axis('off')

    # Save the plot to an image buffer
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    plt.close()

    # Convert buffer to an Image object
    result_image = Image.open(buf)

    return result_image

# Define the Gradio interface
demo = gr.Interface(
    fn=object_detection,
    inputs=gr.Image(type="pil", label="Upload an Image"),
    outputs=gr.Image(type="pil", label="Detected Objects"),
    title="Object Detection with DETR (ResNet-101)",
    description="Upload an image and get object detection results using the DETR model with a ResNet-101 backbone.",
)

# Launch the Gradio interface
if __name__ == "__main__":
    demo.launch()