import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
import requests

# Load model and processor
model_name = "facebook/detr-resnet-50"
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)

# Define prediction function
def detect_objects(image):
    # Preprocess image
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # Process results
    target_sizes = torch.tensor([image.size[::-1]])  # (height, width)
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    # Draw boxes
    draw = ImageDraw.Draw(image)
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        draw.rectangle(box, outline="red", width=3)
        draw.text((box[0], box[1]), f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}", fill="red")

    return image

# Gradio interface
demo = gr.Interface(
    fn=detect_objects,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="Object Detection with Bounding Boxes",
    description="Upload an image and the AI will detect and label objects with bounding boxes using a Hugging Face model."
)

demo.launch()