from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw
import requests
import random
from IPython.display import display
import gradio as gr

# you can specify the revision tag if you don't want the timm dependency
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")

def draw_detections(image, outputs, processor, model, threshold=0.9):
    """
    Draw bounding boxes and labels on an image using detection results.
    
    Args:
        image (PIL.Image): Input image.
        outputs (dict): Model output.
        processor: The processor used for post-processing.
        model: The object detection model.
        threshold (float): Confidence threshold.

    Returns:
        PIL.Image: The image with bounding boxes drawn.
    """
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(
        outputs, target_sizes=target_sizes, threshold=threshold
    )[0]

    draw_image = image.copy()
    draw = ImageDraw.Draw(draw_image, "RGBA")

    # define fixed colors per label for consistency
    COLORS = {}
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        label_name = model.config.id2label[label.item()]
        
        # assign consistent random color for each label type
        if label_name not in COLORS:
            COLORS[label_name] = tuple(random.choices(range(256), k=3))
        color = COLORS[label_name]

        # draw translucent box
        draw.rectangle(box, fill=color + (80,), outline=color, width=3)
        draw.text((box[0] + 3, box[1] + 3),
                  f"{label_name} {round(score.item(), 2)}",
                  fill=(255, 255, 255, 255))
    
    return draw_image


def detect_and_draw(img):
    inputs = processor(images=img, return_tensors="pt")
    outputs = model(**inputs)
    return draw_detections(img, outputs, processor, model)

demo = gr.Interface(
    fn=detect_and_draw,
    inputs=gr.Image(type="pil"),
    outputs="image",
    title="Object Detection Viewer"
)

demo.launch(show_error=True)