import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
import supervision as sv
import json
import requests
from PIL import Image
import numpy as np
 
image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("Guy2/AirportSec-150epoch")
# model = DetrForObjectDetection.from_pretrained("taroii/airport-security")
id2label = {0: 'dangerous-items', 1: 'Gun', 2: 'Knife', 3: 'Pliers', 4: 'Scissors', 5: 'Wrench'}
def anylize(url):
    image = Image.open(requests.get(url, stream=True).raw)
    image = image.convert('RGB')
    with torch.no_grad():

        inputs = image_processor(images=image, return_tensors='pt')
        outputs = model(**inputs)

        image = np.array(image)
        target_sizes = torch.tensor([image.shape[:2]])
        results = image_processor.post_process_object_detection(
            outputs=outputs, 
            threshold=0.8, 
            target_sizes=target_sizes
        )[0]

    # annotate
    detections = sv.Detections.from_transformers(transformers_results=results).with_nms(threshold=0.5)
    # labels = [str([list(xyxy), confidence, id2label[class_id]]) for xyxy, _, confidence, class_id, _ in detections]
    labels = [[list(xyxy), confidence, id2label[class_id]] for xyxy, _, confidence, class_id, _ in detections]
    print(labels)
    return str([image.shape[:2], labels])
    # json_list = json.dumps(labels)
    # return json_list

gr.Interface(fn = anylize, inputs="text", outputs="text").launch()
# gr.Interface(fn = anylize, inputs="text", outputs="image").launch()