app.py

import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch


def anylize(img):
#     input_image_path = os.path.join(os.getcwd(), img.get_data()[0].name)
#     return input_image_path
    image = img

    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("Guy2/AirportSec-100epoch")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    #target_sizes = torch.tensor([image.size])
    #target_sizes = torch.tensor([image.size[::-1]])
    target_sizes = torch.tensor([image.shape[:2]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    print(f"results: {results}")
    
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        return(
                f"Detected {model.config.id2label[label.item()]} with confidence "
                f"{round(score.item(), 3)} at location {box}"
        )


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

app.launch()