app.py

import gradio as gr
import torch
from transformers import DetrForObjectDetection, DetrImageProcessor, AutoModel
import supervision as sv
from supervision.detection.annotate import BoxAnnotator
from supervision.utils.notebook import plot_image
import cv2

CONFIDENCE_TRESHOLD = 0.5
IOU_TRESHOLD = 0.8

og_model = 'facebook/detr-resnet-50'
image_processor =  DetrImageProcessor.from_pretrained(og_model)
model = AutoModel.from_pretrained("taroii/notfinetuned-detr-50")

def predict(image_path):
    image = cv2.imread(image_path)
    with torch.no_grad():
        # load image and predict
        inputs = image_processor(images=image, return_tensors='pt')
        outputs = model(**inputs)
    
        # post-process
        target_sizes = torch.tensor([image.shape[:2]])
        results = image_processor.post_process_object_detection(
            outputs=outputs, 
            threshold=CONFIDENCE_TRESHOLD, 
            target_sizes=target_sizes
        )[0]
    
    # annotate
    detections = sv.Detections.from_transformers(transformers_results=results).with_nms(threshold=0.5)
    labels = [f"{id2label[class_id]} {confidence:.2f}" for _, confidence, class_id, _ in detections]
    frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
    
    #return plot_image(frame, (16, 16))
    
    return frame

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload X-Ray Image", type="filepath"),
    outputs=gr.Image(),
    title="Non-Fine-Tuned Model"
).launch()