app.py

import gradio as gr
import cv2
from geti_sdk.deployment import Deployment
from geti_sdk.utils import show_image_with_annotation_scene

# Step 1: Load the deployment
deployment = Deployment.from_folder("deployment")
deployment.load_inference_models(device="CPU")

def resize_image(image, max_dimension):
    height, width = image.shape[:2]
    if max(height, width) <= max_dimension:
        return image
    if height > width:
        new_height = max_dimension
        new_width = int(width * (max_dimension / height))
    else:
        new_width = max_dimension
        new_height = int(height * (max_dimension / width))
    resized_image = cv2.resize(image, (new_width, new_height))
    return resized_image

def infer(image=None):
    if image is None:
        return None
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    prediction = deployment.infer(image)
    output = show_image_with_annotation_scene(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), prediction, show_results=False)
    output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
    # output = cv2.resize(output, (224, 224))
    return output

gr.Interface(fn=infer, 
             inputs='image',
             outputs='image',
             examples=["eggsample1.png", "eggsample2.png"]).launch()