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, target_dimension):
    height, width = image.shape[:2]
    max_dimension = max(height, width)
    scale_factor = target_dimension / max_dimension
    new_width = int(width * scale_factor)
    new_height = int(height * scale_factor)
    resized_image = cv2.resize(image, (new_width, new_height))
    return resized_image


def infer(image=None):
    if image is None:
        return [None,'no image']
    
    image = resize_image(image, 1200)

    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)
    return [output, prediction.overview]


interface = gr.Interface(fn=infer, 
             inputs='image',
             outputs=['image','text'],
             allow_flagging='manual',
             flagging_dir='flagged',
             examples=["eggsample1.jpg", "eggsample2.jpg"])

interface.launch()