Update main.py

main.py

@@ -2,12 +2,41 @@ import gradio as gr
 import torch
 import requests
 from torchvision import transforms
+import cv2
+from geti_sdk.deployment import Deployment
+from geti_sdk.utils import show_image_with_annotation_scene
 
 model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
 response = requests.get("https://git.io/JJkYN")
 labels = response.text.split("\n")
 
 
+# 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,'Error: No image provided']
+
+    image = resize_image(image, 1200)
+    prediction = deployment.infer(image)
+    output = show_image_with_annotation_scene(image, prediction, show_results=False)
+    output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+    return [output, prediction.overview]
+
+
 def predict(inp):
     inp = transforms.ToTensor()(inp).unsqueeze(0)
     with torch.no_grad():