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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():
# prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
# confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
# return confidences
def run():
# demo = gr.Interface(
# fn=predict,
# inputs=gr.inputs.Image(type="pil"),
# outputs=gr.outputs.Label(num_top_classes=3),
# )
demo = gr.Interface(fn=infer,
inputs=['image'],
outputs=['image', 'text'])
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run()
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