import gradio as gr from transformers_js_py import pipeline from filters import convert pipe = await pipeline('object-detection', 'Xenova/detr-resnet-50') async def fn(image): result = await pipe(image) return result #demo = gr.Interface.from_pipeline(pipe) async def predict(image): result = await pipe(image) print(result) result = convert(result) print(result) return image, result demo = gr.Interface( fn=predict, inputs=gr.Image(type='pil'), outputs=gr.AnnotatedImage(), title='On-Device Object-Detection with Gradio-Lite' ) demo.launch() def convert(input_data): # Initialize the output list result_labels = [] # Iterate over each item in the input data for item in input_data: # Extract the label label = item['label'] # Extract the bounding box coordinates xmin = item['box']['xmin'] ymin = item['box']['ymin'] xmax = item['box']['xmax'] ymax = item['box']['ymax'] # Convert coordinates into the required output format (list of coordinates) coordinates = [xmin, ymin, xmax, ymax] # Append the tuple of coordinates and label to the output list result_labels.append((coordinates, label)) # Return the output list return result_labels # Same syntax as requirements.txt transformers-js-py