| import glob | |
| import gradio as gr | |
| from inference import * | |
| from PIL import Image | |
| def gradio_app(image_path): | |
| """A function that send the file to the inference pipeline, and filters | |
| some predictions before outputting to gradio interface.""" | |
| predictions = run_inference(image_path) | |
| out_img = Image.fromarray(predictions.render()[0]) | |
| return out_img | |
| title = "UWROV Deepsea Detector" | |
| description = "Gradio demo for UWROV Deepsea Detector: Developed by Peyton " \ | |
| "Lee, Neha Nagvekar, and Cassandra Lam as part of the " \ | |
| "Underwater Remotely Operated Vehicles Team (UWROV) at the " \ | |
| "University of Washington. Deepsea Detector is built on " \ | |
| "MBARI's Monterey Bay Benthic Object Detector, which can also " \ | |
| "be found in FathomNet's Model Zoo. The model is trained on " \ | |
| "data from NOAA Ocean Exploration and FathomNet, " \ | |
| "with assistance from WoRMS for organism classification. All " \ | |
| "the images and associated annotations we used can be found in " \ | |
| "our Roboflow project. " | |
| examples = glob.glob("images/*.png") | |
| gr.Interface(gradio_app, | |
| inputs=[gr.inputs.Image(type="filepath")], | |
| outputs=gr.outputs.Image(type="pil"), | |
| enable_queue=True, | |
| title=title, | |
| description=description, | |
| examples=examples).launch() |