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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import gradio as gr | |
| from app_caption import create_demo as create_demo_caption | |
| from app_vqa import create_demo as create_demo_vqa | |
| from prismer_model import build_deformable_conv, download_models | |
| # Prepare model checkpoints | |
| download_models() | |
| build_deformable_conv() | |
| # Demo file here | |
| description = """ | |
| # Prismer | |
| The official demo for **Prismer: A Vision-Language Model with Multi-Task Experts**. | |
| Please refer to our [project page](https://shikun.io/projects/prismer) or [github](https://github.com/NVlabs/prismer) for more details. | |
| Expert labels will be only computed once for the same image checked with md5sum. | |
| """ | |
| if (SPACE_ID := os.getenv('SPACE_ID')) is not None: | |
| description += f'For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a>' | |
| with gr.Blocks(theme='sudeepshouche/minimalist') as demo: | |
| gr.Markdown(description) | |
| with gr.Tabs(): | |
| with gr.TabItem('Zero-shot Image Captioning'): | |
| create_demo_caption() | |
| with gr.TabItem('Visual Question Answering'): | |
| create_demo_vqa() | |
| demo.queue(api_open=False).launch() | |