| import gradio as gr | |
| def main(): | |
| def generate_predictions(image_input, text_input, do_sample, sampling_topp, sampling_temperature): | |
| return None, None | |
| term_of_use = """ | |
| ### Terms of use | |
| By using this model, users are required to agree to the following terms: | |
| The model is intended for academic and research purposes. | |
| The utilization of the model to create unsuitable material is strictly forbidden and not endorsed by this work. | |
| The accountability for any improper or unacceptable application of the model rests exclusively with the individuals who generated such content. | |
| ### License | |
| This project is licensed under the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct). | |
| """ | |
| with gr.Blocks(title="Kosmos-2", theme=gr.themes.Base()).queue() as demo: | |
| gr.Markdown((""" | |
| # Kosmos-2: Grounding Multimodal Large Language Models to the World | |
| [[Paper]](https://arxiv.org/abs/2306.14824) [[Code]](https://github.com/microsoft/unilm/blob/master/kosmos-2) | |
| """)) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input = gr.Image(type="pil", label="Test Image") | |
| text_input = gr.Radio(["Brief", "Detailed"], label="Description Type", value="Brief") | |
| do_sample = gr.Checkbox(label="Enable Sampling", info="(Please enable it before adjusting sampling parameters below)", value=False) | |
| with gr.Accordion("Sampling parameters", open=False) as sampling_parameters: | |
| sampling_topp = gr.Slider(minimum=0.1, maximum=1, step=0.01, value=0.9, label="Sampling: Top-P") | |
| sampling_temperature = gr.Slider(minimum=0.1, maximum=1, step=0.01, value=0.7, label="Sampling: Temperature") | |
| run_button = gr.Button(label="Run", visible=True) | |
| with gr.Column(): | |
| image_output = gr.Image(type="pil") | |
| text_output1 = gr.HighlightedText( | |
| label="Generated Description", | |
| combine_adjacent=False, | |
| show_legend=True, | |
| ).style(color_map={"box": "red"}) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Examples(examples=[ | |
| ["images/two_dogs.jpg", "Detailed", False], | |
| ["images/snowman.png", "Brief", False], | |
| ["images/man_ball.png", "Detailed", False], | |
| ], inputs=[image_input, text_input, do_sample]) | |
| with gr.Column(): | |
| gr.Examples(examples=[ | |
| ["images/six_planes.png", "Brief", False], | |
| ["images/quadrocopter.jpg", "Brief", False], | |
| ["images/carnaby_street.jpg", "Brief", False], | |
| ], inputs=[image_input, text_input, do_sample]) | |
| gr.Markdown(term_of_use) | |
| run_button.click(fn=generate_predictions, | |
| inputs=[image_input, text_input, do_sample, sampling_topp, sampling_temperature], | |
| outputs=[image_output, text_output1], | |
| show_progress=True, queue=True) | |
| demo.launch(share=True) | |
| if __name__ == "__main__": | |
| main() | |