app.py

import gradio as gr
from src.utils import *

if __name__ == '__main__':
    theme = gr.themes.Soft(primary_hue="red", secondary_hue="red", font=['arial'])
    
    with gr.Blocks(theme=theme) as demo:
        with gr.Column(elem_classes="header"):
            gr.Markdown("# MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data")
            gr.Markdown("### Paul Borne–Pons, Mikolaj Czerkawski, Rosalie Martin, Romain Rouffet")
            gr.Markdown('[[GitHub](https://github.com/PaulBorneP/MESA)] [[Model](https://huggingface.co./NewtNewt/MESA)] [[Dataset](https://huggingface.co./datasets/Major-TOM/Core-DEM)]')
            
        # Abstract Section
        with gr.Column(elem_classes="abstract"):
            gr.Markdown("MESA is a novel generative model based on latent denoising diffusion capable of generating 2.5D representations of terrain based on the text prompt conditioning supplied via natural language. The model produces two co-registered modalities of optical and depth maps.") # Replace with your abstract text
            gr.Markdown("This is a test version of the demo app. Please be aware that MESA supports primarily complex, mountainous terrains as opposed to flat land")
            gr.Markdown("The generated image is quite large, so for the full resolution (768) it might take a while to load the surface")
    
        with gr.Row():
            prompt_input = gr.Textbox(lines=2, placeholder="Enter a terrain description...")
            generate_button = gr.Button("Generate Terrain", variant="primary")
    
        model_output = gr.Model3D(
            camera_position=[90, 180, 512]
        )
    
        with gr.Accordion("Advanced Options", open=False) as advanced_options:
            num_inference_steps_slider = gr.Slider(minimum=10, maximum=1000, step=10, value=50, label="Inference Steps")
            guidance_scale_slider = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, value=7.5, label="Guidance Scale")
            seed_number = gr.Number(value=6378, label="Seed")
            crop_size_slider = gr.Slider(minimum=128, maximum=768, step=64, value=512, label="Crop Size")
            prefix_textbox = gr.Textbox(label="Prompt Prefix", value="A Sentinel-2 image of ")
    
        generate_button.click(
            fn=generate_and_display,
            inputs=[prompt_input, num_inference_steps_slider, guidance_scale_slider, seed_number, crop_size_slider, prefix_textbox],
            outputs=model_output,
        )
        
        demo.queue().launch()