app.py

# based on https://huggingface.co/spaces/NimaBoscarino/climategan/blob/main/app.py # noqa: E501
# thank you @NimaBoscarino

import os
import gradio as gr
import googlemaps
from skimage import io
from urllib import parse
import numpy as np
from climategan_wrapper import ClimateGAN


def predict(cg: ClimateGAN, api_key):
    def _predict(*args):
        image = place = painter = None
        if len(args) == 2:
            image = args[0]
            painter = args[1]
        else:
            assert len(args) == 3, "Unknown number of inputs {}".format(len(args))
            image, place, painter = args

        if api_key and place:
            geocode_result = gmaps.geocode(place)

            address = geocode_result[0]["formatted_address"]
            static_map_url = f"https://maps.googleapis.com/maps/api/streetview?size=640x640&location={parse.quote(address)}&source=outdoor&key={api_key}"
            img_np = io.imread(static_map_url)
        else:
            img_np = image

        painters = {
            "ClimateGAN Painter": "climategan",
            "Stable Diffusion Painter": "stable_diffusion",
            "Both": "both",
        }

        output_dict = cg.infer_single(img_np, painters[painter], as_pil_image=True)

        input_image = output_dict["input"]
        masked_input = output_dict["masked_input"]
        wildfire = output_dict["wildfire"]
        smog = output_dict["smog"]
        depth = np.repeat(output_dict["depth"][..., None], 3, axis=-1)
        segmentation = output_dict["segmentation"]

        climategan_flood = output_dict.get(
            "climategan_flood",
            np.ones(input_image.shape) * 255,
        )
        stable_flood = output_dict.get(
            "stable_flood",
            np.ones(input_image.shape) * 255,
        )
        stable_copy_flood = output_dict.get(
            "stable_copy_flood",
            np.ones(input_image.shape) * 255,
        )
        concat = output_dict.get(
            "concat",
            np.ones(input_image.shape) * 255,
        )

        return (
            input_image,
            masked_input,
            segmentation,
            depth,
            climategan_flood,
            stable_flood,
            stable_copy_flood,
            concat,
            wildfire,
            smog,
        )

    return _predict


if __name__ == "__main__":

    api_key = os.environ.get("GMAPS_API_KEY")
    gmaps = None
    if api_key is not None:
        gmaps = googlemaps.Client(key=api_key)

    cg = ClimateGAN(
        model_path="config/model/masker",
        dev_mode=os.environ.get("CG_DEV_MODE", "").lower() == "true",
    )
    cg._setup_stable_diffusion()

    with gr.Blocks() as blocks:
        with gr.Row():
            with gr.Column():
                gr.Markdown("# ClimateGAN: Visualize Climate Change")
                gr.HTML(
                    'Climate change does not impact everyone equally. This Space shows the effects of the climate emergency, "one address at a time". Visit the original experience at <a href="https://thisclimatedoesnotexist.com/">ThisClimateDoesNotExist.com</a>.<br>Enter an address or upload a Street View image, and ClimateGAN will generate images showing how the location could be impacted by flooding, wildfires, or smog if it happened there.'  # noqa: E501
                    + "<br><br>This is <strong>not</strong> an exercise in climate prediction, rather an exercise of empathy, to put yourself in other's shoes, as if Climate Change came crushing on your doorstep."  # noqa: E501
                )
            with gr.Column():
                gr.HTML(
                    "<p style='text-align: center'>Visit <a href='https://thisclimatedoesnotexist.com/'>ThisClimateDoesNotExist</a> for more information. | Original <a href='https://github.com/cc-ai/climategan'>ClimateGAN GitHub Repo</a></p>"  # noqa: E501
                    + "<p>After you have selected an image and started the inference you will see all the outputs of ClimateGAN, including intermediate outputs such as the flood mask, the segmentation map and the depth maps used to produce the 3 events</p>"
                    + "<p>This Space makes use of recent Stable Diffusion in-painting pipelines to replace ClimateGAN's original Painter. If you select 'Both' painters, you will see a comparison</p>"
                    + "<p>Read the original <a href='https://openreview.net/forum?id=EZNOb_uNpJk', target='_blank'>ICLR 2021 ClimateGAN paper</a></p>"
                )
        with gr.Row():
            gr.Markdown("## Inputs")
        with gr.Row():
            with gr.Column():
                inputs = [gr.inputs.Image(label="Input Image")]
            with gr.Column():
                if api_key:
                    inputs += [gr.inputs.Textbox(label="Address or place name")]
                inputs += [
                    gr.inputs.Dropdown(
                        choices=[
                            "ClimateGAN Painter",
                            "Stable Diffusion Painter",
                            "Both",
                        ],
                        label="Choose Flood Painter",
                        default="Both",
                    )
                ]
                btn = gr.Button("See for yourself!", label="Run")
        with gr.Row():
            gr.Markdown("## Outputs")
        with gr.Row():
            outputs = []
            outputs.append(
                gr.outputs.Image(type="numpy", label="Original image"),
            )
            outputs.append(
                gr.outputs.Image(type="numpy", label="Masked input image"),
            )
            outputs.append(
                gr.outputs.Image(type="numpy", label="Segmentation map"),
            )
            outputs.append(
                gr.outputs.Image(type="numpy", label="Depth map"),
            )
        with gr.Row():
            outputs.append(
                gr.outputs.Image(type="numpy", label="ClimateGAN-Flooded image"),
            )
            outputs.append(
                gr.outputs.Image(type="numpy", label="Stable Diffusion-Flooded image"),
            )
            outputs.append(
                gr.outputs.Image(
                    type="numpy",
                    label="Stable Diffusion-Flooded image (restricted to masked area)",
                )
            ),
        with gr.Row():
            outputs.append(
                gr.outputs.Image(type="numpy", label="Comparison of previous images"),
            )
        with gr.Row():
            outputs.append(
                gr.outputs.Image(type="numpy", label="Wildfire"),
            )
            outputs.append(
                gr.outputs.Image(type="numpy", label="Smog"),
            )
        btn.click(predict(cg, api_key), inputs=inputs, outputs=outputs)
    blocks.launch()