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", "false").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 place name, and ClimateGAN will generate images showing how the location could be impacted by flooding, wildfires, or smog.'  # noqa: E501
                )
            with gr.Column():
                gr.HTML(
                    "<p style='text-align: center'>This project is an unofficial clone of <a href='https://thisclimatedoesnotexist.com/'>ThisClimateDoesNotExist</a> | <a href='https://github.com/cc-ai/climategan'>ClimateGAN GitHub Repo</a></p>"  # noqa: E501
                )
        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()