app.py

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

import os
from textwrap import dedent
from urllib import parse
from requests import get

import googlemaps
import gradio as gr
import numpy as np
from gradio.components import (
    HTML,
    Button,
    Column,
    Dropdown,
    Image,
    Markdown,
    Radio,
    Row,
    Textbox,
)
from skimage import io
from datetime import datetime

from climategan_wrapper import ClimateGAN

TEXTS = [
    dedent(
        """
        <p>
            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>
        </p>
        <br>
        <p>
            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.
        </p>
        <br>
        <p>
            This is <strong>NOT</strong> an exercise in climate prediction,
            rather an exercise of empathy, to put yourself in others' shoes,
            as if Climate Change came crushing on your doorstep.
        </p>
        <br>
        <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>
        <br>
        <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>
        <br>
        <p style='text-align: center'>
            Visit
            <a href='https://thisclimatedoesnotexist.com/'>
                ThisClimateDoesNotExist.com</a>
            &nbsp;for more information
            &nbsp;&nbsp;|&nbsp;&nbsp;
            Original
            <a href='https://github.com/cc-ai/climategan'>
                ClimateGAN GitHub Repo
            </a>
            &nbsp;&nbsp;|&nbsp;&nbsp;
            Read the original
            <a
                href='https://openreview.net/forum?id=EZNOb_uNpJk'
                target='_blank'>
            ICLR 2021 ClimateGAN paper
            </a>
        </p>
        """
    ),
    dedent(
        """
        ## How to use this Space

        1. Enter an address or upload a Street View image
        2. Select the type of Painter you'd like to use for the flood renderings
        3. Click on the "See for yourself!" button
        4. Wait for the inference to complete, typically around 30 seconds
            (plus queue time)
        5. Enjoy the results!

            1. The prompt for Stable Diffusion is `An HD picture of a street with
            dirty water after a heavy flood`
            2. Pay attention to potential "inventions" by Stable Diffusion's in-painting
            3. The "restricted to masked area" SD output is the result of:
            `y = mask * flooded + (1-mask) * input`

        """
    ),
]
CSS = dedent(
    """
    a {
        color: #0088ff;
        text-decoration: underline;
    }
    strong {
        color: #c34318;
        font-weight: bolder;
    }
    #how-to-use-md li {
        margin: 0.1em;
    }
    #how-to-use-md li p {
        margin: 0.1em;
    }
    """
)


def toggle(radio):
    if "address" in radio.lower():
        return [
            gr.update(visible=True),
            gr.update(visible=False),
            gr.update(visible=True),
        ]
    else:
        return [
            gr.update(visible=False),
            gr.update(visible=True),
            gr.update(visible=True),
        ]


def predict(cg: ClimateGAN, api_key):
    def _predict(*args):
        print(f"Starting inference ({str(datetime.now())})")
        image = place = painter = radio = None
        if api_key:
            radio, image, place, painter = args
        else:
            image, painter = args

        if api_key and place and "address" in radio.lower():
            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)
            print("Using GSV image")
        else:
            print("Using user image")
            img_np = image

        painters = {
            "ClimateGAN Painter": "climategan",
            "Stable Diffusion Painter": "stable_diffusion",
            "Both": "both",
        }
        print("Using painter", painters[painter])
        output_dict = cg.infer_single(
            img_np,
            painters[painter],
            concats=[
                "input",
                "masked_input",
                "climategan_flood",
                "stable_copy_flood",
            ],
            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"], 3, axis=-1)
        segmentation = output_dict["segmentation"]

        climategan_flood = output_dict.get(
            "climategan_flood",
            np.ones(input_image.shape, dtype=np.uint8) * 255,
        )
        stable_flood = output_dict.get(
            "stable_flood",
            np.ones(input_image.shape, dtype=np.uint8) * 255,
        )
        stable_copy_flood = output_dict.get(
            "stable_copy_flood",
            np.ones(input_image.shape, dtype=np.uint8) * 255,
        )
        concat = output_dict.get(
            "concat",
            np.ones(input_image.shape, dtype=np.uint8) * 255,
        )

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

    return _predict


if __name__ == "__main__":

    ip = get("https://api.ipify.org").content.decode("utf8")
    print("My public IP address is: {}".format(ip))

    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()

    radio = address = None
    pred_ins = []
    pred_outs = []

    with gr.Blocks(css=CSS) as app:
        with Row():
            with Column():
                Markdown("# ClimateGAN: Visualize Climate Change")
                HTML(TEXTS[0])
            with Column():
                Markdown(TEXTS[1], elem_id="how-to-use-md")
        with Row():
            HTML("<hr><br><h2 style='font-size: 1.5rem;'>Choose Inputs</h2>")
        with Row():
            with Column():
                if api_key:
                    radio = Radio(["From Address", "From Image"], label="Input Type")
                    pred_ins += [radio]
                im_inp = Image(label="Input Image", visible=not api_key)
                pred_ins += [im_inp]
                if api_key:
                    address = Textbox(label="Address or place name", visible=False)
                    pred_ins += [address]
            with Column():
                pred_ins += [
                    Dropdown(
                        choices=[
                            "ClimateGAN Painter",
                            "Stable Diffusion Painter",
                            "Both",
                        ],
                        label="Choose Flood Painter",
                        value="Both",
                    )
                ]
                btn = Button(
                    "See for yourself!",
                    label="Run",
                    variant="primary",
                    visible=not api_key,
                )
        with Row():
            Markdown("## Outputs")
        with Row():
            pred_outs += [Image(type="numpy", label="Original image")]
            pred_outs += [Image(type="numpy", label="Masked input image")]
            pred_outs += [Image(type="numpy", label="Segmentation map")]
            pred_outs += [Image(type="numpy", label="Depth map")]
        with Row():
            pred_outs += [Image(type="numpy", label="ClimateGAN-Flooded image")]
            pred_outs += [Image(type="numpy", label="Stable Diffusion-Flooded image")]
            pred_outs += [
                Image(
                    type="numpy",
                    label="Stable Diffusion-Flooded image (restricted to masked area)",
                )
            ]
        with Row():
            pred_outs += [Image(type="numpy", label="Comparison of flood images")]
        with Row():
            pred_outs += [Image(type="numpy", label="Wildfire")]
            pred_outs += [Image(type="numpy", label="Smog")]
            Image(type="numpy", label="Empty on purpose", interactive=False)
        btn.click(predict(cg, api_key), inputs=pred_ins, outputs=pred_outs)

        if api_key:
            radio.change(toggle, inputs=[radio], outputs=[address, im_inp, btn])

    app.launch(show_api=False)