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# 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
output_dict = cg.infer_single(img_np, painter)
input_image = output_dict["input"]
masked_input = output_dict["masked_input"]
wildfire = output_dict["wildfire"]
smog = output_dict["smog"]
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,
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=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"),
)
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()
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