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import os |
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from textwrap import dedent |
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from urllib import parse |
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from requests import get |
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import googlemaps |
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import gradio as gr |
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import numpy as np |
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from gradio.components import ( |
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HTML, |
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Button, |
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Column, |
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Dropdown, |
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Image, |
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Markdown, |
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Radio, |
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Row, |
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Textbox, |
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) |
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from skimage import io |
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from datetime import datetime |
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from climategan_wrapper import ClimateGAN |
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TEXTS = [ |
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dedent( |
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""" |
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<p> |
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Climate change does not impact everyone equally. |
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This Space shows the effects of the climate emergency, |
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"one address at a time". |
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Visit the original experience at |
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<a href="https://thisclimatedoesnotexist.com/"> |
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ThisClimateDoesNotExist.com |
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</a> |
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</p> |
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<br> |
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<p> |
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Enter an address or upload a Street View image, and ClimateGAN |
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will generate images showing how the location could be impacted |
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by flooding, wildfires, or smog if it happened there. |
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</p> |
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<br> |
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<p> |
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This is <strong>NOT</strong> an exercise in climate prediction, |
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rather an exercise of empathy, to put yourself in others' shoes, |
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as if Climate Change came crushing on your doorstep. |
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</p> |
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<br> |
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<p> |
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After you have selected an image and started the inference you |
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will see all the outputs of ClimateGAN, including intermediate |
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outputs such as the flood mask, the segmentation map and the |
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depth maps used to produce the 3 events. |
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</p> |
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<br> |
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<p> |
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This Space makes use of recent Stable Diffusion in-painting |
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pipelines to replace ClimateGAN's original Painter. If you |
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select 'Both' painters, you will see a comparison |
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</p> |
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<br> |
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<p style='text-align: center'> |
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Visit |
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<a href='https://thisclimatedoesnotexist.com/'> |
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ThisClimateDoesNotExist.com</a> |
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for more information |
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Original |
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<a href='https://github.com/cc-ai/climategan'> |
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ClimateGAN GitHub Repo |
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</a> |
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Read the original |
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<a |
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href='https://openreview.net/forum?id=EZNOb_uNpJk' |
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target='_blank'> |
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ICLR 2021 ClimateGAN paper |
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</a> |
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</p> |
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""" |
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), |
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dedent( |
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""" |
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## How to use this Space |
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1. Enter an address or upload a Street View image |
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2. Select the type of Painter you'd like to use for the flood renderings |
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3. Click on the "See for yourself!" button |
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4. Wait for the inference to complete, typically around 30 seconds |
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(plus queue time) |
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5. Enjoy the results! |
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1. The prompt for Stable Diffusion is `An HD picture of a street with |
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dirty water after a heavy flood` |
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2. Pay attention to potential "inventions" by Stable Diffusion's in-painting |
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3. The "restricted to masked area" SD output is the result of: |
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`y = mask * flooded + (1-mask) * input` |
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""" |
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), |
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] |
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CSS = dedent( |
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""" |
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a { |
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color: #0088ff; |
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text-decoration: underline; |
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} |
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strong { |
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color: #c34318; |
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font-weight: bolder; |
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} |
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#how-to-use-md li { |
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margin: 0.1em; |
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} |
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#how-to-use-md li p { |
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margin: 0.1em; |
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} |
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""" |
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) |
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def toggle(radio): |
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if "address" in radio.lower(): |
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return [ |
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gr.update(visible=True), |
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gr.update(visible=False), |
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gr.update(visible=True), |
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] |
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else: |
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return [ |
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gr.update(visible=False), |
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gr.update(visible=True), |
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gr.update(visible=True), |
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] |
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def predict(cg: ClimateGAN, api_key): |
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def _predict(*args): |
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print(f"Starting inference ({str(datetime.now())})") |
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image = place = painter = radio = None |
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if api_key: |
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radio, image, place, painter = args |
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else: |
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image, painter = args |
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if api_key and place and "address" in radio.lower(): |
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geocode_result = gmaps.geocode(place) |
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address = geocode_result[0]["formatted_address"] |
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static_map_url = f"https://maps.googleapis.com/maps/api/streetview?size=640x640&location={parse.quote(address)}&source=outdoor&key={api_key}" |
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img_np = io.imread(static_map_url) |
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print("Using GSV image") |
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else: |
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print("Using user image") |
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img_np = image |
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painters = { |
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"ClimateGAN Painter": "climategan", |
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"Stable Diffusion Painter": "stable_diffusion", |
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"Both": "both", |
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} |
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print("Using painter", painters[painter]) |
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output_dict = cg.infer_single( |
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img_np, |
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painters[painter], |
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concats=[ |
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"input", |
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"masked_input", |
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"climategan_flood", |
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"stable_copy_flood", |
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], |
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as_pil_image=True, |
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) |
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input_image = output_dict["input"] |
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masked_input = output_dict["masked_input"] |
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wildfire = output_dict["wildfire"] |
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smog = output_dict["smog"] |
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depth = np.repeat(output_dict["depth"], 3, axis=-1) |
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segmentation = output_dict["segmentation"] |
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climategan_flood = output_dict.get( |
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"climategan_flood", |
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np.ones(input_image.shape, dtype=np.uint8) * 255, |
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) |
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stable_flood = output_dict.get( |
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"stable_flood", |
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np.ones(input_image.shape, dtype=np.uint8) * 255, |
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) |
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stable_copy_flood = output_dict.get( |
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"stable_copy_flood", |
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np.ones(input_image.shape, dtype=np.uint8) * 255, |
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) |
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concat = output_dict.get( |
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"concat", |
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np.ones(input_image.shape, dtype=np.uint8) * 255, |
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) |
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return ( |
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input_image, |
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masked_input, |
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segmentation, |
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depth, |
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climategan_flood, |
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stable_flood, |
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stable_copy_flood, |
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concat, |
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wildfire, |
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smog, |
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) |
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return _predict |
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if __name__ == "__main__": |
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ip = get("https://api.ipify.org").content.decode("utf8") |
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print("My public IP address is: {}".format(ip)) |
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api_key = os.environ.get("GMAPS_API_KEY") |
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gmaps = None |
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if api_key is not None: |
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gmaps = googlemaps.Client(key=api_key) |
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cg = ClimateGAN( |
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model_path="config/model/masker", |
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dev_mode=os.environ.get("CG_DEV_MODE", "").lower() == "true", |
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) |
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cg._setup_stable_diffusion() |
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radio = address = None |
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pred_ins = [] |
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pred_outs = [] |
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with gr.Blocks(css=CSS) as app: |
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with Row(): |
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with Column(): |
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Markdown("# ClimateGAN: Visualize Climate Change") |
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HTML(TEXTS[0]) |
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with Column(): |
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Markdown(TEXTS[1], elem_id="how-to-use-md") |
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with Row(): |
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HTML("<hr><br><h2 style='font-size: 1.5rem;'>Choose Inputs</h2>") |
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with Row(): |
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with Column(): |
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if api_key: |
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radio = Radio(["From Address", "From Image"], label="Input Type") |
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pred_ins += [radio] |
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im_inp = Image(label="Input Image", visible=not api_key) |
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pred_ins += [im_inp] |
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if api_key: |
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address = Textbox(label="Address or place name", visible=False) |
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pred_ins += [address] |
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with Column(): |
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pred_ins += [ |
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Dropdown( |
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choices=[ |
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"ClimateGAN Painter", |
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"Stable Diffusion Painter", |
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"Both", |
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], |
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label="Choose Flood Painter", |
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value="Both", |
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) |
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] |
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btn = Button( |
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"See for yourself!", |
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label="Run", |
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variant="primary", |
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visible=not api_key, |
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) |
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with Row(): |
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Markdown("## Outputs") |
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with Row(): |
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pred_outs += [Image(type="numpy", label="Original image")] |
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pred_outs += [Image(type="numpy", label="Masked input image")] |
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pred_outs += [Image(type="numpy", label="Segmentation map")] |
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pred_outs += [Image(type="numpy", label="Depth map")] |
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with Row(): |
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pred_outs += [Image(type="numpy", label="ClimateGAN-Flooded image")] |
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pred_outs += [Image(type="numpy", label="Stable Diffusion-Flooded image")] |
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pred_outs += [ |
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Image( |
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type="numpy", |
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label="Stable Diffusion-Flooded image (restricted to masked area)", |
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) |
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] |
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with Row(): |
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pred_outs += [Image(type="numpy", label="Comparison of flood images")] |
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with Row(): |
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pred_outs += [Image(type="numpy", label="Wildfire")] |
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pred_outs += [Image(type="numpy", label="Smog")] |
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Image(type="numpy", label="Empty on purpose", interactive=False) |
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btn.click(predict(cg, api_key), inputs=pred_ins, outputs=pred_outs) |
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if api_key: |
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radio.change(toggle, inputs=[radio], outputs=[address, im_inp, btn]) |
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app.launch(show_api=False) |
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