app.py

import os
import gradio as gr
import googlemaps
from skimage import io

from inferences import ClimateGAN

API_KEY = os.environ.get("API_KEY")
gmaps = googlemaps.Client(key=API_KEY)
model = ClimateGAN(model_path="config/model/masker")

def predict(place):
    geocode_result = gmaps.geocode(place)
    loc = geocode_result[0]['geometry']['location']
    static_map_url = f"https://maps.googleapis.com/maps/api/streetview?size=640x640&location={loc['lat']},{loc['lng']}&source=outdoor&key={API_KEY}"

    img_np = io.imread(static_map_url)
    flood, wildfire, smog = model.inference(img_np)
    return img_np, flood, wildfire, smog


gr.Interface(
    predict,
    inputs=[
        gr.inputs.Textbox(label="Address or place name")
    ],
    outputs=[
        gr.outputs.Image(type="numpy", label="Original image"),
        gr.outputs.Image(type="numpy", label="Flooding"),
        gr.outputs.Image(type="numpy", label="Wildfire"),
        gr.outputs.Image(type="numpy", label="Smog"),
    ],
    title="ClimateGAN",
    description="Enter an address or place name, and ClimateGAN will generate images showing how the location could be impacted by flooding, wildfires, or smog.",
    article="<p style='text-align: center'>This project is a clone of <a href='https://thisclimatedoesnotexist.com/'>ThisClimateDoesNotExist</a> | <a href='https://github.com/cc-ai/climategan'>ClimateGAN GitHub Repo</a></p>",
    examples=[
        "Vancouver Art Gallery",
        "Chicago Bean",
        "Duomo Siracusa"
    ],
    css=".footer{display:none !important}",
).launch(cache_examples=True)