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

os.system('gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K')
os.system('unzip release-github-v1.zip')
os.system('mkdir config')
os.system('mv model config')

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=400x400&location={loc['lat']},{loc['lng']}&fov=80&heading=70&pitch=0&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=["image", "image", "image", "image"],
    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=[
        "Kafka's Great Northern Way, Vancouver",
        "Simon Fraser University",
        "Duomo, Milano"
    ],
    css=".footer{display:none !important}",
).launch()