app.py

import gradio as gr
import os
from SolarPanelDetector import solar_panel_predict, detector

# Custom CSS for styling the app
custom_css = """
.feedback textarea {font-size: 20px !important;}
.centered-text {text-align: center; width: 100%;}
.center-image {display: flex; justify-content: center; align-items: center;}
"""
# URL for the logo image
logo_url = 'https://raw.githubusercontent.com/ArielDrabkin/Solar-Panel-Detector/master/deployment/examples/DALL-E.jpeg'

# Starting Gradio app configuration
with gr.Blocks(theme="HaleyCH/HaleyCH_Theme", title="Solar Panel Detector", css=custom_css) as app:
 # Main title for the app
 gr.Markdown("# **Solar Panel Detector 2.0** 🛰️☀️", elem_classes="centered-text")

 # Displaying the logo
 with gr.Row(elem_classes="center-image"):
 gr.Image(logo_url, scale=1, height=450, width=700, show_label=False, show_download_button=False,
 show_share_button=False)

 # Description for using the app with address and Google Maps API
 gr.Markdown(
 "## This app provides the ability to detect solar panels in a given address or a given satellite image. "
 "You can learn all about the project and the model in [this article](https://levelup.gitconnected.com/solar-panel-detector-guide-to-an-end-to-end-computer-vision-project-using-nothing-but-free-a7ee3610de43)")

 # Instructions for address-based detection
 gr.Markdown("### Using by address with google maps:\n1. Enter an address or geographic coordinates.\n"
 "2. Insert your Google maps api key which you can get from - "
 "https://developers.google.com/maps/documentation/maps-static/get-api-key .\n"
 "3. Choose the zoom level (19 is the default).")
 address = gr.Textbox(label="Address")
 api_key = gr.Textbox(label="Google maps api key", type="password")
 zoom = gr.Slider(minimum=18, maximum=22, step=1, value=19, label="zoom")
 btn = gr.Button(value="Submit")

 # Layout for displaying predictions for address-based detection
 with gr.Row():
 predicted_image_address = gr.Image(type="pil", show_label=False, scale=1)
 prediction_address = gr.Textbox(type="text", show_label=False, scale=1, elem_classes="feedback")
 btn.click(detector, inputs=[address, api_key, zoom], outputs=[predicted_image_address, prediction_address])

 # Description for image-based detection
 gr.Markdown("### Using by a given image:\nUpload an image or use the examples below.")
 with gr.Row():
 im = gr.Image(type="pil", show_label=False, scale=1)
 predicted_image = gr.Image(type="pil", show_label=False, scale=1)

 # Layout for displaying predictions for image-based detection
 prediction = gr.Textbox(type="text", show_label=False, elem_classes="feedback")
 btn = gr.Button(value="Submit")

 # Function call for image-based detection
 btn.click(solar_panel_predict, inputs=im, outputs=[predicted_image, prediction])

 # Providing example images for quick testing
 gr.Markdown("### Image Examples")
 gr.Examples(
 examples=[os.path.join(os.path.dirname(__file__), "examples/Gottingen.jpg"),
 os.path.join(os.path.dirname(__file__), "examples/Tubingen.jpg"),
 os.path.join(os.path.dirname(__file__), "examples/San-Diego.jpg"),
 os.path.join(os.path.dirname(__file__), "examples/Ceske-Budejovice.jpg")],
 inputs=im,
 outputs=[predicted_image, prediction],
 fn=solar_panel_predict,
 cache_examples=False,
 )

if __name__ == "__main__":
 app.launch()