import gradio as gr
from ultralytics import YOLO
from PIL import Image
import requests
import os
import random

model = YOLO('detector.pt')


def satellite_image_params(address, api_key, zoom, size):
    """
    Generate parameters for Google Maps API request based on given address, API key, zoom level, and image size.

    Parameters:
    address (str): The address to center the map on.
    api_key (str): Google Maps API key.
    zoom (int): Zoom level for the map.
    size (str): Size of the requested map image.

    Returns:
    dict: A dictionary of parameters for the API request.
    """
    params = {
        "center": address,
        "zoom": str(zoom),
        "size": size,
        "maptype": "satellite",
        "key": api_key
    }
    return params


def fetch_satellite_image(address, api_key, zoom=18, size="640x640"):
    """
    Fetches a satellite image from Google Maps API based on the given address, api_key, zoom level, and size.

    Parameters:
    address (str): The address for the satellite image.
    api_key (str): Google Maps API key.
    zoom (int): Zoom level for the satellite image.
    size (str): Size of the satellite image.

    Returns:
    str: File name of the saved satellite image or None if the request fails.
    """
    base_url = "https://maps.googleapis.com/maps/api/staticmap?"
    params = satellite_image_params(address, api_key, zoom=zoom, size=size)
    try:
        response = requests.get(base_url, params=params)
    except requests.exceptions.RequestException as e:
        print(e)
        return None
    if response.status_code == 200:
        image_data = response.content
        img_name = f"{'_'.join(address.split()[-2:])}.jpg"
        with open(img_name, "wb") as file:
            file.write(image_data)
        print("Image was downloaded successfully")
        return img_name


def plot_results(im_array, save_image=False, img_path="results.jpg"):
    """
     Converts an image array to a PIL image and optionally saves it.

     Parameters:
     im_array (numpy.ndarray): The image array to be converted.
     save_image (bool): Whether to save the image.
     img_path (str): Path to save the image.

     Returns:
     PIL.Image: The converted PIL image.
     """
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
    if save_image:
        im.save(img_path)  # save image
    return im


def solar_panel_predict(image, conf=0.45):
    """
    Analyzes an image to detect solar panels and returns an annotated image along with a relevant message.

    This function uses a model to detect solar panels in the given image. If solar panels are detected with confidence
    above the specified threshold, it selects a positive sentence; otherwise, it chooses a sentence encouraging
    solar panel installation. It also annotates the image with detection results.

    Parameters:
    image: The input image for solar panel detection.
    conf: Confidence threshold for detection, default is 0.5.

    Returns:
    Tuple of (annotated image, prediction message)
    """
    negative_setences = [
        "No solar panels yet? Your roof is a blank canvas waiting for a green masterpiece! 🎨🌱",
        "It's lonely up here without solar panels. Imagine the sun-powered parties you're missing! 🌞🎉",
        "Your roof could be a superhero in disguise. Just needs its solar cape! 🦸‍♂️☀️",
        "Clear skies, empty roof. It's the perfect opportunity to harness the sun! 🌤️🔋",
        "No panels detected – but don't worry, it's never too late to join the solar revolution and be a ray of hope! 🌍💡"]

    positive_sentences = [
        "Solar panels detected: You're not just saving money, you're also charging up Mother Earth's good vibes! 🌍💚",
        "Roof status: Sunny side up! Your panels are turning rays into awesome days! ☀️😎",
        "You've got solar power! Now your roof is cooler than a polar bear in sunglasses. 🐻‍❄️🕶️",
        "Green alert: Your roof is now a climate hero's cape! Solar panels are saving the day, one ray at a time. 🦸‍♂️🌞",
        "Solar panels spotted: Your roof is now officially a member of the Renewable Energy Rockstars Club! ⭐🌱"]

    results = model(image, stream=True, conf=conf)
    for result in results:
        annotated_image = result.plot()
        im = plot_results(annotated_image)

        r = result.boxes
        confi = r.conf.numpy().tolist()
        if not confi:
            prediction = random.choice(negative_setences)
        else:
            prediction = random.choice(positive_sentences)
        return im, prediction


def detector(address, api_key, zoom=18, size="640x640"):
    """
    Detects solar panels in a satellite image fetched based on the given address.

    Parameters:
    address (str): The address to fetch the satellite image of.
    api_key (str): Google Maps API key.
    zoom (int): Zoom level for the image.
    size (str): Size of the image.

    Returns:
    tuple: Prediction text and detected image.
    """
    img_name = fetch_satellite_image(address, api_key, zoom=zoom, size=size)
    im, prediction = solar_panel_predict(img_name)
    return im, prediction

custom_css = """
.feedback textarea {font-size: 20px !important;}
.centered-text {text-align: center; width: 100%;}
"""

with gr.Blocks(theme="HaleyCH/HaleyCH_Theme", title="Solar Panel Detector", css=custom_css) as app:
    # add logo
    gr.Markdown("# **Solar Panel Detector 2.0** 🛰️☀️", elem_classes="centered-text")
    with gr.Column(scale=1, variant="default"):
        gr.HTML(f"""
                <div style='text-align: center;'>
                    <img src='https://github.com/ArielDrabkin/Solar-Panel-Detector/blob/master/deployment/examples/DALL-E.jpeg?raw=true'
                     height='500' width='1200';'/>
                </div>
                """)
    gr.Markdown("## This app provides the ability to detect solar panels in a given address or a given image.")

    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")
    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])

    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)

    prediction = gr.Textbox(type="text", show_label=False, elem_classes="feedback")
    btn = gr.Button(value="Submit")
    btn.click(solar_panel_predict, inputs=im, outputs=[predicted_image, prediction])

    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()