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README.md

@@ -10,5 +10,4 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-sadasd
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,193 @@
+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-detector2/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 your address.\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()

detector.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08ba1e94a8c8850342d995a95875b6bbf2d0cb567803d33c945a8a4b4ba9e345
+size 6261081