app.py

import matplotlib.pyplot as plt
import torch
from PIL import Image
from torchvision import transforms
import torch.nn.functional as F
from typing import Literal, Any
import gradio as gr
import spaces
from io import BytesIO


class Classifier:
    LABELS = [
        "Panoramic",
        "Feature",
        "Detail",
        "Enclosed",
        "Focal",
        "Ephemeral",
        "Canopied",
    ]

    @spaces.GPU(duration=60)
    def __init__(
        self, model_path="Litton-7type-visual-landscape-model.pth", device=None
    ):
        if device is None:
            self.device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
        else:
            self.device = device

        self.device = device
        self.model = torch.load(
            model_path, map_location=self.device, weights_only=False
        )
        if hasattr(self.model, "module"):
            self.model = self.model.module
        self.model.eval()
        self.preprocess = transforms.Compose(
            [
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        )

    @spaces.GPU(duration=60)
    def predict(self, image: Image.Image) -> tuple[Literal["Failed", "Success"], Any]:
        image = image.convert("RGB")
        input_tensor = self.preprocess(image).unsqueeze(0).to(self.device)

        with torch.no_grad():
            logits = self.model(input_tensor)
            probs = F.softmax(logits[:, :7], dim=1).cpu()

        return draw_bar_chart(
            {
                "class": self.LABELS,
                "probs": probs[0] * 100,
            }
        )


def draw_bar_chart(data: dict[str, list[str | float]]):
    classes = data["class"]
    probabilities = data["probs"]

    plt.figure(figsize=(8, 6))
    plt.bar(classes, probabilities, color="skyblue")

    plt.xlabel("Class")
    plt.ylabel("Probability (%)")
    plt.title("Class Probabilities")

    for i, prob in enumerate(probabilities):
        plt.text(i, prob + 0.01, f"{prob:.2f}", ha="center", va="bottom")

    plt.tight_layout()

    return plt


def get_layout():
    css = """
    .main-title {
        font-size: 24px;
        font-weight: bold;
        text-align: center;
        margin-bottom: 20px;
    }
    .reference {
        text-align: center;
        font-size: 1.2em;
        color: #d1d5db;
        margin-bottom: 20px;
    }
    .reference a {
        color: #FB923C;
        text-decoration: none;
    }
    .reference a:hover {
        text-decoration: underline;
        color: #FB923C;
    }
    .title {
        border-bottom: 1px solid;
    }
    .footer {
        text-align: center;
        margin-top: 30px;
        padding-top: 20px;
        border-top: 1px solid #ddd;
        color: #d1d5db;
        font-size: 14px;
    }
    """
    theme = gr.themes.Base(
        primary_hue="orange",
        secondary_hue="orange",
        neutral_hue="gray",
        font=gr.themes.GoogleFont("Source Sans Pro"),
    ).set(
        background_fill_primary="*neutral_950",  # 主背景色（深黑）
        button_primary_background_fill="*primary_500",  # 按鈕顏色（橘色）
        body_text_color="*neutral_200",  # 文字顏色（淺色）
    )
    # with gr.Blocks(css=css, theme=theme) as demo:
    with gr.Blocks() as demo:
        with gr.Column():
            gr.HTML(
                value=(
                    '<div class="main-title">Litton7景觀分類模型</div>'
                    '<div class="reference">引用資料：'
                    '<a href="https://www.airitilibrary.com/Article/Detail/10125434-N202406210003-00003" target="_blank">'
                    "何立智、李沁築、邱浩修(2024)。Litton7：Litton視覺景觀分類深度學習模型。戶外遊憩研究，37(2)"
                    "</a>"
                    "</div>"
                ),
            )
    
            with gr.Row(equal_height=True):
                image_input = gr.Image(label="上傳影像", type="pil")
                chart = gr.Image(label="分類結果")

            start_button = gr.Button("開始分類", variant="primary")
            gr.HTML(
                '<div class="footer">© 2024 LCL 版權所有<br>開發者：何立智、楊哲睿</div>',
            )
            start_button.click(
                fn=Classifier().predict,
                inputs=image_input,
                outputs=chart,
            )

    return demo


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