app.py

import gradio as gr
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

def predict(image):
  predictions = pipeline(image)
  return {p["label"]: p["score"] for p in predictions}

gr.Interface(
    predict,
    inputs=gr.Image(label="Upload hot dog candidate", type="filepath"),
    outputs=gr.Label(num_top_classes=2),
    title="Hot Dog? Or Not?",
    allow_flagging="manual"
).launch()

# 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="cuda:0"
#     ):
#         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():
#     demo = gr.Interface(fn=Classifier().predict, inputs="image", outputs="plot")
#     return demo
#     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()