Update app.py

app.py

@@ -1,176 +1,159 @@
+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
-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?",
-    flagging_mode="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>"
-#                 ),
-#             )
+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")
+            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,
-#             )
+            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
+    return demo
 
 
-# if __name__ == "__main__":
-#     get_layout().launch()
+if __name__ == "__main__":
+    get_layout().launch()