Update app.py

app.py

@@ -1,159 +1,176 @@
-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>"
-                ),
-            )
+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.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+    outputs=gr.outputs.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")
+#             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()