File size: 5,336 Bytes
8a589e9 f2b954d c860e5a f2b954d 70cbfc1 f2b954d 52b4297 f2b954d 70cbfc1 f2b954d 8a589e9 f2b954d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
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()
|