Update app.py

app.py

@@ -1,16 +1,128 @@
+# import argparse
+# import os
+# from importlib import import_module
+
+# import gradio as gr
+# from tqdm import tqdm
+# import models.TextCNN
+# import torch
+# import pickle as pkl
+# from utils import build_dataset
+
+# classes = ['金融类', '房地产类', '股票类', '教育类', '科技类', '社会类', '政治类', '体育类', '游戏类',
+#            '娱乐类']
+
+# MAX_VOCAB_SIZE = 10000  # 词表长度限制
+# UNK, PAD = '<UNK>', '<PAD>'  # 未知字，padding符号
+
+
+# def build_vocab(file_path, tokenizer, max_size, min_freq):
+#     vocab_dic = {}
+#     with open(file_path, 'r', encoding='UTF-8') as f:
+#         for line in tqdm(f):
+#             lin = line.strip()
+#             if not lin:
+#                 continue
+#             content = lin.split('\t')[0]
+#             for word in tokenizer(content):
+#                 vocab_dic[word] = vocab_dic.get(word, 0) + 1
+#         vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[
+#                      :max_size]
+#         vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
+#         vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
+#     return vocab_dic
+
+
+
+
+
+
+
+
+# def greet(text):
+#     parser = argparse.ArgumentParser(description='Chinese Text Classification')
+#     parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
+#     args = parser.parse_args()
+#     model_name = 'TextCNN'
+#     dataset = 'THUCNews'  # 数据集
+#     embedding = 'embedding_SougouNews.npz'
+#     x = import_module('models.' + model_name)
+
+#     config = x.Config(dataset, embedding)
+#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+#     model = models.TextCNN.Model(config)
+
+#     # vocab, train_data, dev_data, test_data = build_dataset(config, args.word)
+#     model.load_state_dict(torch.load('THUCNews/saved_dict/TextCNN.ckpt', map_location=torch.device('cpu')))
+#     model.to(device)
+#     model.eval()
+
+#     tokenizer = lambda x: [y for y in x]  # char-level
+#     if os.path.exists(config.vocab_path):
+#         vocab = pkl.load(open(config.vocab_path, 'rb'))
+#     else:
+#         vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
+#         pkl.dump(vocab, open(config.vocab_path, 'wb'))
+#     # print(f"Vocab size: {len(vocab)}")
+
+#     # content='时评：“国学小天才”录取缘何少佳话'
+#     content = text
+
+#     words_line = []
+#     token = tokenizer(content)
+#     seq_len = len(token)
+#     pad_size = 32
+#     contents = []
+
+#     if pad_size:
+#         if len(token) < pad_size:
+#             token.extend([PAD] * (pad_size - len(token)))
+#         else:
+#             token = token[:pad_size]
+#             seq_len = pad_size
+#     # word to id
+#     for word in token:
+#         words_line.append(vocab.get(word, vocab.get(UNK)))
+
+#     contents.append((words_line, seq_len))
+#     # print(words_line)
+#     # input = torch.LongTensor(words_line).unsqueeze(1).to(device)  # convert words_line to LongTensor and add batch dimension
+#     x = torch.LongTensor([_[0] for _ in contents]).to(device)
+
+#     # pad前的长度(超过pad_size的设为pad_size)
+#     seq_len = torch.LongTensor([_[1] for _ in contents]).to(device)
+#     input = (x, seq_len)
+#     # print(input)
+#     with torch.no_grad():
+#         output = model(input)
+#         predic = torch.max(output.data, 1)[1].cpu().numpy()
+#     # print(predic)
+#     # print('类别为：{}'.format(classes[predic[0]]))
+#     return classes[predic[0]]
+
+
+
+
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text", title="text-classification app", 
+#                      layout="vertical", description="This is a demo for text classification.")
+# demo.launch()
+
+#你可以使用 CSS 和 HTML 来自定义你的 Gradio 界面，以使其更具吸引力。以下是一个示例，其中使用了一些 CSS 样式和 HTML 标记来改进界面布局和风格：
+
+
 import argparse
 import os
 from importlib import import_module
 
 import gradio as gr
-from tqdm import tqdm
 import models.TextCNN
 import torch
 import pickle as pkl
-from utils import build_dataset
+from tqdm import tqdm
 
-classes = ['金融类', '房地产类', '股票类', '教育类', '科技类', '社会类', '政治类', '体育类', '游戏类',
-           '娱乐类']
+classes = ['金融类', '房地产类', '股票类', '教育类', '科技类', '社会类', '政治类', '体育类', '游戏类', '娱乐类']
 
 MAX_VOCAB_SIZE = 10000  # 词表长度限制
 UNK, PAD = '<UNK>', '<PAD>'  # 未知字，padding符号
@@ -33,12 +145,6 @@ def build_vocab(file_path, tokenizer, max_size, min_freq):
     return vocab_dic
 
 
-
-
-
-
-
-
 def greet(text):
     parser = argparse.ArgumentParser(description='Chinese Text Classification')
     parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
@@ -101,14 +207,142 @@ def greet(text):
     # print('类别为：{}'.format(classes[predic[0]]))
     return classes[predic[0]]
 
+# 自定义样式和布局
+css = """
+body {
+    background-color: #f8f8f8;
+    font-family: Arial, sans-serif;
+}
+
+.container {
+    max-width: 800px;
+   margin: 0 auto;
+    padding: 50px;
+}
+
+h1 {
+    font-size: 36px;
+    font-weight: bold;
+    color: #333333;
+    text-align: center;
+    margin-bottom: 50px;
+}
+
+.gradio-interface {
+    border: none;
+    box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+    border-radius: 10px;
+    overflow: hidden;
+    margin-bottom: 50px;
+}
+
+.gradio-input {
+    background-color: #ffffff;
+    border: none;
+    border-radius: 5px;
+    padding: 15px;
+    box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+    font-size: 18px;
+    color: #333333;
+    width: 100%;
+    margin-bottom: 20px;
+}
+
+.gradio-output {
+    background-color: #ffffff;
+    border: none;
+    border-radius: 5px;
+    padding: 15px;
+    box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+    font-size: 18px;
+    color: #333333;
+    width: 100%;
+    margin-bottom: 20px;
+}
+
+.gradio-interface:hover {
+    box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.2);
+}
+
+.gradio-interface:focus-within {
+    box-shadow: 0px 0px 20px rgba(0, 0, 0, 0.2);
+}
+
+.gradio-interface .input-group {
+    margin-bottom: 20px;
+}
+
+.gradio-interface .input-label {
+    font-size: 24px;
+    font-weight: bold;
+    color: #333333;
+    margin-bottom: 10px;
+}
+
+.gradio-interface .input-description {
+    font-size: 16px;
+    color: #666666;
+    margin-bottom: 20px;
+}
+
+.gradio-interface .output-label {
+    font-size: 24px;
+    font-weight: bold;
+    color: #333333;
+    margin-bottom: 10px;
+}
+
+.gradio-interface .output-description {
+    font-size: 16px;
+    color: #666666;
+    margin-bottom: 20px;
+}
 
+.gradio-interface .input-group input[type="text"]::placeholder {
+    color: #999999;
+}
 
+.gradio-button {
+    background-color: #333333;
+    color: #ffffff;
+    border: none;
+    border-radius: 5px;
+    padding: 15px 30px;
+    font-size: 18px;
+    font-weight: bold;
+    cursor: pointer;
+    transition: background-color 0.2s ease;
+}
 
+.gradio-button:hover {
+    background-color: #111111;
+}
+"""
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text", title="text-classification app", 
-                     layout="vertical", description="This is a demo for text classification.")
-demo.launch()
+html = """
+<div class="container">
+    <h1>Text Classification</h1>
+    <div class="gradio-interface">
+        <div class="input-group">
+            <label class="input-label">Input Text</label>
+            <div class="input-description">Enter the text that you want to classify:</div>
+            <input type="text" class="gradio-input" placeholder="Enter your text here" id="input_text">
+        </div>
+        <button type="button" class="gradio-button" id="classify_button">Classify</button>
+        <div class="output-group">
+            <label class="output-label">Classification Result</label>
+            <div class="output-description">The predicted class is:</div>
+            <div class="gradio-output" id="output_text"></div>
+        </div>
+    </div>
+</div>
+"""
 
+iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Text Classification App",
+                     layout="unaligned", description="This is a demo for text classification.", css=css, 
+                     allow_screenshot=False, allow_flagging=False, allow_share=False, allow_download=False,
+                     examples=[["今天天气真好"], ["这个手机真不错"], ["新冠疫情对经济的影响"]])
 
+iface.launch(html=html)