Update app.py

app.py

@@ -1,128 +1,16 @@
-# 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 tqdm import tqdm
+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']
+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
@@ -145,6 +33,12 @@ 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')
@@ -207,123 +101,12 @@ 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;
-}
-"""
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Text Classification App",
-                     description="This is a demo for text classification.", css=css, 
-                     
-                     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()
 
+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()
 
+#