Update app.py

app.py

@@ -33,64 +33,6 @@ def build_vocab(file_path, tokenizer, max_size, min_freq):
     return vocab_dic
 
 
-# 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 = 'cuda:0'
-# 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'))
-# 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 = input('输入语句:')
-#
-# 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]]))
 
 
 
@@ -156,44 +98,97 @@ def greet(text):
     # print(predic)
     # print('类别为：{}'.format(classes[predic[0]]))
     return classes[predic[0]]
-#
+
 css = """
 body {
     background-color: #f6f6f6;
-    font-family: Arial, sans-serif;
+    font-family:Arial, sans-serif;
+}
+
+.gradio-interface {
+    padding-top: 2rem;
+}
+
+.gradio-interface-header-logo {
+    display: flex;
+    align-items: center;
+}
+
+.gradio-interface-header-logo img {
+    height: 3rem;
+    margin-right: 1rem;
+}
+
+.gradio-interface-header-title {
+    font-size: 2rem;
+    font-weight: bold;
+    margin: 0;
+}
+
+.gradio-interface-inputs label {
+    font-weight: bold;
+}
+
+.gradio-interface-inputs gr-input input[type="text"], .gradio-interface-inputs gr-output textarea {
+    border: 1px solid #ccc;
+    border-radius: 0.25rem;
+    padding: 0.5rem;
+    font-size: 1rem;
+    width: 100%;
+    margin-bottom: 1rem;
+    resize: none;
+    height: 6rem;
+}
+
+.gradio-interface-outputs gr-output div {
+    border: 1px solid #ccc;
+    border-radius: 0.25rem;
+    padding: 0.5rem;
+    font-size: 1rem;
+    width: 100%;
+    margin-bottom: 1rem;
+    min-height: 6rem;
+}
+
+.gradio-interface-footer {
+    margin-top: 2rem;
 }
-.btn-primary {
+
+.gradio-interface-footer .btn-primary {
     background-color: #1abc9c;
     border-color: #1abc9c;
     color: #ffffff;
 }
-"""
-demo = gr.Interface(fn=greet, inputs="text", outputs="text",title="text-classification app",css=css)
 
+.gradio-interface-header-icon {
+    font-size: 2rem;
+    margin-right: 1rem;
+}
+
+.gradio-interface-footer-icon {
+    font-size: 2rem;
+    margin-left: 1rem;
+}
+
+.gradio-interface-header-icon.emoji-icon {
+    display: none;
+}
+
+.gradio-interface-header-icon.fa-icon {
+    display: inline-block;
+    font-family: 'Font Awesome 5 Free';
+    font-weight: 900;
+}
+
+.gradio-interface-header-icon.fa-icon:before {
+    content: '\f007';
+}
+
+"""
+demo = gr.Interface(fn=greet, inputs="text", outputs="text", title="text-classification app", 
+                     icon="&#x1F60E;", css=css)
 demo.launch()
-# with torch.no_grad():
-#     output=model(input)
-# print(output)
-
-#
-# start_time = time.time()
-# test_iter = build_iterator(test_data, config)
-# with torch.no_grad():
-#     predict_all = np.array([], dtype=int)
-#     labels_all = np.array([], dtype=int)
-#     for texts, labels in test_iter:
-#         # texts=texts.to(device)
-#         print(texts)
-#         outputs = model(texts)
-#         loss = F.cross_entropy(outputs, labels)
-#         labels = labels.data.cpu().numpy()
-#         predic = torch.max(outputs.data, 1)[1].cpu().numpy()
-#         labels_all = np.append(labels_all, labels)
-#         predict_all = np.append(predict_all, predic)
-#         break
-#     print(labels_all)
-#     print(predict_all)
-#
-#
+
+