Add application file

app.py

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+import gradio as gr
+from scipy.spatial.distance import cosine
+from transformers import AutoModel, AutoTokenizer
+from argparse import Namespace
+import torch
+from LuotuoEmbedding.lib.tsne import TSNE_Plot
+
+tokenizer = AutoTokenizer.from_pretrained("silk-road/luotuo-bert")
+model_args = Namespace(do_mlm=None,
+                       pooler_type="cls",
+                       temp=0.05,
+                       mlp_only_train=False,
+                       init_embeddings_model=None)
+model = AutoModel.from_pretrained("silk-road/luotuo-bert",
+                  trust_remote_code=True,
+                  model_args=model_args)
+
+def divide_str(s, sep=['\n', '.', '。']):
+    mid_len = len(s) // 2  # 中心点位置
+    best_sep_pos = len(s) + 1  # 最接近中心点的分隔符位置
+    best_sep = None  # 最接近中心点的分隔符
+    for curr_sep in sep:
+        sep_pos = s.rfind(curr_sep, 0, mid_len)  # 从中心点往左找分隔符
+        if sep_pos > 0 and abs(sep_pos - mid_len) < abs(best_sep_pos - mid_len):
+            best_sep_pos = sep_pos
+            best_sep = curr_sep
+    if not best_sep:  # 没有找到分隔符
+        return s, ''
+    return s[:best_sep_pos + 1], s[best_sep_pos + 1:]
+
+def strong_divide( s ):
+  left, right = divide_str(s)
+
+  if right != '':
+    return left, right
+
+  whole_sep = ['\n', '.', '，', '、', ';', ',', '；',\
+               '：', '！', '？', '(', ')', '”', '“', \
+               '’', '‘', '[', ']', '{', '}', '<', '>', \
+               '/', '''\''', '|', '-', '=', '+', '*', '%', \
+               '$', '''#''', '@', '&', '^', '_', '`', '~',\
+               '·', '…']
+  left, right = divide_str(s, sep = whole_sep )
+
+  if right != '':
+    return left, right
+  
+  mid_len = len(s) // 2
+  return s[:mid_len], s[mid_len:]
+
+def generate_image(text_input):
+    # 将输入的文本按行分割并保存到列表中
+    text_input = text_input.split('\n')
+    label = []
+    for idx, i in enumerate(text_input):
+        if '#' in i:
+            label.append(i[i.find('#') + 1:])
+            text_input[idx] = i[:i.find('#')]
+        else:
+            label.append('No.{}'.format(idx))
+
+    divided_text = [strong_divide(i) for i in text_input]
+    text_left, text_right = [i[0] for i in divided_text], [i[1] for i in divided_text]
+    inputs = tokenizer(text_left, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+      embeddings_left = model(**inputs, output_hidden_states=True, return_dict=True, sent_emb=True).pooler_output
+    inputs = tokenizer(text_right, padding=True, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+      embeddings_right = model(**inputs, output_hidden_states=True, return_dict=True, sent_emb=True).pooler_output
+
+    merged_list = text_left + text_right
+    merged_embed = torch.cat((embeddings_left, embeddings_right), dim=0)
+    tsne_plot = TSNE_Plot(merged_list, merged_embed, label=label * 2, n_annotation_positions=len(merged_list))
+    fig = tsne_plot.tsne_plot(n_sentence=len(merged_list), return_fig=True)
+    return fig
+
+with gr.Blocks() as demo:
+    name = gr.inputs.Textbox(lines=20,
+          placeholder='在此输入歌词，每一行为一个输入，如果需要输入歌词对应的歌名，请用#隔开\n例如：听雷声 滚滚 他默默 闭紧嘴唇 停止吟唱暮色与想念 他此刻沉痛而危险 听雷声 滚滚 他渐渐 感到胸闷 乌云阻拦明月涌河湾 他起身独立向荒原#河北墨麒麟')
+    output = gr.Plot()
+    btn = gr.Button("Generate")
+    btn.click(fn=generate_image, inputs=name, outputs=output, api_name="generate-image")
+
+demo.launch(debug=True)

requirements.txt

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+kaleido
+gradio
+transformers
+openTSNE