add custom siblings

app.py

@@ -1,7 +1,7 @@
 import math, torch, gradio as gr
 
 from lex_rank import LexRank
-from lex_rank_distiluse_v1 import LexRankDistiluseV1
+from lex_rank_text2vec_v1 import LexRankText2VecV1
 from lex_rank_L12 import LexRankL12
 from sentence_transformers import SentenceTransformer, util
 
@@ -9,14 +9,14 @@ from sentence_transformers import SentenceTransformer, util
 # ---===--- instances ---===---
 embedder = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
 lex = LexRank()
-lex_distiluse_v1 = LexRankDistiluseV1()
+lex_distiluse_v1 = LexRankText2VecV1()
 lex_l12 = LexRankL12()
 
 
 # 摘要方法1
-def extract_handler(content):
+def extract_handler(content, siblings, num):
     summary_length = math.ceil(len(content) / 10)
-    sentences = lex.find_central(content)
+    sentences = lex.find_central(content, siblings=siblings, num=num)
     output = ""
     for index, sentence in enumerate(sentences):
         output += f"{index}: {sentence}\n"
@@ -24,9 +24,9 @@ def extract_handler(content):
 
 
 # 摘要方法2
-def extract_handler_distiluse_v1(content):
+def extract_handler_distiluse_v1(content, siblings, num):
     summary_length = math.ceil(len(content) / 10)
-    sentences = lex_distiluse_v1.find_central(content)
+    sentences = lex_distiluse_v1.find_central(content, siblings=siblings, num=num)
     output = ""
     for index, sentence in enumerate(sentences):
         output += f"{index}: {sentence}\n"
@@ -34,9 +34,9 @@ def extract_handler_distiluse_v1(content):
 
 
 # 摘要方法3
-def extract_handler_l12(content):
+def extract_handler_l12(content, siblings, num):
     summary_length = math.ceil(len(content) / 10)
-    sentences = lex_l12.find_central(content)
+    sentences = lex_l12.find_central(content, siblings=siblings, num=num)
     output = ""
     for index, sentence in enumerate(sentences):
         output += f"{index}: {sentence}\n"
@@ -69,16 +69,25 @@ with gr.Blocks() as app:
     gr.Markdown("从下面的标签选择测试模块 [摘要生成,相似度检测]")
     with gr.Tab("LexRank-mpnet"):
         text_input_1 = gr.Textbox(label="请输入长文本:", lines=10, max_lines=1000)
-        text_button_1 = gr.Button("生成摘要")
-        text_output_1 = gr.Textbox(label="摘要文本(长度设置为原文长度的1/10)", lines=10)
-    with gr.Tab("LexRank-distiluse"):
+        with gr.Row():
+            text_button_1 = gr.Button("生成摘要")
+            siblings_input_1 = gr.Textbox("请输入摘要的宽度半径, 默认为0, 即显示摘要本身.")
+            num_input_1 = gr.Textbox("摘要的条数, 默认10条")
+        text_output_1 = gr.Textbox(label="摘要文本", lines=10)
+    with gr.Tab("shibing624/text2vec-base-chinese-paraphrase"):
         text_input_2 = gr.Textbox(label="请输入长文本:", lines=10, max_lines=1000)
-        text_button_2 = gr.Button("生成摘要")
-        text_output_2 = gr.Textbox(label="摘要文本(长度设置为原文长度的1/10)", lines=10)
+        with gr.Row():
+            text_button_2 = gr.Button("生成摘要")
+            siblings_input_2 = gr.Textbox("请输入摘要的宽度半径, 默认为0, 即显示摘要本身.")
+            num_input_2 = gr.Textbox("摘要的条数, 默认10条")
+        text_output_2 = gr.Textbox(label="摘要文本", lines=10)
     with gr.Tab("LexRank-MiniLM-L12-v2"):
         text_input_3 = gr.Textbox(label="请输入长文本:", lines=10, max_lines=1000)
-        text_button_3 = gr.Button("生成摘要")
-        text_output_3 = gr.Textbox(label="摘要文本(长度设置为原文长度的1/10)", lines=10)
+        with gr.Row():
+            text_button_3 = gr.Button("生成摘要")
+            siblings_input_3 = gr.Textbox("请输入摘要的宽度半径, 默认为0, 即显示摘要本身.")
+            num_input_3 = gr.Textbox("摘要的条数, 默认10条")
+        text_output_3 = gr.Textbox(label="摘要文本", lines=10)
     with gr.Tab("相似度检测"):
         with gr.Row():
             text_input_query = gr.Textbox(lines=10, label="查询文本")
@@ -86,9 +95,9 @@ with gr.Blocks() as app:
         text_button_similarity = gr.Button("对比相似度")
         text_output_similarity = gr.Textbox()
 
-    text_button_1.click(extract_handler, inputs=text_input_1, outputs=text_output_1)
-    text_button_2.click(extract_handler_distiluse_v1, inputs=text_input_2, outputs=text_output_2)
-    text_button_3.click(extract_handler_l12, inputs=text_input_3, outputs=text_output_3)
+    text_button_1.click(extract_handler, inputs=[text_input_1, siblings_input_1, num_input_1], outputs=text_output_1)
+    text_button_2.click(extract_handler_distiluse_v1, inputs=[text_input_2, siblings_input_2, num_input_2], outputs=text_output_2)
+    text_button_3.click(extract_handler_l12, inputs=[text_input_3, siblings_input_3, num_input_3], outputs=text_output_3)
     text_button_similarity.click(similarity_search, inputs=[text_input_query, text_input_doc], outputs=text_output_similarity)
 
 app.launch(