fix

app.py

@@ -1,3 +1,5 @@
+import math
+
 import numpy
 import torch
 import gradio as gr
@@ -64,7 +66,8 @@ class LexRank(object):
     def __init__(self):
         self.model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
         self.ht = HarvestText()
-    def find_central(self, content: str):
+
+    def find_central(self, content: str, num=100):
         sentences = self.ht.cut_sentences(content)
         embeddings = self.model.encode(sentences, convert_to_tensor=True).cpu()
 
@@ -77,7 +80,7 @@ class LexRank(object):
         # We argsort so that the first element is the sentence with the highest score
         most_central_sentence_indices = numpy.argsort(-centrality_scores)
 
-        num = 100
+        # num = 100
         res = []
         for index in most_central_sentence_indices:
             if num < 0:
@@ -96,7 +99,8 @@ lex = LexRank()
 
 
 def randeng_extract(content):
-    sentences = lex.find_central(content)
+    summary_length = math.ceil(len(content) / 10)
+    sentences = lex.find_central(content, num=summary_length)
     output = "原文: \n"
     for index, sentence in enumerate(sentences):
         output += f"{index}: {sentence}\n"