Update consult_func.py

consult_func.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import os
+from app_utils import text_score
 
 os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
 
@@ -312,18 +313,59 @@ def visibility_choice4(choice):  # 根据选择选项的不同决定下一道题
         return gr.Radio(visible=False), gr.Radio(visible=True)
 
 
-def keyword(
-    text,
-):  # 设置关于自杀倾向问题的触发词(你决定要不要加一个文本负向概率大于一定值的判定)
-    keywords = ["轻生", "自杀", "死", "活着", "意义", "意思", "干劲"]
-    for i in range(len(keywords)):
-        if keywords[i].find(text) != -1:
-            return True
-    return False
-
 
 def visibility_choice5(text):  # 关于自杀倾向问题是否触发
     if keyword(text) is True:
         return gr.Radio(visible=True), gr.Column(visible=False)
     else:
         return gr.Radio(visible=False), gr.Column(visible=True)
+
+
+##########################################################
+# 获取next数组
+def get_next(T):
+    i = 0
+    j = -1
+    next_val = [-1] * len(T)
+    while i < len(T) - 1:
+        if j == -1 or T[i] == T[j]:
+            i += 1
+            j += 1
+            # next_val[i] = j
+            if i < len(T) and T[i] != T[j]:
+                next_val[i] = j
+            else:
+                next_val[i] = next_val[j]
+        else:
+            j = next_val[j]
+    return next_val
+
+
+# KMP算法
+def kmp(S, T):
+    i = 0
+    j = 0
+    next = get_next(T)
+    while i < len(S) and j < len(T):
+        if j == -1 or S[i] == T[j]:
+            i += 1
+            j += 1
+        else:
+            j = next[j]
+    if j == len(T):
+        return i - j
+    else:
+        return -1
+
+#识别负面词的函数
+def keyword(
+    text
+):      
+    negative_keywords = ["轻生", "自杀", "死", "活着", "意义", "意思", "干劲","自残","孤单","结束",
+                         "救","血","割腕","歇斯底里","抑郁","创伤","遗书","遗嘱","痛苦","跳楼","双向",
+                         "障碍","精神病","变态","一无是处","疯",]
+    text,score=text_score(text)
+    for word in negative_keywords:
+        if kmp(text,word)!=-1 and score>=0:
+            return True
+    return False