合并item relate函数进入file load，并更新其调用方式

ItemRelate.py

@@ -1,29 +0,0 @@
-import os
-os.environ["OPENAI_API_KEY"]= "sk-PRJ811XeKzEy20Ug3dA98a34Af8b40B5816dE15503D33599"
-os.environ["OPENAI_BASE_URL"]= "http://154.9.28.247:3000/v1/"
-
-from openai import OpenAI
-client = OpenAI()
-
-from sklearn.metrics.pairwise import cosine_similarity
-
-def main(scale):
-
-	item=[]
-	vec=[]
-
-	for i in scale:
-		item.append(i)
-		vec.append(client.embeddings.create(
-    		input=scale[i], model="text-embedding-3-small" # nomic-embed-text text-embedding-3-small
-			).data[0].embedding)
-
-	simi=cosine_similarity(vec)
-
-	que=[]
-
-	for i,v in enumerate(simi):
-		for j in range(0,i):
-			que.append({"from":item[j], "to":item[i], "similarity":simi[i][j]})
-
-	return 	sorted(que, key = lambda t : t["similarity"], reverse=True)

file_load.py

@@ -1,9 +1,32 @@
-from ItemRelate import main as relate
-
 import json
 import os
 import random
 
+from openai import OpenAI
+client = OpenAI()
+
+os.environ["OPENAI_API_KEY"]= "sk-PRJ811XeKzEy20Ug3dA98a34Af8b40B5816dE15503D33599"
+os.environ["OPENAI_BASE_URL"]= "http://154.9.28.247:3000/v1/"
+
+
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+def calc_similarity(scale):
+	item=[]
+	vec=[]
+	for i in scale:
+		item.append(i)
+		vec.append(client.embeddings.create(
+    		input=scale[i], model="text-embedding-3-small" # nomic-embed-text text-embedding-3-small
+			).data[0].embedding)
+	simi=cosine_similarity(vec)
+	que=[]
+	for i,v in enumerate(simi):
+		for j in range(0,i):
+			que.append({"from":item[j], "to":item[i], "similarity":simi[i][j]})
+	return 	sorted(que, key = lambda t : t["similarity"], reverse=True)
+
 def batch():
 	scales = os.listdir("Scales")
 	items={}
@@ -25,31 +48,26 @@ def old_type(str):
 	with open(str,"w") as file:
 		file.write(json.dumps(new))
 
-def data():
-	s=""
-
-	item = batch()
-	for i in item:
-		s+=i+','
-	s=s[:-1]+'\n'
-
-	for i in range(0,1000):
-		s += str(random.randint(0,4))
-		for j in range(1,20):
-			s += ',' + str(random.randint(0,4))
-		s+='\n'
-
-	with open("Temp/data.csv","w") as data:
-		data.write(s)
-
-
-def main(force:bool = False):
+def calc_similarity(force:bool = False):
 	if force or os.path.getsize("Temp/items.json") == 0:
-		que=relate(batch())
+		que=embedding(batch())
 		with open("Temp/items.json","w") as items:
 			items.write(json.dumps(que))
 	else:
 		with open("Temp/items.json","r") as items:
 			que = json.load(items)
-	data()
 	return que
+
+def data():
+	s=""
+	item = batch()
+	for i in item:
+		s+=i+','
+	s=s[:-1]+'\n'
+	for i in range(0,1000):
+		s += str(random.randint(0,4))
+		for j in range(1,20):
+			s += ',' + str(random.randint(0,4))
+		s+='\n'
+	with open("Temp/data.csv","w") as data:
+		data.write(s)

main.py

@@ -1,10 +1,11 @@
-from file_load import main as init
+import file_load 
 
 import json
 import os
 
 
-que = init()
+similarity = file_load.similarity()
+file_load.data()
 
-for i in que:
+for i in similarity:
 	print(i)