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={}
	for i in scales:
		with open("Scales/"+i,"r") as scale:
			tmp = json.load(scale)
			for i in tmp["item"]:
				items[i]=tmp["item"][i]
	# print(items)
	return items

def old_type(str):
	with open(str,"r") as file:
		scale=json.load(file)
	new={"item":{}}
	for i in scale:
		new["item"][i["name"]]=i["label"]
		# print(i["name"],i["label"])
	with open(str,"w") as file:
		file.write(json.dumps(new))

def calc_similarity(force:bool = False):
	if force or os.path.getsize("Temp/items.json") == 0:
		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)
	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)