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ChatGPT_Sever
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新增多api多篇并发flask

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majiahui@haimaqingfan.com 2 years ago
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      flask_sever_1.py

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flask_sever_1.py
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from flask import Flask, jsonify, Response
from flask import request
import redis
import uuid
import json
import time
import threading
from threading import Thread
from flask import send_file, send_from_directory
import os
from flask import make_response
import openai
import base64
import re
import urllib.parse as pa
import socket
app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
localhostip = s.getsockname()[0]
lock = threading.RLock()
pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=50, db=2, password='Zhicheng123*')
redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
pantten_second_biaoti = '[2二ⅡⅠ][、.]\s{0,}?[\u4e00-\u9fa5]+'
pantten_other_biaoti = '[2-9二三四五六七八九ⅡⅢⅣⅤⅥⅦⅧⅨ][、.]\s{0,}?[\u4e00-\u9fa5]+'
mulu_prompt = "请帮我根据题目为“{}”生成一个论文目录"
first_title_prompt = "论文题目是“{}”，目录是“{}”，请把其中的大标题“{}”的内容续写完整，保证续写内容不少于800字"
small_title_prompt = "论文题目是“{}”，目录是“{}”，请把其中的小标题“{}”的内容续写完整，保证续写内容不少于800字"
references_prompt = "论文题目是“{}”，目录是“{}”，请为这篇论文生成15篇左右的参考文献，要求其中有有中文参考文献不低于12篇，英文参考文献不低于2篇"
thank_prompt = "请以“{}”为题写一篇论文的致谢"
kaitibaogao_prompt = "请以《{}》为题目生成研究的主要的内容、背景、目的、意义，要求不少于100字"
chinese_abstract_prompt = "请以《{}》为题目生成论文摘要，要求不少于1500字"
english_abstract_prompt = "请把“{}”这段文字翻译成英文"
chinese_keyword_prompt = "请为“{}”这段论文摘要生成3-5个关键字"
english_keyword_prompt = "请把“{}”这几个关键字翻译成英文"
thanks = "致谢"
references = "参考文献"
dabiaoti = ["二", "三", "四", "五", "六", "七", "八", "九"]
project_data_txt_path = "/home/majiahui/ChatGPT_Sever/new_data_txt"
"""
key_list = [
{"ip": key-api},
{"ip": key-api},
{"ip": key-api},
]
redis_title = []
redis_title_ing = []
redis_small_task = [
{
uuid,
api_key,
mulu_title_id,
title,
mulu,
subtitle,
prompt
}
]
redis_res = [
{
"uuid":
"完成进度":
"标题":
"中文摘要":"",
"英文摘要"
"中文关键字"
"英文关键字"
"正文" : [""] * len(content)
}
] -
> list()
"""
openaikey_list = ["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",
"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",
"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",
"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",
"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",
"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",
"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",
"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",
"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"]
redis_key_name_openaikey_list = "openaikey_list_{}".format(str(localhostip))
redis_title = "redis_title"
redis_title_ing = "redis_title_ing"
redis_small_task = "redis_small_task"
redis_res = "redis_res"
for i in openaikey_list:
redis_.rpush(redis_key_name_openaikey_list, i)
def chat_kaitibaogao(api_key, uuid, main_parameter):
# t = Thread(target=chat_kaitibaogao, args=(api_key,
# uuid,
# main_parameter
# time.sleep(1)
openai.api_key = api_key
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": kaitibaogao_prompt.format(main_parameter[0])},
],
temperature=0.5
)
kaitibaogao = res.choices[0].message.content
# kaitibaogao_path = os.path.join(, "kaitibaogao.txt")
# with open(kaitibaogao_path, 'w', encoding='utf8') as f_kaitibaogao:
# f_kaitibaogao.write(kaitibaogao)
redis_.rpush(redis_key_name_openaikey_list, api_key)
lock.acquire()
res_dict_str = redis_.hget(redis_res, uuid)
res_dict = json.loads(res_dict_str)
res_dict["tasking_num"] += 1
res_dict["开题报告"] = kaitibaogao
res_dict_str = json.dumps(res_dict, ensure_ascii=False)
redis_.hset(redis_res, uuid, res_dict_str)
lock.release()
def chat_abstract_keyword(api_key, uuid, main_parameter):
# api_key,
# uuid,
# main_parameter
# time.sleep(7)
openai.api_key = api_key
# 生成中文摘要
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": chinese_abstract_prompt.format(main_parameter[0])},
],
temperature=0.5
)
chinese_abstract = res.choices[0].message.content
# 生成英文的摘要
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": english_abstract_prompt.format(chinese_abstract)},
],
temperature=0.5
)
english_abstract = res.choices[0].message.content
# 生成中文关键字
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": chinese_keyword_prompt.format(chinese_abstract)},
],
temperature=0.5
)
chinese_keyword = res.choices[0].message.content
# 生成英文关键字
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": english_keyword_prompt.format(chinese_keyword)},
],
temperature=0.5
)
english_keyword = res.choices[0].message.content
paper_abstract_keyword = {
"中文摘要": chinese_abstract,
"英文摘要": english_abstract,
"中文关键词": chinese_keyword,
"英文关键词": english_keyword
}
# json_str = json.dumps(paper_abstract_keyword, indent=4, ensure_ascii=False)
# abstract_keyword_path = os.path.join(uuid_path, "abstract_keyword.json")
# with open(abstract_keyword_path, 'w') as json_file:
# json_file.write(json_str)
#
# lock.acquire()
# api_key_list.append(api_key)
# lock.release()
redis_.rpush(redis_key_name_openaikey_list, api_key)
lock.acquire()
res_dict_str = redis_.hget(redis_res, uuid)
res_dict = json.loads(res_dict_str)
res_dict["tasking_num"] += 1
res_dict["中文摘要"] = paper_abstract_keyword["中文摘要"]
res_dict["英文摘要"] = paper_abstract_keyword["英文摘要"]
res_dict["中文关键词"] = paper_abstract_keyword["中文关键词"]
res_dict["英文关键词"] = paper_abstract_keyword["英文关键词"]
res_dict_str = json.dumps(res_dict, ensure_ascii=False)
redis_.hset(redis_res, uuid, res_dict_str)
lock.release()
def chat_content(api_key, uuid, main_parameter):
'''
:param api_key:
:param uuid:
:param main_parameter:
:return:
'''
content_index = main_parameter[0]
title = main_parameter[1]
mulu = main_parameter[2]
subtitle = main_parameter[3]
prompt = main_parameter[4]
if subtitle[:2] == "@@":
res_content = subtitle[2:]
else:
openai.api_key = api_key
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": prompt.format(title, mulu, subtitle)},
],
temperature=0.5
)
res_content = res.choices[0].message.content
redis_.rpush(redis_key_name_openaikey_list, api_key)
lock.acquire()
res_dict_str = redis_.hget(redis_res, uuid)
res_dict = json.loads(res_dict_str)
res_dict["tasking_num"] += 1
table_of_contents = res_dict["table_of_contents"]
table_of_contents[content_index] = res_content
res_dict["table_of_contents"] = table_of_contents
res_dict_str = json.dumps(res_dict, ensure_ascii=False)
redis_.hset(redis_res, uuid, res_dict_str)
lock.release()
def chat_thanks(api_key, uuid, main_parameter):
'''
:param api_key:
:param uuid:
:param main_parameter:
:return:
'''
# title,
# thank_prompt
title = main_parameter[0]
prompt = main_parameter[1]
openai.api_key = api_key
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": prompt.format(title)},
],
temperature=0.5
)
res_content = res.choices[0].message.content
redis_.rpush(redis_key_name_openaikey_list, api_key)
# "致谢": "",
# "参考文献": "",
# 加锁 读取redis生成致谢并存储
lock.acquire()
res_dict_str = redis_.hget(redis_res, uuid)
res_dict = json.loads(res_dict_str)
res_dict["tasking_num"] += 1
res_dict["致谢"] = res_content
res_dict_str = json.dumps(res_dict, ensure_ascii=False)
redis_.hset(redis_res, uuid, res_dict_str)
lock.release()
def chat_references(api_key, uuid, main_parameter):
'''
:param api_key:
:param uuid:
:param main_parameter:
:return:
'''
# title,
# mulu,
# references_prompt
title = main_parameter[0]
mulu = main_parameter[1]
prompt = main_parameter[2]
openai.api_key = api_key
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": prompt.format(title, mulu)},
],
temperature=0.5
)
res_content = res.choices[0].message.content
redis_.rpush(redis_key_name_openaikey_list, api_key)
# 加锁 读取resis并存储结果
lock.acquire()
res_dict_str = redis_.hget(redis_res, uuid)
res_dict = json.loads(res_dict_str)
res_dict["tasking_num"] += 1
res_dict["参考文献"] = res_content
res_dict_str = json.dumps(res_dict, ensure_ascii=False)
redis_.hset(redis_res, uuid, res_dict_str)
lock.release()
def threading_mulu(key_api, title, uuid):
'''
生成目录并吧任务拆解进入子任务的redis_list中和储存结果的redis_list中
:return:
'''
openai.api_key = key_api
res = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": mulu_prompt.format(title)},
],
temperature=0.5
)
redis_.rpush(redis_key_name_openaikey_list, key_api)
mulu = res.choices[0].message.content
mulu_list = str(mulu).split("\n")
mulu_list = [i.strip() for i in mulu_list if i != ""]
print(mulu_list)
cun_bool = False
table_of_contents = [mulu_list[0]]
for i in mulu_list[1:]:
result_second_biaoti_list = re.findall(pantten_second_biaoti, i)
result_other_biaoti_list = re.findall(pantten_other_biaoti, i)
if result_second_biaoti_list != []:
table_of_contents.append("@@" + i)
cun_bool = True
continue
if cun_bool == False:
continue
else:
if result_other_biaoti_list != []:
table_of_contents.append("@@" + i)
else:
table_of_contents.append(i)
print(table_of_contents)
# table_of_contents = table_of_contents[:3] + table_of_contents[-1:]
# print(table_of_contents)
thanks_references_bool_table = table_of_contents[-3:]
# thanks = "致谢"
# references = "参考文献"
if references in thanks_references_bool_table:
table_of_contents.remove(references)
if thanks in thanks_references_bool_table:
table_of_contents.remove(thanks)
# table_of_contents.append(thanks)
# table_of_contents.append(references)
# if thanks not in thanks_bool_table:
# table_of_contents.insert(-1, "致谢")
#
# if thanks not in thanks_bool_table:
# table_of_contents.insert(-1, "致谢")
print(len(table_of_contents))
small_task_list = []
# api_key,
# index,
# title,
# mulu,
# subtitle,
# prompt
kaitibaogao_task = {
"task_type": "kaitibaogao",
"uuid": uuid,
"main_parameter": [title]
}
chat_abstract_task = {
"task_type": "chat_abstract",
"uuid": uuid,
"main_parameter": [title]
}
small_task_list.append(kaitibaogao_task)
small_task_list.append(chat_abstract_task)
content_index = 0
while True:
if content_index == len(table_of_contents):
break
subtitle = table_of_contents[content_index]
if content_index == 0:
prompt = first_title_prompt
elif subtitle == "参考文献":
prompt = references_prompt
elif subtitle == "致谢":
prompt = thank_prompt
else:
prompt = small_title_prompt
print("请求的所有参数",
content_index,
title,
subtitle,
prompt)
paper_content = {
"task_type": "paper_content",
"uuid": uuid,
"main_parameter": [
content_index,
title,
mulu,
subtitle,
prompt
]
}
small_task_list.append(paper_content)
content_index += 1
thanks_task = {
"task_type": "thanks_task",
"uuid": uuid,
"main_parameter": [
title,
thank_prompt
]
}
references_task = {
"task_type": "references_task",
"uuid": uuid,
"main_parameter": [
title,
mulu,
references_prompt
]
}
small_task_list.append(thanks_task)
small_task_list.append(references_task)
for small_task in small_task_list:
small_task = json.dumps(small_task, ensure_ascii=False)
redis_.rpush(redis_small_task, small_task)
res = {
"uuid": uuid,
"num_small_task": len(small_task_list),
"tasking_num": 0,
"标题": title,
"目录": mulu,
"开题报告": "",
"任务书": "",
"中文摘要": "",
"英文摘要": "",
"中文关键词": "",
"英文关键词": "",
"正文": "",
"致谢": "",
"参考文献": "",
"table_of_contents": [""] * len(table_of_contents)
}
res = json.dumps(res, ensure_ascii=False)
redis_.hset(redis_res, uuid, res)
def threading_1():
# title, redis_key_name_openaikey_list
'''
生成目录
:param title:
:param redis_key_name_openaikey_list:
:return:
'''
while True:
if redis_.llen(redis_small_task) != 0: # 若队列中有元素就跳过
time.sleep(1)
continue
elif redis_.llen(redis_title) != 0 and redis_.llen(redis_key_name_openaikey_list) != 0:
title_uuid_dict_str = redis_.lpop(redis_title).decode('UTF-8')
api_key = redis_.lpop(redis_key_name_openaikey_list).decode('UTF-8')
# redis_title:{"id": id_, "title": title}
title_uuid_dict = json.loads(title_uuid_dict_str)
title = title_uuid_dict["title"]
uuid_id = title_uuid_dict["id"]
t = Thread(target=threading_mulu, args=(api_key,
title,
uuid_id,
))
t.start()
else:
time.sleep(1)
continue
def threading_2():
'''
顺序读取子任务
:return:
'''
while True:
if redis_.llen(redis_small_task) != 0 and redis_.llen(redis_key_name_openaikey_list) != 0:
# 执行小标题的任务
api_key = redis_.lpop(redis_key_name_openaikey_list).decode('UTF-8')
small_title = redis_.lpop(redis_small_task).decode('UTF-8')
small_title = json.loads(small_title)
task_type = small_title["task_type"]
uuid = small_title["uuid"]
main_parameter = small_title["main_parameter"]
# "task_type": "paper_content",
# "uuid": uuid,
# "main_parameter": [
# "task_type": "paper_content",
# "task_type": "chat_abstract",
# "task_type": "kaitibaogao",
if task_type == "kaitibaogao":
t = Thread(target=chat_kaitibaogao, args=(api_key,
uuid,
main_parameter
))
t.start()
elif task_type == "chat_abstract":
t = Thread(target=chat_abstract_keyword, args=(api_key,
uuid,
main_parameter
))
t.start()
elif task_type == "paper_content":
t = Thread(target=chat_content, args=(api_key,
uuid,
main_parameter
))
t.start()
elif task_type == "thanks_task":
t = Thread(target=chat_thanks, args=(api_key,
uuid,
main_parameter
))
t.start()
elif task_type == "references_task":
t = Thread(target=chat_references, args=(api_key,
uuid,
main_parameter
))
t.start()
else:
time.sleep(1)
continue
def threading_3():
while True:
res_end_list = []
res_dict = redis_.hgetall(redis_res)
for key, values in res_dict.items():
values_dict = json.loads(values)
# "num_small_task": len(small_task_list) - 1,
# "tasking_num": 0,
if int(values_dict["num_small_task"]) == int(values_dict["tasking_num"]):
res_end_list.append(key)
for key in res_end_list:
redis_.hdel(redis_res, key)
res_str = res_dict[key].decode("utf-8")
json_str = json.dumps(res_str, indent=4, ensure_ascii=False)
key = str(key, encoding="utf-8")
uuid_path = os.path.join(project_data_txt_path, key)
os.makedirs(uuid_path)
paper_content_path = os.path.join(uuid_path, "paper_content.json")
with open(paper_content_path, 'w') as json_file:
json_file.write(json_str)
"""
调用jar包
占位
"""
url_path_paper = "http://104.244.90.248:14000/download?filename_path={}/paper.docx".format(key)
url_path_kaiti = "http://104.244.90.248:14000/download?filename_path={}/paper_start.docx".format(key)
return_text = str({"id": key,
"content_url_path": url_path_paper,
"content_report_url_path": url_path_kaiti,
"probabilities": None,
"status_code": 200})
redis_.srem(redis_title_ing, key)
redis_.set(key, return_text, 28800)
time.sleep(1)
# def main(title):
# # print(request.remote_addr)
# # title = request.json["title"]
#
# id_ = str(uuid.uuid1())
# print(id_)
# redis_.rpush(redis_title, json.dumps({"id": id_, "title": title})) # 加入redis
@app.route("/chat", methods=["POST"])
def chat():
print(request.remote_addr)
title = request.json["title"]
id_ = str(uuid.uuid1())
print(id_)
redis_.rpush(redis_title, json.dumps({"id":id_, "title": title})) # 加入redis
return_text = {"texts": {'id': id_,}, "probabilities": None, "status_code": 200}
print("ok")
redis_.sadd(redis_title_ing, id_)
return jsonify(return_text) # 返回结果
@app.route("/download", methods=['GET'])
def download_file():
# 需要知道2个参数, 第1个参数是本地目录的path, 第2个参数是文件名(带扩展名)
# directory = os.path.join(project_data_txt_path, filename) # 假设在当前目录
# uuid_path, word_name = str(filename).split("/")
# word_path_root = os.path.join(project_data_txt_path, uuid_path)
# response = make_response(send_from_directory(word_path_root, word_name, as_attachment=True))
# response.headers["Content-Disposition"] = "attachment; filename={}".format(filename.encode().decode('latin-1'))
filename_path = request.args.get('filename_path', '')
filename = filename_path.split("/")[1]
path_name = os.path.join(project_data_txt_path, filename_path)
with open(path_name, 'rb') as f:
stream = f.read()
response = Response(stream, content_type='application/octet-stream')
response.headers['Content-disposition'] = 'attachment; filename={}'.format(filename)
return response
@app.route("/search", methods=["POST"])
def search():
id_ = request.json['id'] # 获取用户query中的文本 例如"I love you"
result = redis_.get(id_) # 获取该query的模型结果
if result is not None:
# redis_.delete(id_)
# result_dict = result.decode('UTF-8')
result_dict = eval(result)
# return_text = {"id":query_id, "load_result_path": load_result_path, "probabilities": None, "status_code": 200}
query_id = result_dict["id"]
# "content_url_path": url_path_paper,
# "content_report_url_path": url_path_kaiti,
content_url_path = result_dict["content_url_path"]
content_report_url_path = result_dict["content_report_url_path"]
probabilities = result_dict["probabilities"]
result_text = {'code': 200,
'content_url_path': content_url_path,
'content_report_url_path': content_report_url_path,
'probabilities': probabilities}
else:
querying_list = list(redis_.smembers(redis_title_ing))
querying_set = set()
for i in querying_list:
querying_set.add(i.decode())
querying_bool = False
if id_ in querying_set:
querying_bool = True
query_list_json = redis_.lrange(redis_title, 0, -1)
query_set_ids = set()
for i in query_list_json:
data_dict = json.loads(i)
query_id = data_dict['id']
query_set_ids.add(query_id)
query_bool = False
if id_ in query_set_ids:
query_bool = True
if querying_bool == True and query_bool == True:
result_text = {'code': "201", 'text': "", 'probabilities': None}
elif querying_bool == True and query_bool == False:
result_text = {'code': "202", 'text': "", 'probabilities': None}
else:
result_text = {'code': "203", 'text': "", 'probabilities': None}
return jsonify(result_text) # 返回结果
# threading_1 # 根据标题获取子任务，存入子任务序列
# threading_2 # 根据子任务生成结果，存入结果序列
# threading_3 # 根据存储的结果序列，看是否完成，如果完成输出json文件以及word
t = Thread(target=threading_1)
t.start()
t = Thread(target=threading_2)
t.start()
t = Thread(target=threading_3)
t.start()
if __name__ == '__main__':
# main("大型商业建筑人员疏散设计研究")
app.run(host="0.0.0.0", port=14002, threaded=True, debug=False)
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