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@@ -1,22 +1,22 @@
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# -*- coding:utf-8 -*-
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if __name__ == '__main__':
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import os
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+
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os.chdir("..")
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-import requests
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-import random
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-import json
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import time
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-from tools.loglog import logger,simple_logger,log_err_e
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-from tools.new_mysql import MySQLUploader
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-from typing import Optional, Dict, Any,Union
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+from typing import Dict, Any, Union
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+
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import httpx
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-import asyncio
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+import requests
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+from tools.loglog import logger, simple_logger, log_err_e
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+from tools.new_mysql import MySQLUploader
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m = MySQLUploader()
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-def get_openai_model(model_text:str):
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+
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+def get_openai_model(model_text: str):
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"""模糊获得模型名"""
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if "3.5" in model_text or "3.5-turbo" in model_text or "3.5turbo" in model_text:
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model = "gpt-3.5-turbo"
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@@ -29,16 +29,16 @@ def get_openai_model(model_text:str):
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return model
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-def insert_ip_token(ip,demo_name,gpt_content,prompt_tokens,completion_tokens,total_tokens):
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+def insert_ip_token(ip, demo_name, gpt_content, prompt_tokens, completion_tokens, total_tokens):
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sql = "insert into consumer_token (ip,demo_name,gpt_content,prompt_tokens,completion_tokens,total_tokens) values (%s,%s,%s,%s,%s,%s)"
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- m.execute_(sql,(ip,demo_name,str(gpt_content),prompt_tokens,completion_tokens,total_tokens))
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+ m.execute_(sql, (ip, demo_name, str(gpt_content), prompt_tokens, completion_tokens, total_tokens))
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+
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-def get_answer_from_gpt(question,real_ip="localhost",demo_name="无",model="gpt-4o",max_tokens=3500,temperature:float=0,
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- json_resp:Union[Dict[Any, Any],bool]=False,n=1,check_fucn=None,sys_prompt=None):
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+def get_answer_from_gpt(question, real_ip="localhost", demo_name="无", model="gpt-4o", max_tokens=3500, temperature: float = 0,
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+ json_resp: Union[Dict[Any, Any], bool] = False, n=1, check_fucn=None, sys_prompt=None):
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model = get_openai_model(model)
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-
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- d2 = {"model": model,"messages": [],"max_tokens": max_tokens,"temperature": temperature,'n': n}
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+ d2 = {"model": model, "messages": [], "max_tokens": max_tokens, "temperature": temperature, 'n': n}
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if sys_prompt:
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d2['messages'].append({"role": "system", "content": sys_prompt})
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d2['messages'].append({"role": "user", "content": question})
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@@ -52,36 +52,33 @@ def get_answer_from_gpt(question,real_ip="localhost",demo_name="无",model="gpt-
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for num_count in range(3):
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try:
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-
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+
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response = requests.post(f'http://170.106.108.95/v1/chat/completions', json=d2)
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r_json = response.json()
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- if r2:= r_json.get("choices",None):
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- if n>1:
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+ if r2 := r_json.get("choices", None):
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+ if n > 1:
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gpt_res = []
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for i in r2:
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gpt_res.append(i["message"]["content"])
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else:
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- gpt_res= r2[0]["message"]["content"]
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+ gpt_res = r2[0]["message"]["content"]
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-
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gpt_content = str(gpt_res)
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prompt_tokens = r_json["usage"]["prompt_tokens"]
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completion_tokens = r_json["usage"]["completion_tokens"]
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total_tokens = r_json["usage"]["total_tokens"]
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- insert_ip_token(real_ip,demo_name,gpt_content,prompt_tokens,completion_tokens,total_tokens)
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+ insert_ip_token(real_ip, demo_name, gpt_content, prompt_tokens, completion_tokens, total_tokens)
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simple_logger.info(f"问题日志：\n{question}\n回答日志：\n{gpt_res}")
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-
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if not check_fucn:
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return gpt_res
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-
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check_result = check_fucn(str(gpt_res))
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- if check_result:
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+ if check_result:
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return gpt_res
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else:
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- raise Exception(f"第{num_count+1}次共3次，GPT的校验没有通过，校验函数:{check_fucn.__name__}")
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+ raise Exception(f"第{num_count + 1}次共3次，GPT的校验没有通过，校验函数:{check_fucn.__name__}")
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elif r_json.get("message") == "IP address blocked":
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print("IP address blocked")
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@@ -95,8 +92,9 @@ def get_answer_from_gpt(question,real_ip="localhost",demo_name="无",model="gpt-
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logger.critical("get_answer_from_gpt 严重错误，3次后都失败了")
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-def get_article_gpt_pydantic(question, real_ip="localhost", demo_name="无", model="gpt-4.1", max_tokens=3500, temperature:float=0, n=1,
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- check_fucn=None, sys_prompt=None):
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+
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+def get_article_gpt_pydantic(question, real_ip="localhost", demo_name="无", model="gpt-4.1", max_tokens=3500, temperature: float = 0, n=1,
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+ check_fucn=None, sys_prompt=None):
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"""
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异步获取文章
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:param question: 问题
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@@ -110,8 +108,94 @@ def get_article_gpt_pydantic(question, real_ip="localhost", demo_name="无", mod
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:param sys_prompt: 系统提示
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:return: 文章内容
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"""
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-
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- d2 = {"model": model, "messages": [], "max_tokens": max_tokens, "temperature": temperature,"n":n, "response_format":{'type': 'json_schema', 'json_schema': {'name': 'Article', 'schema': {'$defs': {'Candidate': {'properties': {'label': {'title': 'Label', 'type': 'string'}, 'text': {'title': 'Text', 'type': 'string'}, 'isRight': {'title': 'Isright', 'type': 'integer'}}, 'required': ['label', 'text', 'isRight'], 'title': 'Candidate', 'type': 'object'}, 'DifficultSentence': {'properties': {'english': {'title': 'English', 'type': 'string'}, 'chinese': {'title': 'Chinese', 'type': 'string'}}, 'required': ['english', 'chinese'], 'title': 'DifficultSentence', 'type': 'object'}, 'Question': {'properties': {'trunk': {'title': 'Trunk', 'type': 'string'}, 'analysis': {'title': 'Analysis', 'type': 'string'}, 'candidates': {'items': {'$ref': '#/$defs/Candidate'}, 'title': 'Candidates', 'type': 'array'}}, 'required': ['trunk', 'analysis', 'candidates'], 'title': 'Question', 'type': 'object'}}, 'properties': {'difficultSentences': {'items': {'$ref': '#/$defs/DifficultSentence'}, 'title': 'Difficultsentences', 'type': 'array'}, 'usedMeanIds': {'items': {'type': 'integer'}, 'title': 'Usedmeanids', 'type': 'array'}, 'questions': {'items': {'$ref': '#/$defs/Question'}, 'title': 'Questions', 'type': 'array'}, 'englishArticle': {'title': 'Englisharticle', 'type': 'string'}, 'chineseArticle': {'title': 'Chinesearticle', 'type': 'string'}, 'allWordAmount': {'title': 'Allwordamount', 'type': 'integer'}}, 'required': ['difficultSentences', 'usedMeanIds', 'questions', 'englishArticle', 'chineseArticle', 'allWordAmount'], 'title': 'Article', 'type': 'object'}}}}
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+
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+ d2 = {"model": model, "messages": [], "max_tokens": max_tokens, "temperature": temperature, "n": n, "response_format": {'type': 'json_schema',
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+ 'json_schema': {
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+ 'name': 'Article',
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+ 'schema': {'$defs': {
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+ 'Candidate': {
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+ 'properties': {
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+ 'label': {
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+ 'title': 'Label',
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+ 'type': 'string'},
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+ 'text': {
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+ 'title': 'Text',
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+ 'type': 'string'},
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+ 'isRight': {
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+ 'title': 'Isright',
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+ 'type': 'integer'}},
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+ 'required': [
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+ 'label',
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+ 'text',
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+ 'isRight'],
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+ 'title': 'Candidate',
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+ 'type': 'object'},
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+ 'DifficultSentence': {
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+ 'properties': {
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+ 'english': {
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+ 'title': 'English',
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+ 'type': 'string'},
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+ 'chinese': {
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+ 'title': 'Chinese',
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+ 'type': 'string'}},
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+ 'required': [
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+ 'english',
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+ 'chinese'],
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+ 'title': 'DifficultSentence',
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+ 'type': 'object'},
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+ 'Question': {
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+ 'properties': {
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+ 'trunk': {
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+ 'title': 'Trunk',
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+ 'type': 'string'},
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+ 'analysis': {
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+ 'title': 'Analysis',
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+ 'type': 'string'},
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+ 'candidates': {
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+ 'items': {
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+ '$ref': '#/$defs/Candidate'},
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+ 'title': 'Candidates',
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+ 'type': 'array'}},
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+ 'required': [
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+ 'trunk',
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+ 'analysis',
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+ 'candidates'],
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+ 'title': 'Question',
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+ 'type': 'object'}},
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+ 'properties': {
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+ 'difficultSentences': {
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+ 'items': {
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+ '$ref': '#/$defs/DifficultSentence'},
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+ 'title': 'Difficultsentences',
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+ 'type': 'array'},
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+ 'usedMeanIds': {
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+ 'items': {
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+ 'type': 'integer'},
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+ 'title': 'Usedmeanids',
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+ 'type': 'array'},
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+ 'questions': {
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+ 'items': {
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+ '$ref': '#/$defs/Question'},
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+ 'title': 'Questions',
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+ 'type': 'array'},
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+ 'englishArticle': {
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+ 'title': 'Englisharticle',
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+ 'type': 'string'},
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+ 'chineseArticle': {
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+ 'title': 'Chinesearticle',
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+ 'type': 'string'},
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+ 'allWordAmount': {
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+ 'title': 'Allwordamount',
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+ 'type': 'integer'}},
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+ 'required': [
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+ 'difficultSentences',
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+ 'usedMeanIds',
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+ 'questions',
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+ 'englishArticle',
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+ 'chineseArticle',
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+ 'allWordAmount'],
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+ 'title': 'Article',
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+ 'type': 'object'}}}}
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if sys_prompt:
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d2['messages'].append({"role": "system", "content": sys_prompt})
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d2['messages'].append({"role": "user", "content": question})
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@@ -119,30 +203,26 @@ def get_article_gpt_pydantic(question, real_ip="localhost", demo_name="无", mod
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for num_count in range(3):
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try:
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response = requests.post('http://170.106.108.95/v1/chat/completions', json=d2)
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- r_json = response.json()
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+ r_json = response.json()
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simple_logger.info(f"问题日志：\n{question}\n回答日志：\n{r_json}")
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return r_json
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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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-
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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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except httpx.HTTPError as e:
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logger.error(f"HTTP请求错误: {str(e)}")
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- if num_count < 2:
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+ if num_count < 2:
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time.sleep(10)
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else:
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raise
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except Exception as e:
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- log_err_e(e,"其他错误")
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+ log_err_e(e, "其他错误")
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if num_count < 2:
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time.sleep(10)
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@@ -153,25 +233,23 @@ def get_article_gpt_pydantic(question, real_ip="localhost", demo_name="无", mod
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raise Exception("获取文章失败，已达到最大重试次数")
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-def parse_gpt_phon_to_tuplelist(text:str) -> list:
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+def parse_gpt_phon_to_tuplelist(text: str) -> list:
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"""解析gpt返回的音标数据"""
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result = []
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if not text:
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return []
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for i in text.split("\n"):
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ii = i.split("***")
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- if len(ii)>=3:
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- result.append((ii[0].strip(),ii[1].strip(),ii[2].strip()))
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+ if len(ii) >= 3:
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+ result.append((ii[0].strip(), ii[1].strip(), ii[2].strip()))
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return result
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if __name__ == '__main__':
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-
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question = "hello"
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-
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sys_prompt = "你是一个专业的英语老师，擅长根据用户提供的词汇生成对应的英语文章和中文翻译和4个配套选择题。"
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- q= """下面我会为你提供两组数据，[单词组1]和[单词组2]（里面包含词义id，英语单词，中文词义），优先使用[单词组1]内的单词，请根据这些单词的中文词义，生成一篇带中文翻译的考场英语文章，英语文章和中文翻译要有[标题]。注意这个单词有多个词义时，生成的英语文章一定要用提供的中文词义。并挑选一句复杂的句子和其中文翻译，放入difficultSentences。英语文章，放入"englishArticle"中。中文翻译，放入"chineseArticle"中。最终文中使用到的单词id放入"usedMeanIds"中。4个选择题，放入questions字段。questions结构下有4个选择题对象，其中trunk是[英语]问题文本，analysis是[中文]的问题分析，candidates是4个ABCD选项，内部有label是指选项序号A B C D ，text是[英语]选项文本，isRight是否正确答案1是正确0是错误。
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+ q = """下面我会为你提供两组数据，[单词组1]和[单词组2]（里面包含词义id，英语单词，中文词义），优先使用[单词组1]内的单词，请根据这些单词的中文词义，生成一篇带中文翻译的考场英语文章，英语文章和中文翻译要有[标题]。注意这个单词有多个词义时，生成的英语文章一定要用提供的中文词义。并挑选一句复杂的句子和其中文翻译，放入difficultSentences。英语文章，放入"englishArticle"中。中文翻译，放入"chineseArticle"中。最终文中使用到的单词id放入"usedMeanIds"中。4个选择题，放入questions字段。questions结构下有4个选择题对象，其中trunk是[英语]问题文本，analysis是[中文]的问题分析，candidates是4个ABCD选项，内部有label是指选项序号A B C D ，text是[英语]选项文本，isRight是否正确答案1是正确0是错误。
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要求:
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1.必须用提供的这个词义的单词，其他单词使用常见、高中难度的的单词。文章整体难度适中，大约和中国的高中生，中国CET-6，雅思6分这样的难度标准。
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@@ -183,7 +261,6 @@ if __name__ == '__main__':
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提供[单词组1]：4238 penalty:惩罚, 刑罚;4591 bare:赤裸的, 无遮蔽的;4227 stable:畜舍, 马厩;4236 psychology:心理学;4245 offense:进攻, 攻势, 冒犯, 触怒, 过错;4237 innocent:清白的, 无辜的, 天真的;4228 refrigerator:冰箱, 冷库;4247 tissue:(动植物)组织;4250 awareness:察觉, 觉悟, 意识;4234 mode:方式, 模式;4224 neat:整洁, 利索;4225 statistics:统计;4251 random:任意的, 随机的;4201 laundry:洗衣房;4545 barrel:桶, 一桶之量;4249 recruit:招募, 新成员;4229 pregnant:怀孕的, 孕育的;4235 relevant:有关的, 相关联的;4252 incentive:刺激, 激励, 鼓励;4194 grave:坟墓, 墓穴;
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提供[单词组2]：;
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"""
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- resp = get_answer_from_gpt(question=question,temperature=0.9,sys_prompt=sys_prompt,model="gpt-4.1")
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+ resp = get_answer_from_gpt(question=question, temperature=0.9, sys_prompt=sys_prompt, model="gpt-4.1")
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print(type(resp))
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print(resp)
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-
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xie