majiahui@haimaqingfan.com
2 years ago
commit
8f279d7fd3
57 changed files with 66233 additions and 0 deletions
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<option name="enabled" value="true" /> |
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<component name="NewModuleRootManager"> |
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<content url="file://$MODULE_DIR$" /> |
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@ -0,0 +1,22 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<serverData> |
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<paths name="chat"> |
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<serverdata> |
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<mappings> |
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<mapping local="$PROJECT_DIR$" web="/" /> |
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</mappings> |
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<paths name="majiahui@104.244.90.248:28385"> |
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<serverdata> |
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<mappings> |
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<mapping deploy="/home/majiahui/ChatGPT_Sever" local="$PROJECT_DIR$" /> |
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</mappings> |
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</serverdata> |
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<option name="myAutoUpload" value="ALWAYS" /> |
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@ -0,0 +1,61 @@ |
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<item index="34" class="java.lang.String" itemvalue="tensorboard" /> |
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<item index="36" class="java.lang.String" itemvalue="olefile" /> |
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<item index="38" class="java.lang.String" itemvalue="mkl-service" /> |
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@ -0,0 +1,6 @@ |
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<component name="InspectionProjectProfileManager"> |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.9.16 (sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python)" project-jdk-type="Python SDK" /> |
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@ -0,0 +1,8 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<modules> |
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<module fileurl="file://$PROJECT_DIR$/.idea/ChatGPT_Sever.iml" filepath="$PROJECT_DIR$/.idea/ChatGPT_Sever.iml" /> |
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@ -0,0 +1,8 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<component name="SshConfigs"> |
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<configs> |
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<sshConfig authType="PASSWORD" host="104.244.90.248" id="88e52b8d-a70e-4743-9a44-06aaefa0048a" port="28385" nameFormat="DESCRIPTIVE" username="majiahui" useOpenSSHConfig="true" /> |
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@ -0,0 +1,6 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<mapping directory="$PROJECT_DIR$" vcs="Git" /> |
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@ -0,0 +1,14 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<project version="4"> |
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<option name="servers"> |
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<webServer id="c2e40908-9bb6-423b-a163-c99a15eb604c" name="chat"> |
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<fileTransfer rootFolder="/home/majiahui/ChatGPT_Sever" accessType="SFTP" host="104.244.90.248" port="28385" sshConfigId="88e52b8d-a70e-4743-9a44-06aaefa0048a" sshConfig="majiahui@104.244.90.248:28385 password"> |
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@ -0,0 +1,585 @@ |
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<?xml version="1.0" encoding="UTF-8"?> |
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<option name="myCustomStartScript" value="import sys sys.path.extend([WORKING_DIR_AND_PYTHON_PATHS]) from flask.cli import ScriptInfo locals().update(ScriptInfo(create_app=None).load_app().make_shell_context()) print("Python %s on %s\nApp: %s [%s]\nInstance: %s" % (sys.version, sys.platform, app.import_name, app.env, app.instance_path))" /> |
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<option name="myEnvs"> |
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<component name="Git.Settings"> |
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<component name="RunManager" selected="Python.ceshisuo"> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<option name="PARENT_ENVS" value="true" /> |
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<option name="SDK_HOME" value="sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python" /> |
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<option name="IS_MODULE_SDK" value="false" /> |
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<option name="ADD_CONTENT_ROOTS" value="true" /> |
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<option name="ADD_SOURCE_ROOTS" value="true" /> |
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" /> |
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/ceshi.py" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<option name="INPUT_FILE" value="" /> |
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<method v="2" /> |
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<configuration name="GPT_service" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="SDK_HOME" value="sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python" /> |
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" /> |
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/GPT_service.py" /> |
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<option name="PARAMETERS" value="" /> |
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<option name="SHOW_COMMAND_LINE" value="false" /> |
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<option name="EMULATE_TERMINAL" value="false" /> |
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<option name="MODULE_MODE" value="false" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<option name="INPUT_FILE" value="" /> |
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<method v="2" /> |
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</configuration> |
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<configuration name="ceshiduojincheng" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<option name="PARAMETERS" value="" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<option name="INPUT_FILE" value="" /> |
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<method v="2" /> |
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<configuration name="ceshiflask" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<option name="PARENT_ENVS" value="true" /> |
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<option name="SDK_HOME" value="sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python" /> |
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" /> |
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<option name="PARAMETERS" value="" /> |
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<option name="MODULE_MODE" value="false" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<option name="INPUT_FILE" value="" /> |
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<method v="2" /> |
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</configuration> |
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<configuration name="ceshisuo" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<option name="PARENT_ENVS" value="true" /> |
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<envs> |
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" /> |
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" /> |
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/ceshisuo.py" /> |
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<option name="PARAMETERS" value="" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<option name="INPUT_FILE" value="" /> |
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<method v="2" /> |
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</configuration> |
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<configuration name="cewshi" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<option name="PARENT_ENVS" value="true" /> |
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<option name="IS_MODULE_SDK" value="true" /> |
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<option name="ADD_SOURCE_ROOTS" value="true" /> |
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" /> |
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/cewshi.py" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<module name="ChatGPT_Sever" /> |
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<envs> |
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<option name="SDK_HOME" value="sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python" /> |
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<option name="ADD_SOURCE_ROOTS" value="true" /> |
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<option name="REDIRECT_INPUT" value="false" /> |
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<method v="2" /> |
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<configuration name="lock_flask" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true"> |
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<module name="ChatGPT_Sever" /> |
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<option name="INTERPRETER_OPTIONS" value="" /> |
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<envs> |
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<option name="SDK_HOME" value="sftp://majiahui@104.244.90.248:28385/home/majiahui/miniconda3/envs/chatgpt/bin/python" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$jar_bagk.coverage" NAME="jar_bagk Coverage Results" MODIFIED="1680256965020" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
|||
<SUITE FILE_PATH="coverage/ChatGPT_Sever$Flask__ceshiflask_py_.coverage" NAME="Flask (ceshiflask.py) Coverage Results" MODIFIED="1680334430007" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" /> |
|||
<SUITE FILE_PATH="coverage/ChatGPT_Sever$flask_serves.coverage" NAME="flask_serves Coverage Results" MODIFIED="1680492218263" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" /> |
|||
<SUITE FILE_PATH="coverage/ChatGPT_Sever$ceshishengcehngpath.coverage" NAME="ceshishengcehngpath Coverage Results" MODIFIED="1680236688849" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$cewshi.coverage" NAME="cewshi Coverage Results" MODIFIED="1680509846740" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$Flask__lock_flask_py_.coverage" NAME="Flask (lock_flask.py) Coverage Results" MODIFIED="1680494176187" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$ceshibianma.coverage" NAME="ceshibianma Coverage Results" MODIFIED="1680255589158" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$ceshi_chat.coverage" NAME="ceshi_chat Coverage Results" MODIFIED="1680439193947" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$ceshiduojincheng.coverage" NAME="ceshiduojincheng Coverage Results" MODIFIED="1680502039974" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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<SUITE FILE_PATH="coverage/ChatGPT_Sever$ceshisuo.coverage" NAME="ceshisuo Coverage Results" MODIFIED="1680511457852" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" /> |
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</component> |
|||
</project> |
|||
@ -0,0 +1,57 @@ |
|||
#!/usr/bin/env python |
|||
# -*- coding: utf-8 -*- |
|||
''' |
|||
@Time : 2023/3/26 18:38 |
|||
@Author : ZhangYuPeng |
|||
@Email : zhangyupeng@bonc.com.cn |
|||
@File : GPT_service.py |
|||
@Software: PyCharm |
|||
|
|||
''' |
|||
import openai |
|||
from flask import Flask |
|||
from flask import request, jsonify |
|||
|
|||
# openai.api_key = "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" |
|||
openai.api_key = "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" |
|||
|
|||
# def chat_drop(): |
|||
# openai.api_key = "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" |
|||
# res = openai.ChatCompletion.create( |
|||
# model="gpt-3.5-turbo", |
|||
# messages=[ |
|||
# {"role": "user", "content": "请帮我改写这句话:在城市发展进程当中，逐渐呈现出一些综合性的大型建筑群。"}, |
|||
# {"role": "assistant", "content": "随着城市的发展，综合性大型建筑群正在逐渐出现。"}, |
|||
# {"role": "user", "content": "这句话我不满意，再改一下帮我"} |
|||
# ], |
|||
# temperature=0.5, |
|||
# top_p=1, |
|||
# ) |
|||
# print(res.choices[0].message.content) |
|||
|
|||
|
|||
|
|||
app = Flask(__name__) |
|||
|
|||
|
|||
@app.route("/chatgpt", methods=['POST', 'GET']) |
|||
def index(): |
|||
print(request.remote_addr, request.remote_user) |
|||
prompt = request.json["prompt"] |
|||
|
|||
response = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": "请帮我改写这句话:在城市发展进程当中，逐渐呈现出一些综合性的大型建筑群。"}, |
|||
{"role": "assistant", "content": "随着城市的发展，综合性大型建筑群正在逐渐出现。"}, |
|||
{"role": "user", "content": prompt} |
|||
], |
|||
) |
|||
message = response.choices[0].message.content |
|||
return message |
|||
|
|||
|
|||
if __name__ == '__main__': |
|||
app.run('0.0.0.0', port=9999, debug=True) |
|||
|
|||
|
|||
@ -0,0 +1,2 @@ |
|||
Files in this folder are to enhance backwards compatibility of anaconda software with older compilers. |
|||
See: https://github.com/conda/conda/issues/6030 for more information. |
|||
Binary file not shown.
@ -0,0 +1,31 @@ |
|||
{ |
|||
"build": "main", |
|||
"build_number": 0, |
|||
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|||
"constrains": [], |
|||
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"extracted_package_dir": "/home/majiahui/miniconda3/pkgs/_libgcc_mutex-0.1-main", |
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"features": "", |
|||
"files": [], |
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|||
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|||
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|||
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|||
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} |
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@ -0,0 +1,46 @@ |
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{ |
|||
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|||
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|||
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|||
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|||
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|||
], |
|||
"depends": [ |
|||
"_libgcc_mutex 0.1 main", |
|||
"libgomp >=7.5.0" |
|||
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|||
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|||
"features": "", |
|||
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|||
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|||
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"license": "BSD-3-Clause", |
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|||
"md5": "71d281e9c2192cb3fa425655a8defb85", |
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"name": "_openmp_mutex", |
|||
"package_tarball_full_path": "/home/majiahui/miniconda3/pkgs/_openmp_mutex-5.1-1_gnu.conda", |
|||
"paths_data": { |
|||
"paths": [ |
|||
{ |
|||
"_path": "lib/libgomp.so.1", |
|||
"path_type": "softlink", |
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|||
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|||
"version": "5.1" |
|||
} |
|||
@ -0,0 +1,50 @@ |
|||
{ |
|||
"build": "h06a4308_0", |
|||
"build_number": 0, |
|||
"channel": "https://repo.anaconda.com/pkgs/main/linux-64", |
|||
"constrains": [], |
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"depends": [], |
|||
"extracted_package_dir": "/home/majiahui/miniconda3/pkgs/ca-certificates-2023.01.10-h06a4308_0", |
|||
"features": "", |
|||
"files": [ |
|||
"ssl/cacert.pem", |
|||
"ssl/cert.pem" |
|||
], |
|||
"fn": "ca-certificates-2023.01.10-h06a4308_0.conda", |
|||
"legacy_bz2_md5": "5cc97953b9c0731a4e4624761f93b255", |
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"license": "MPL-2.0", |
|||
"license_family": "Other", |
|||
"link": { |
|||
"source": "/home/majiahui/miniconda3/pkgs/ca-certificates-2023.01.10-h06a4308_0", |
|||
"type": 1 |
|||
}, |
|||
"md5": "7704989a2ccf6c1f5a50c985509841c4", |
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"name": "ca-certificates", |
|||
"package_tarball_full_path": "/home/majiahui/miniconda3/pkgs/ca-certificates-2023.01.10-h06a4308_0.conda", |
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"paths_data": { |
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"paths": [ |
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{ |
|||
"_path": "ssl/cacert.pem", |
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"path_type": "hardlink", |
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{ |
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"features": "", |
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} |
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@ -0,0 +1,25 @@ |
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==> 2023-03-30 17:29:05 <== |
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# cmd: /home/majiahui/miniconda3/bin/conda create --name chatgpt python=3.9 |
|||
# conda version: 23.1.0 |
|||
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|||
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|||
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|||
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|||
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# update specs: ['python=3.9'] |
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@ -0,0 +1,65 @@ |
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{ |
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|||
"version": "2.38" |
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} |
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@ -0,0 +1,155 @@ |
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{ |
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|||
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|||
"share/man/man3/ffi_call.3", |
|||
"share/man/man3/ffi_prep_cif.3", |
|||
"share/man/man3/ffi_prep_cif_var.3" |
|||
], |
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@ -0,0 +1,237 @@ |
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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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"path_type": "hardlink", |
|||
"sha256": "484772830223a556a296594d0dfa2acc3750a5b4e17ad52cf6ae08f451a173a7", |
|||
"sha256_in_prefix": "484772830223a556a296594d0dfa2acc3750a5b4e17ad52cf6ae08f451a173a7", |
|||
"size_in_bytes": 12183 |
|||
}, |
|||
{ |
|||
"_path": "lib/python3.9/site-packages/wheel/vendored/packaging/_manylinux.py", |
|||
"path_type": "hardlink", |
|||
"sha256": "d4e58a003eb1b604e038654486e8c55aa177e495aa32f40bc59119d9094723d8", |
|||
"sha256_in_prefix": "d4e58a003eb1b604e038654486e8c55aa177e495aa32f40bc59119d9094723d8", |
|||
"size_in_bytes": 11489 |
|||
}, |
|||
{ |
|||
"_path": "lib/python3.9/site-packages/wheel/vendored/packaging/_musllinux.py", |
|||
"path_type": "hardlink", |
|||
"sha256": "93dbd98f8b66c7412517edb2f21ee06f3d3dcc27e112367d6506de66be6c55db", |
|||
"sha256_in_prefix": "93dbd98f8b66c7412517edb2f21ee06f3d3dcc27e112367d6506de66be6c55db", |
|||
"size_in_bytes": 4374 |
|||
}, |
|||
{ |
|||
"_path": "lib/python3.9/site-packages/wheel/vendored/packaging/tags.py", |
|||
"path_type": "hardlink", |
|||
"sha256": "33f0d0238cc69cfab786c455f505987f94a830998107dd60aaa1bd26dcaffa6f", |
|||
"sha256_in_prefix": "33f0d0238cc69cfab786c455f505987f94a830998107dd60aaa1bd26dcaffa6f", |
|||
"size_in_bytes": 15612 |
|||
}, |
|||
{ |
|||
"_path": "lib/python3.9/site-packages/wheel/wheelfile.py", |
|||
"path_type": "hardlink", |
|||
"sha256": "f6259639672f5574b1dba61d7caf50a6d440d4e1de4518ea128ecf11beb7d662", |
|||
"sha256_in_prefix": "f6259639672f5574b1dba61d7caf50a6d440d4e1de4518ea128ecf11beb7d662", |
|||
"size_in_bytes": 7536 |
|||
} |
|||
], |
|||
"paths_version": 1 |
|||
}, |
|||
"requested_spec": "None", |
|||
"sha256": "9fea62266ddb82be6d8d0465d2841a6271444efc514bbf572cf4ae7b5c6beab8", |
|||
"size": 65087, |
|||
"subdir": "linux-64", |
|||
"timestamp": 1676621870595, |
|||
"track_features": "", |
|||
"url": "https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.38.4-py39h06a4308_0.conda", |
|||
"version": "0.38.4" |
|||
} |
|||
File diff suppressed because it is too large
@ -0,0 +1,93 @@ |
|||
{ |
|||
"build": "h5eee18b_0", |
|||
"build_number": 0, |
|||
"channel": "https://repo.anaconda.com/pkgs/main/linux-64", |
|||
"constrains": [], |
|||
"depends": [ |
|||
"libgcc-ng >=11.2.0" |
|||
], |
|||
"extracted_package_dir": "/home/majiahui/miniconda3/pkgs/zlib-1.2.13-h5eee18b_0", |
|||
"features": "", |
|||
"files": [ |
|||
"include/zconf.h", |
|||
"include/zlib.h", |
|||
"lib/libz.a", |
|||
"lib/libz.so", |
|||
"lib/libz.so.1", |
|||
"lib/libz.so.1.2.13", |
|||
"lib/pkgconfig/zlib.pc" |
|||
], |
|||
"fn": "zlib-1.2.13-h5eee18b_0.conda", |
|||
"legacy_bz2_md5": "4f315d7551f4bbeb06b6fc28342a9aa5", |
|||
"license": "Zlib", |
|||
"license_family": "Other", |
|||
"link": { |
|||
"source": "/home/majiahui/miniconda3/pkgs/zlib-1.2.13-h5eee18b_0", |
|||
"type": 1 |
|||
}, |
|||
"md5": "333e31fbfbb5057c92fa845ad6adef93", |
|||
"name": "zlib", |
|||
"package_tarball_full_path": "/home/majiahui/miniconda3/pkgs/zlib-1.2.13-h5eee18b_0.conda", |
|||
"paths_data": { |
|||
"paths": [ |
|||
{ |
|||
"_path": "include/zconf.h", |
|||
"path_type": "hardlink", |
|||
"sha256": "e5a9079e37fa799583634fcd6905b53f1c20fd1d98e6bae942674c3c419b9fab", |
|||
"sha256_in_prefix": "e5a9079e37fa799583634fcd6905b53f1c20fd1d98e6bae942674c3c419b9fab", |
|||
"size_in_bytes": 16671 |
|||
}, |
|||
{ |
|||
"_path": "include/zlib.h", |
|||
"path_type": "hardlink", |
|||
"sha256": "a980a0d104198a53cc220c51ab5856e5be901bec8a2d02e0ee79a8754219dfed", |
|||
"sha256_in_prefix": "a980a0d104198a53cc220c51ab5856e5be901bec8a2d02e0ee79a8754219dfed", |
|||
"size_in_bytes": 97323 |
|||
}, |
|||
{ |
|||
"_path": "lib/libz.a", |
|||
"path_type": "hardlink", |
|||
"sha256": "adc88690bcc3b1bfde01a3375879a78677c72e5cc331566627f47343313881a9", |
|||
"sha256_in_prefix": "adc88690bcc3b1bfde01a3375879a78677c72e5cc331566627f47343313881a9", |
|||
"size_in_bytes": 165662 |
|||
}, |
|||
{ |
|||
"_path": "lib/libz.so", |
|||
"path_type": "softlink", |
|||
"sha256": "0b0e682a9dc7fd4895a6783288f851b793dc89633f28714027974fa4d66f3914", |
|||
"size_in_bytes": 124744 |
|||
}, |
|||
{ |
|||
"_path": "lib/libz.so.1", |
|||
"path_type": "softlink", |
|||
"sha256": "0b0e682a9dc7fd4895a6783288f851b793dc89633f28714027974fa4d66f3914", |
|||
"size_in_bytes": 124744 |
|||
}, |
|||
{ |
|||
"_path": "lib/libz.so.1.2.13", |
|||
"path_type": "hardlink", |
|||
"sha256": "0b0e682a9dc7fd4895a6783288f851b793dc89633f28714027974fa4d66f3914", |
|||
"sha256_in_prefix": "0b0e682a9dc7fd4895a6783288f851b793dc89633f28714027974fa4d66f3914", |
|||
"size_in_bytes": 124744 |
|||
}, |
|||
{ |
|||
"_path": "lib/pkgconfig/zlib.pc", |
|||
"file_mode": "text", |
|||
"path_type": "hardlink", |
|||
"prefix_placeholder": "/croot/zlib_1666594980945/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl", |
|||
"sha256": "11202b5c550c2d9b391a82e4c02e1f4a5647684b657d35d0422a179a668f1cce", |
|||
"sha256_in_prefix": "dbbce8c765504cdaa574c5f9a5877b636bfdc234da6bdbdce9124e0f483ae0bf", |
|||
"size_in_bytes": 1487 |
|||
} |
|||
], |
|||
"paths_version": 1 |
|||
}, |
|||
"requested_spec": "None", |
|||
"sha256": "4b69b6752e384459b1fc47dfc078598d5b687b3ccffe21591676b640481fb794", |
|||
"size": 105851, |
|||
"subdir": "linux-64", |
|||
"timestamp": 1666595012798, |
|||
"track_features": "", |
|||
"url": "https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda", |
|||
"version": "1.2.13" |
|||
} |
|||
@ -0,0 +1,200 @@ |
|||
#!/croot/openssl_1676330466875/_build_env/bin/perl |
|||
# Copyright 2002-2018 The OpenSSL Project Authors. All Rights Reserved. |
|||
# Copyright (c) 2002 The OpenTSA Project. All rights reserved. |
|||
# |
|||
# Licensed under the OpenSSL license (the "License"). You may not use |
|||
# this file except in compliance with the License. You can obtain a copy |
|||
# in the file LICENSE in the source distribution or at |
|||
# https://www.openssl.org/source/license.html |
|||
|
|||
use strict; |
|||
use IO::Handle; |
|||
use Getopt::Std; |
|||
use File::Basename; |
|||
use WWW::Curl::Easy; |
|||
|
|||
use vars qw(%options); |
|||
|
|||
# Callback for reading the body. |
|||
sub read_body { |
|||
my ($maxlength, $state) = @_; |
|||
my $return_data = ""; |
|||
my $data_len = length ${$state->{data}}; |
|||
if ($state->{bytes} < $data_len) { |
|||
$data_len = $data_len - $state->{bytes}; |
|||
$data_len = $maxlength if $data_len > $maxlength; |
|||
$return_data = substr ${$state->{data}}, $state->{bytes}, $data_len; |
|||
$state->{bytes} += $data_len; |
|||
} |
|||
return $return_data; |
|||
} |
|||
|
|||
# Callback for writing the body into a variable. |
|||
sub write_body { |
|||
my ($data, $pointer) = @_; |
|||
${$pointer} .= $data; |
|||
return length($data); |
|||
} |
|||
|
|||
# Initialise a new Curl object. |
|||
sub create_curl { |
|||
my $url = shift; |
|||
|
|||
# Create Curl object. |
|||
my $curl = WWW::Curl::Easy::new(); |
|||
|
|||
# Error-handling related options. |
|||
$curl->setopt(CURLOPT_VERBOSE, 1) if $options{d}; |
|||
$curl->setopt(CURLOPT_FAILONERROR, 1); |
|||
$curl->setopt(CURLOPT_USERAGENT, |
|||
"OpenTSA tsget.pl/openssl-1.1.1t"); |
|||
|
|||
# Options for POST method. |
|||
$curl->setopt(CURLOPT_UPLOAD, 1); |
|||
$curl->setopt(CURLOPT_CUSTOMREQUEST, "POST"); |
|||
$curl->setopt(CURLOPT_HTTPHEADER, |
|||
["Content-Type: application/timestamp-query", |
|||
"Accept: application/timestamp-reply,application/timestamp-response"]); |
|||
$curl->setopt(CURLOPT_READFUNCTION, \&read_body); |
|||
$curl->setopt(CURLOPT_HEADERFUNCTION, sub { return length($_[0]); }); |
|||
|
|||
# Options for getting the result. |
|||
$curl->setopt(CURLOPT_WRITEFUNCTION, \&write_body); |
|||
|
|||
# SSL related options. |
|||
$curl->setopt(CURLOPT_SSLKEYTYPE, "PEM"); |
|||
$curl->setopt(CURLOPT_SSL_VERIFYPEER, 1); # Verify server's certificate. |
|||
$curl->setopt(CURLOPT_SSL_VERIFYHOST, 2); # Check server's CN. |
|||
$curl->setopt(CURLOPT_SSLKEY, $options{k}) if defined($options{k}); |
|||
$curl->setopt(CURLOPT_SSLKEYPASSWD, $options{p}) if defined($options{p}); |
|||
$curl->setopt(CURLOPT_SSLCERT, $options{c}) if defined($options{c}); |
|||
$curl->setopt(CURLOPT_CAINFO, $options{C}) if defined($options{C}); |
|||
$curl->setopt(CURLOPT_CAPATH, $options{P}) if defined($options{P}); |
|||
$curl->setopt(CURLOPT_RANDOM_FILE, $options{r}) if defined($options{r}); |
|||
$curl->setopt(CURLOPT_EGDSOCKET, $options{g}) if defined($options{g}); |
|||
|
|||
# Setting destination. |
|||
$curl->setopt(CURLOPT_URL, $url); |
|||
|
|||
return $curl; |
|||
} |
|||
|
|||
# Send a request and returns the body back. |
|||
sub get_timestamp { |
|||
my $curl = shift; |
|||
my $body = shift; |
|||
my $ts_body; |
|||
local $::error_buf; |
|||
|
|||
# Error-handling related options. |
|||
$curl->setopt(CURLOPT_ERRORBUFFER, "::error_buf"); |
|||
|
|||
# Options for POST method. |
|||
$curl->setopt(CURLOPT_INFILE, {data => $body, bytes => 0}); |
|||
$curl->setopt(CURLOPT_INFILESIZE, length(${$body})); |
|||
|
|||
# Options for getting the result. |
|||
$curl->setopt(CURLOPT_FILE, \$ts_body); |
|||
|
|||
# Send the request... |
|||
my $error_code = $curl->perform(); |
|||
my $error_string; |
|||
if ($error_code != 0) { |
|||
my $http_code = $curl->getinfo(CURLINFO_HTTP_CODE); |
|||
$error_string = "could not get timestamp"; |
|||
$error_string .= ", http code: $http_code" unless $http_code == 0; |
|||
$error_string .= ", curl code: $error_code"; |
|||
$error_string .= " ($::error_buf)" if defined($::error_buf); |
|||
} else { |
|||
my $ct = $curl->getinfo(CURLINFO_CONTENT_TYPE); |
|||
if (lc($ct) ne "application/timestamp-reply" |
|||
&& lc($ct) ne "application/timestamp-response") { |
|||
$error_string = "unexpected content type returned: $ct"; |
|||
} |
|||
} |
|||
return ($ts_body, $error_string); |
|||
|
|||
} |
|||
|
|||
# Print usage information and exists. |
|||
sub usage { |
|||
|
|||
print STDERR "usage: $0 -h <server_url> [-e <extension>] [-o <output>] "; |
|||
print STDERR "[-v] [-d] [-k <private_key.pem>] [-p <key_password>] "; |
|||
print STDERR "[-c <client_cert.pem>] [-C <CA_certs.pem>] [-P <CA_path>] "; |
|||
print STDERR "[-r <file:file...>] [-g <EGD_socket>] [<request>]...\n"; |
|||
exit 1; |
|||
} |
|||
|
|||
# ---------------------------------------------------------------------- |
|||
# Main program |
|||
# ---------------------------------------------------------------------- |
|||
|
|||
# Getting command-line options (default comes from TSGET environment variable). |
|||
my $getopt_arg = "h:e:o:vdk:p:c:C:P:r:g:"; |
|||
if (exists $ENV{TSGET}) { |
|||
my @old_argv = @ARGV; |
|||
@ARGV = split /\s+/, $ENV{TSGET}; |
|||
getopts($getopt_arg, \%options) or usage; |
|||
@ARGV = @old_argv; |
|||
} |
|||
getopts($getopt_arg, \%options) or usage; |
|||
|
|||
# Checking argument consistency. |
|||
if (!exists($options{h}) || (@ARGV == 0 && !exists($options{o})) |
|||
|| (@ARGV > 1 && exists($options{o}))) { |
|||
print STDERR "Inconsistent command line options.\n"; |
|||
usage; |
|||
} |
|||
# Setting defaults. |
|||
@ARGV = ("-") unless @ARGV != 0; |
|||
$options{e} = ".tsr" unless defined($options{e}); |
|||
|
|||
# Processing requests. |
|||
my $curl = create_curl $options{h}; |
|||
undef $/; # For reading whole files. |
|||
REQUEST: foreach (@ARGV) { |
|||
my $input = $_; |
|||
my ($base, $path) = fileparse($input, '\.[^.]*'); |
|||
my $output_base = $base . $options{e}; |
|||
my $output = defined($options{o}) ? $options{o} : $path . $output_base; |
|||
|
|||
STDERR->printflush("$input: ") if $options{v}; |
|||
# Read request. |
|||
my $body; |
|||
if ($input eq "-") { |
|||
# Read the request from STDIN; |
|||
$body = <STDIN>; |
|||
} else { |
|||
# Read the request from file. |
|||
open INPUT, "<" . $input |
|||
or warn("$input: could not open input file: $!\n"), next REQUEST; |
|||
$body = <INPUT>; |
|||
close INPUT |
|||
or warn("$input: could not close input file: $!\n"), next REQUEST; |
|||
} |
|||
|
|||
# Send request. |
|||
STDERR->printflush("sending request") if $options{v}; |
|||
|
|||
my ($ts_body, $error) = get_timestamp $curl, \$body; |
|||
if (defined($error)) { |
|||
die "$input: fatal error: $error\n"; |
|||
} |
|||
STDERR->printflush(", reply received") if $options{v}; |
|||
|
|||
# Write response. |
|||
if ($output eq "-") { |
|||
# Write to STDOUT. |
|||
print $ts_body; |
|||
} else { |
|||
# Write to file. |
|||
open OUTPUT, ">", $output |
|||
or warn("$output: could not open output file: $!\n"), next REQUEST; |
|||
print OUTPUT $ts_body; |
|||
close OUTPUT |
|||
or warn("$output: could not close output file: $!\n"), next REQUEST; |
|||
} |
|||
STDERR->printflush(", $output written.\n") if $options{v}; |
|||
} |
|||
$curl->cleanup(); |
|||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,40 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/30 14:16 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
from flask import send_file, send_from_directory |
|||
from flask import Flask, jsonify |
|||
import os |
|||
from flask import request |
|||
from flask import make_response |
|||
|
|||
app = Flask(__name__) |
|||
app.config["JSON_AS_ASCII"] = False |
|||
|
|||
|
|||
# @app.route('/download', methods=["GET", "POST"]) |
|||
# def download(): |
|||
# path = './new_data_txt/大型商业建筑人员疏散设计研究(1).docx' |
|||
# return send_file(path, |
|||
# as_attachment=True, |
|||
# download_name="大型商业建筑人员疏散设计研究(1).docx") |
|||
# |
|||
# # rv.headers['Content-Disposition'] += "; filename*=utf-8''{}".format(filename) |
|||
|
|||
|
|||
@app.route("/download/<filename>", methods=['GET']) |
|||
def download_file(filename): |
|||
# 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 = './new_data_txt/' # 假设在当前目录 |
|||
|
|||
response = make_response(send_from_directory(directory, filename, as_attachment=True)) |
|||
response.headers["Content-Disposition"] = "attachment; filename={}".format(filename.encode().decode('latin-1')) |
|||
return response |
|||
|
|||
if __name__ == "__main__": |
|||
app.run(host="0.0.0.0", port=14009, threaded=True, debug=False) |
|||
@ -0,0 +1,385 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/29 14:27 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
|
|||
import os |
|||
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 |
|||
|
|||
|
|||
pool = redis.ConnectionPool(host='localhost', port=6379, max_connections=50, db=1) |
|||
redis_ = redis.Redis(connection_pool=pool, decode_responses=True) |
|||
|
|||
db_key_query = 'query' |
|||
db_key_querying = 'querying' |
|||
batch_size = 32 |
|||
|
|||
app = Flask(__name__) |
|||
app.config["JSON_AS_ASCII"] = False |
|||
|
|||
import logging |
|||
lock = threading.RLock() |
|||
|
|||
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1000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1000字" |
|||
references_prompt = "论文题目是“{}”，目录是“{}”，请为这篇论文生成中文的{}" |
|||
thank_prompt = "论文题目是“{}”，目录是“{}”，请把其中的{}部分续写完整" |
|||
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1500字" |
|||
|
|||
dabiaoti = ["二","三","四","五","六","七","八","九"] |
|||
|
|||
# 正则 |
|||
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]+' |
|||
|
|||
project_data_txt_path = "/home/majiahui/ChatGPT_Sever/new_data_txt" |
|||
|
|||
api_key_list = ["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", |
|||
"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", |
|||
"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", |
|||
"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"] |
|||
|
|||
def chat_title(title, api_key): |
|||
global lock |
|||
# time.sleep(5) |
|||
# return [str(i) for i in range(20)] |
|||
openai.api_key = api_key |
|||
res = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": mulu_prompt.format(title)}, |
|||
], |
|||
temperature=0.5 |
|||
) |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
mulu = res.choices[0].message.content |
|||
mulu_list = str(mulu).split("\n") |
|||
mulu_list = [i.strip() for i in mulu_list if i != ""] |
|||
return mulu, mulu_list |
|||
|
|||
|
|||
def chat_kaitibaogao(title, api_key, uuid_path): |
|||
global lock |
|||
openai.api_key = api_key |
|||
res = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": kaitibaogao_prompt.format(title)}, |
|||
], |
|||
temperature=0.5 |
|||
) |
|||
kaitibaogao = res.choices[0].message.content |
|||
kaitibaogao_path = os.path.join(uuid_path, "kaitibaogao.txt") |
|||
with open(kaitibaogao_path, 'w', encoding='utf8') as f_kaitibaogao: |
|||
f_kaitibaogao.write(kaitibaogao) |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
|
|||
|
|||
class GeneratePaper: |
|||
def __init__(self, mulu, table): |
|||
self.mulu = mulu |
|||
self.paper = [""] * len(table) |
|||
|
|||
def chat_content_(self,api_key, mulu_title_id, title, mulu, subtitle, prompt): |
|||
global lock |
|||
# time.sleep(5) |
|||
# api_key_list.append(api_key) |
|||
# self.paper[mulu_title_id] = subtitle |
|||
if subtitle[:2] == "@@": |
|||
self.paper[mulu_title_id] = 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 |
|||
) |
|||
self.paper[mulu_title_id] = res.choices[0].message.content |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
# return res.choices[0].message.content |
|||
|
|||
|
|||
def classify(): # 调用模型，设置最大batch_size |
|||
while True: |
|||
if redis_.llen(db_key_query) == 0: # 若队列中没有元素就继续获取 |
|||
time.sleep(3) |
|||
continue |
|||
thread_list = [] |
|||
query = redis_.lpop(db_key_query).decode('UTF-8') # 获取query的text |
|||
data_dict_path = json.loads(query) |
|||
query_id = data_dict_path['id'] |
|||
title = data_dict_path['title'] |
|||
# project_data_txt_path = os.path.abspath("new_data_txt") |
|||
# uuid_path = "new_data_txt/{}/".format(query_id) |
|||
|
|||
# uuid路径 |
|||
uuid_path = os.path.join(project_data_txt_path, query_id) |
|||
print("uuid",query_id) |
|||
os.makedirs(uuid_path) |
|||
print("uuid_path", os.path.exists(uuid_path)) |
|||
|
|||
# 生成开题报告 |
|||
# title, api_key, uuid_path |
|||
api_key = api_key_list.pop() |
|||
t = Thread(target=chat_kaitibaogao, args=(title, |
|||
api_key, |
|||
uuid_path, |
|||
)) |
|||
t.start() |
|||
thread_list.append(t) |
|||
|
|||
# 生成目录 |
|||
while True: |
|||
if api_key_list != []: |
|||
api_key = api_key_list.pop() |
|||
break |
|||
else: |
|||
time.sleep(3) |
|||
|
|||
|
|||
mulu, mulu_list = chat_title(title, api_key) |
|||
|
|||
|
|||
# mulu_base64 = base64.b64encode(mulu.encode('utf-8')) |
|||
# mulu_path = os.path.join(uuid_path, "mulu.txt") |
|||
# with open(mulu_path, 'wb', encoding='utf8') as f2: |
|||
# f2.write(mulu_base64) |
|||
|
|||
|
|||
index = 0 |
|||
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) |
|||
chat_class = GeneratePaper(mulu_list, table_of_contents) |
|||
print(len(table_of_contents)) |
|||
############################################################ |
|||
while True: |
|||
if api_key_list == []: |
|||
continue |
|||
if index == len(table_of_contents): |
|||
break |
|||
api_key = api_key_list.pop() |
|||
subtitle = table_of_contents[index] |
|||
if index == 0: |
|||
prompt = first_title_prompt |
|||
elif subtitle == "参考文献": |
|||
prompt = references_prompt |
|||
elif subtitle == "致谢": |
|||
prompt = thank_prompt |
|||
else: |
|||
prompt = first_title_prompt |
|||
print("请求的所有参数", api_key, |
|||
index, |
|||
title, |
|||
mulu_list, |
|||
subtitle, |
|||
prompt) |
|||
|
|||
t = Thread(target=chat_class.chat_content_, args=(api_key, |
|||
index, |
|||
title, |
|||
mulu_list, |
|||
subtitle, |
|||
prompt)) |
|||
t.start() |
|||
thread_list.append(t) |
|||
lock.acquire() |
|||
index += 1 |
|||
lock.release() |
|||
|
|||
for thread in thread_list: |
|||
thread.join() |
|||
|
|||
|
|||
print(chat_class.paper) |
|||
paper = "\n".join(chat_class.paper) |
|||
print(paper) |
|||
|
|||
content_path = os.path.join(uuid_path, "content.txt") |
|||
with open(content_path, 'w', encoding='utf8') as f_content: |
|||
f_content.write(paper) |
|||
|
|||
mulu_path = os.path.join(uuid_path, "mulu.txt") |
|||
with open(mulu_path, 'w', encoding='utf8') as f_mulu: |
|||
f_mulu.write(mulu) |
|||
|
|||
kaitibaogao_txt_path = os.path.join(uuid_path, "kaitibaogao.txt") |
|||
|
|||
# word保存路径 |
|||
|
|||
save_word_paper = os.path.join(uuid_path, "paper.docx") |
|||
save_word_paper_start = os.path.join(uuid_path, "paper_start.docx".format(title)) |
|||
|
|||
# content_base64 = base64.b64encode(paper.encode('utf-8')) |
|||
# content_path = os.path.join(uuid_path, "content.txt") |
|||
# with open(content_path, 'wb', encoding='utf8') as f2: |
|||
# f2.write(content_base64) |
|||
|
|||
# 拼接成word |
|||
title = pa.quote(title) |
|||
mulu_path = mulu_path |
|||
content_path = content_path |
|||
|
|||
# 调用jar包 |
|||
print("java_path", mulu_path, content_path, title, save_word_paper) |
|||
os.system( |
|||
"java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoWord.jar' '{}' '{}' '{}' '{}'".format( |
|||
mulu_path, content_path, title, save_word_paper)) |
|||
|
|||
print("jaba_kaitibaogao", kaitibaogao_txt_path, save_word_paper_start) |
|||
os.system("java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoKaitiWord.jar' '{}' '{}'".format( |
|||
kaitibaogao_txt_path, save_word_paper_start)) |
|||
|
|||
url_path_paper = "http://104.244.90.248:14000/download?filename_path={}/paper.docx".format(query_id) |
|||
url_path_kaiti = "http://104.244.90.248:14000/download?filename_path={}/paper_start.docx".format(query_id) |
|||
# content_path = os.path.join(uuid_path, "content.txt") |
|||
# load_result_path = res_path.format(query_id) |
|||
# load_result_path = os.path.abspath(load_result_path) |
|||
# with open(load_result_path, 'w', encoding='utf8') as f2: |
|||
# f2.write(paper) |
|||
|
|||
return_text = str({"id":query_id, |
|||
"content_url_path": url_path_paper, |
|||
"content_report_url_path": url_path_kaiti, |
|||
"probabilities": None, |
|||
"status_code": 200}) |
|||
redis_.srem(db_key_querying, query_id) |
|||
redis_.set(query_id, return_text, 28800) |
|||
|
|||
|
|||
@app.route("/chat", methods=["POST"]) |
|||
def chat(): |
|||
print(request.remote_addr) |
|||
title = request.json["title"] |
|||
id_ = str(uuid.uuid1()) |
|||
|
|||
redis_.rpush(db_key_query, json.dumps({"id":id_, "title": title})) # 加入redis |
|||
return_text = {"texts": {'id': id_,}, "probabilities": None, "status_code": 200} |
|||
print("ok") |
|||
redis_.sadd(db_key_querying, 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("querying")) |
|||
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(db_key_query, 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 |
|||
|
|||
t = Thread(target=classify) |
|||
t.start() |
|||
|
|||
if __name__ == "__main__": |
|||
app.run(host="0.0.0.0", port=14000, threaded=True, debug=False) |
|||
@ -0,0 +1,28 @@ |
|||
import os |
|||
|
|||
|
|||
import os |
|||
import urllib.parse as pa |
|||
|
|||
os.system('df -h') |
|||
|
|||
mulu = "/home/majiahui/ChatGPT_Sever/new_data_txt/c799722c-cfaf-11ed-8ad4-aaaa001aad2e/mulu.txt" |
|||
content = "/home/majiahui/ChatGPT_Sever/new_data_txt/c799722c-cfaf-11ed-8ad4-aaaa001aad2e/content.txt" |
|||
kaitibaogao = "/home/majiahui/ChatGPT_Sever/new_data_txt/c799722c-cfaf-11ed-8ad4-aaaa001aad2e/kaitibaogao.txt" |
|||
title = pa.quote("大型商业建筑人员疏散设计研究") |
|||
save_word = "/home/majiahui/ChatGPT_Sever/new_data_txt/c799722c-cfaf-11ed-8ad4-aaaa001aad2e/paper.docx" |
|||
save_word_kaiti = "/home/majiahui/ChatGPT_Sever/new_data_txt/c799722c-cfaf-11ed-8ad4-aaaa001aad2e/paper_kaiti.docx" |
|||
|
|||
print("java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoWord.jar' '{}' '{}' '{}' '{}'".format(mulu, |
|||
content, |
|||
title, |
|||
save_word)) |
|||
os.system("java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoWord.jar' '{}' '{}' '{}' '{}'".format(mulu, content, title, save_word)) |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
os.system("java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoKaitiWord.jar' '{}' '{}'".format(kaitibaogao, |
|||
save_word_kaiti)) |
|||
@ -0,0 +1,385 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/29 14:27 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
|
|||
import os |
|||
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 |
|||
|
|||
|
|||
pool = redis.ConnectionPool(host='localhost', port=6379, max_connections=50, db=2) |
|||
redis_ = redis.Redis(connection_pool=pool, decode_responses=True) |
|||
|
|||
db_key_query = 'query' |
|||
db_key_querying = 'querying' |
|||
batch_size = 32 |
|||
|
|||
app = Flask(__name__) |
|||
app.config["JSON_AS_ASCII"] = False |
|||
|
|||
import logging |
|||
lock = threading.RLock() |
|||
|
|||
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1000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1000字" |
|||
references_prompt = "论文题目是“{}”，目录是“{}”，请为这篇论文生成中文的{}" |
|||
thank_prompt = "论文题目是“{}”，目录是“{}”，请把其中的{}部分续写完整" |
|||
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1500字" |
|||
|
|||
dabiaoti = ["二","三","四","五","六","七","八","九"] |
|||
|
|||
# 正则 |
|||
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]+' |
|||
|
|||
project_data_txt_path = "/home/majiahui/ChatGPT_Sever/new_data_txt" |
|||
|
|||
api_key_list = ["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", |
|||
"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", |
|||
"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", |
|||
"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"] |
|||
|
|||
def chat_title(title, api_key): |
|||
global lock |
|||
# time.sleep(5) |
|||
# return [str(i) for i in range(20)] |
|||
openai.api_key = api_key |
|||
res = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": mulu_prompt.format(title)}, |
|||
], |
|||
temperature=0.5 |
|||
) |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
mulu = res.choices[0].message.content |
|||
mulu_list = str(mulu).split("\n") |
|||
mulu_list = [i.strip() for i in mulu_list if i != ""] |
|||
return mulu, mulu_list |
|||
|
|||
|
|||
def chat_kaitibaogao(title, api_key, uuid_path): |
|||
global lock |
|||
openai.api_key = api_key |
|||
res = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": kaitibaogao_prompt.format(title)}, |
|||
], |
|||
temperature=0.5 |
|||
) |
|||
kaitibaogao = res.choices[0].message.content |
|||
kaitibaogao_path = os.path.join(uuid_path, "kaitibaogao.txt") |
|||
with open(kaitibaogao_path, 'w', encoding='utf8') as f_kaitibaogao: |
|||
f_kaitibaogao.write(kaitibaogao) |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
|
|||
|
|||
class GeneratePaper: |
|||
def __init__(self, mulu, table): |
|||
self.mulu = mulu |
|||
self.paper = [""] * len(table) |
|||
|
|||
def chat_content_(self,api_key, mulu_title_id, title, mulu, subtitle, prompt): |
|||
global lock |
|||
# time.sleep(5) |
|||
# api_key_list.append(api_key) |
|||
# self.paper[mulu_title_id] = subtitle |
|||
if subtitle[:2] == "@@": |
|||
self.paper[mulu_title_id] = 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 |
|||
) |
|||
self.paper[mulu_title_id] = res.choices[0].message.content |
|||
lock.acquire() |
|||
api_key_list.append(api_key) |
|||
lock.release() |
|||
# return res.choices[0].message.content |
|||
|
|||
|
|||
def classify(): # 调用模型，设置最大batch_size |
|||
while True: |
|||
if redis_.llen(db_key_query) == 0: # 若队列中没有元素就继续获取 |
|||
time.sleep(3) |
|||
continue |
|||
thread_list = [] |
|||
query = redis_.lpop(db_key_query).decode('UTF-8') # 获取query的text |
|||
data_dict_path = json.loads(query) |
|||
query_id = data_dict_path['id'] |
|||
title = data_dict_path['title'] |
|||
# project_data_txt_path = os.path.abspath("new_data_txt") |
|||
# uuid_path = "new_data_txt/{}/".format(query_id) |
|||
|
|||
# uuid路径 |
|||
uuid_path = os.path.join(project_data_txt_path, query_id) |
|||
print("uuid",query_id) |
|||
os.makedirs(uuid_path) |
|||
print("uuid_path", os.path.exists(uuid_path)) |
|||
|
|||
# 生成开题报告 |
|||
# title, api_key, uuid_path |
|||
api_key = api_key_list.pop() |
|||
t = Thread(target=chat_kaitibaogao, args=(title, |
|||
api_key, |
|||
uuid_path, |
|||
)) |
|||
t.start() |
|||
thread_list.append(t) |
|||
|
|||
# 生成目录 |
|||
while True: |
|||
if api_key_list != []: |
|||
api_key = api_key_list.pop() |
|||
break |
|||
else: |
|||
time.sleep(3) |
|||
|
|||
|
|||
mulu, mulu_list = chat_title(title, api_key) |
|||
|
|||
|
|||
# mulu_base64 = base64.b64encode(mulu.encode('utf-8')) |
|||
# mulu_path = os.path.join(uuid_path, "mulu.txt") |
|||
# with open(mulu_path, 'wb', encoding='utf8') as f2: |
|||
# f2.write(mulu_base64) |
|||
|
|||
|
|||
index = 0 |
|||
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) |
|||
chat_class = GeneratePaper(mulu_list, table_of_contents) |
|||
print(len(table_of_contents)) |
|||
############################################################ |
|||
while True: |
|||
if api_key_list == []: |
|||
continue |
|||
if index == len(table_of_contents): |
|||
break |
|||
api_key = api_key_list.pop() |
|||
subtitle = table_of_contents[index] |
|||
if index == 0: |
|||
prompt = first_title_prompt |
|||
elif subtitle == "参考文献": |
|||
prompt = references_prompt |
|||
elif subtitle == "致谢": |
|||
prompt = thank_prompt |
|||
else: |
|||
prompt = first_title_prompt |
|||
print("请求的所有参数", api_key, |
|||
index, |
|||
title, |
|||
mulu_list, |
|||
subtitle, |
|||
prompt) |
|||
|
|||
t = Thread(target=chat_class.chat_content_, args=(api_key, |
|||
index, |
|||
title, |
|||
mulu_list, |
|||
subtitle, |
|||
prompt)) |
|||
t.start() |
|||
thread_list.append(t) |
|||
lock.acquire() |
|||
index += 1 |
|||
lock.release() |
|||
|
|||
for thread in thread_list: |
|||
thread.join() |
|||
|
|||
|
|||
print(chat_class.paper) |
|||
paper = "\n".join(chat_class.paper) |
|||
print(paper) |
|||
|
|||
content_path = os.path.join(uuid_path, "content.txt") |
|||
with open(content_path, 'w', encoding='utf8') as f_content: |
|||
f_content.write(paper) |
|||
|
|||
mulu_path = os.path.join(uuid_path, "mulu.txt") |
|||
with open(mulu_path, 'w', encoding='utf8') as f_mulu: |
|||
f_mulu.write(mulu) |
|||
|
|||
kaitibaogao_txt_path = os.path.join(uuid_path, "kaitibaogao.txt") |
|||
|
|||
# word保存路径 |
|||
|
|||
save_word_paper = os.path.join(uuid_path, "paper.docx") |
|||
save_word_paper_start = os.path.join(uuid_path, "paper_start.docx".format(title)) |
|||
|
|||
# content_base64 = base64.b64encode(paper.encode('utf-8')) |
|||
# content_path = os.path.join(uuid_path, "content.txt") |
|||
# with open(content_path, 'wb', encoding='utf8') as f2: |
|||
# f2.write(content_base64) |
|||
|
|||
# 拼接成word |
|||
title = pa.quote(title) |
|||
mulu_path = mulu_path |
|||
content_path = content_path |
|||
|
|||
# 调用jar包 |
|||
print("java_path", mulu_path, content_path, title, save_word_paper) |
|||
os.system( |
|||
"java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoWord.jar' '{}' '{}' '{}' '{}'".format( |
|||
mulu_path, content_path, title, save_word_paper)) |
|||
|
|||
print("jaba_kaitibaogao", kaitibaogao_txt_path, save_word_paper_start) |
|||
os.system("java -Dfile.encoding=UTF-8 -jar '/home/majiahui/ChatGPT_Sever/createAiXieZuoKaitiWord.jar' '{}' '{}'".format( |
|||
kaitibaogao_txt_path, save_word_paper_start)) |
|||
|
|||
url_path_paper = "http://104.244.90.248:14000/download?filename_path={}/paper.docx".format(query_id) |
|||
url_path_kaiti = "http://104.244.90.248:14000/download?filename_path={}/paper_start.docx".format(query_id) |
|||
# content_path = os.path.join(uuid_path, "content.txt") |
|||
# load_result_path = res_path.format(query_id) |
|||
# load_result_path = os.path.abspath(load_result_path) |
|||
# with open(load_result_path, 'w', encoding='utf8') as f2: |
|||
# f2.write(paper) |
|||
|
|||
return_text = str({"id":query_id, |
|||
"content_url_path": url_path_paper, |
|||
"content_report_url_path": url_path_kaiti, |
|||
"probabilities": None, |
|||
"status_code": 200}) |
|||
redis_.srem(db_key_querying, query_id) |
|||
redis_.set(query_id, return_text, 28800) |
|||
|
|||
|
|||
@app.route("/chat", methods=["POST"]) |
|||
def chat(): |
|||
print(request.remote_addr) |
|||
title = request.json["title"] |
|||
id_ = str(uuid.uuid1()) |
|||
|
|||
redis_.rpush(db_key_query, json.dumps({"id":id_, "title": title})) # 加入redis |
|||
return_text = {"texts": {'id': id_,}, "probabilities": None, "status_code": 200} |
|||
print("ok") |
|||
redis_.sadd(db_key_querying, 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("querying")) |
|||
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(db_key_query, 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 |
|||
|
|||
t1 = Thread(target=classify) |
|||
t1.start() |
|||
|
|||
if __name__ == "__main__": |
|||
app.run(host="0.0.0.0", port=14002, threaded=True, debug=False) |
|||
@ -0,0 +1,71 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/30 9:11 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
|
|||
import flask |
|||
import redis |
|||
import uuid |
|||
import json |
|||
from threading import Thread |
|||
import time |
|||
|
|||
app = flask.Flask(__name__) |
|||
pool = redis.ConnectionPool(host='localhost', port=6379, max_connections=50, db=1) |
|||
redis_ = redis.Redis(connection_pool=pool, decode_responses=True) |
|||
|
|||
db_key_query = 'query' |
|||
db_key_querying = 'querying' |
|||
|
|||
@app.route("/search", methods=["POST"]) |
|||
def handle_query(): |
|||
id_ = flask.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"] |
|||
url_path = result_dict["url_path"] |
|||
probabilities = result_dict["probabilities"] |
|||
result_text = {'code': 200, 'url_path': url_path, 'probabilities': probabilities} |
|||
else: |
|||
querying_list = list(redis_.smembers("querying")) |
|||
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(db_key_query, 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 flask.jsonify(result_text) # 返回结果 |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
app.run(debug=True, host='0.0.0.0', port=14002) |
|||
|
|||
@ -0,0 +1,6 @@ |
|||
import requests |
|||
import time |
|||
|
|||
res = requests.get(url='http//104.244.90.248:14000/download?filename_path=03f68fd0-d05e-11ed-a1b6-aaaa001aad2e/paper.docx') |
|||
|
|||
print() |
|||
@ -0,0 +1,13 @@ |
|||
import requests |
|||
import time |
|||
|
|||
data = {"id": "58abde1c-d1ef-11ed-a2cd-aaaa001aad2e"} |
|||
|
|||
start = time.time() |
|||
|
|||
res = requests.post('http://104.244.90.248:14000/search', json=data) |
|||
end = time.time() |
|||
print(end - start) |
|||
|
|||
|
|||
print(res.text) |
|||
@ -0,0 +1,38 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/27 11:51 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
import requests |
|||
import time |
|||
|
|||
# data = { |
|||
# 'prompt': "请帮我写一篇论文，题目", |
|||
# 'text': "大型商业建筑人员疏散设计研究" |
|||
# } |
|||
# |
|||
# |
|||
# res = requests.post('http://98.142.138.229:9999/chatgpt', data=data) |
|||
# |
|||
# |
|||
# print(res.json()['res']) |
|||
# # a = pre_data_list = [""] * 5 |
|||
# # print(a) |
|||
|
|||
|
|||
import requests |
|||
import time |
|||
|
|||
data = {"prompt": "请帮帮我在改写一下句子"} |
|||
|
|||
start = time.time() |
|||
|
|||
res = requests.post('http://104.244.90.248:9999/chat', json=data) |
|||
end = time.time() |
|||
print(end - start) |
|||
|
|||
print(res.text) |
|||
@ -0,0 +1,25 @@ |
|||
import openai |
|||
|
|||
# 替换为您的API密钥 |
|||
openai.api_key = "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" |
|||
|
|||
# 设置GPT-3 API参数 |
|||
prompt = "输入您的提示文字" |
|||
model = "text-davinci-003" # 选择您想使用的模型，例如："text-davinci-003"，这是GPT-3的Davinci模型 |
|||
temperature = 0.8 |
|||
max_tokens = 100 |
|||
|
|||
# 调用GPT-3 API |
|||
response = openai.Completion.create( |
|||
engine=model, |
|||
prompt=prompt, |
|||
temperature=temperature, |
|||
max_tokens=max_tokens, |
|||
n=1, |
|||
stop=None, |
|||
echo=False |
|||
) |
|||
|
|||
# 输出结果 |
|||
generated_text = response.choices[0].text.strip() |
|||
print(generated_text) |
|||
@ -0,0 +1,27 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/30 18:04 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
import openai |
|||
import flask |
|||
|
|||
def chat_drop(): |
|||
openai.api_key = "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" |
|||
res = openai.ChatCompletion.create( |
|||
model="gpt-3.5-turbo", |
|||
messages=[ |
|||
{"role": "user", "content": "请帮我改写这句话:在城市发展进程当中，逐渐呈现出一些综合性的大型建筑群。"}, |
|||
{"role": "assistant", "content": "随着城市的发展，综合性大型建筑群正在逐渐出现。"}, |
|||
{"role": "user", "content": "这句话我不满意，再改一下帮我"} |
|||
], |
|||
temperature=0.5, |
|||
top_p=1, |
|||
) |
|||
print(res.choices[0].message.content) |
|||
|
|||
chat_drop() |
|||
@ -0,0 +1,42 @@ |
|||
# -*- coding: utf-8 -*- |
|||
|
|||
""" |
|||
@Time : 2023/3/29 16:00 |
|||
@Author : |
|||
@FileName: |
|||
@Software: |
|||
@Describe: |
|||
""" |
|||
import time |
|||
from threading import Thread |
|||
|
|||
c_list = [1,1.1,1.2] |
|||
b_list = [1,2,3,4,5,6,7,8,9,10,11,12,13,14] |
|||
|
|||
d_list = [] |
|||
|
|||
def func(a,b): |
|||
time.sleep(5) |
|||
r = a * b |
|||
c_list.append(b) |
|||
d_list.append(r) |
|||
print(r) |
|||
return r |
|||
|
|||
run_list = [] |
|||
index = 0 |
|||
start = time.time() |
|||
while True: |
|||
if c_list == []: |
|||
continue |
|||
if index == len(b_list): |
|||
break |
|||
num = c_list.pop() |
|||
t = Thread(target=func, args=(num, b_list[index])) |
|||
t.start() |
|||
index += 1 |
|||
|
|||
end = time.time() |
|||
print(end-start) |
|||
time.sleep(10) |
|||
print(d_list) |
|||
@ -0,0 +1,29 @@ |
|||
import threading |
|||
|
|||
num = 0 |
|||
lock = threading.Lock() |
|||
|
|||
|
|||
def add(): |
|||
global num |
|||
lock.acquire() |
|||
num += 1 |
|||
lock.release() |
|||
|
|||
|
|||
def sub(): |
|||
global num |
|||
lock.acquire() |
|||
num -= 1 |
|||
lock.release() |
|||
|
|||
for i in range(1000): |
|||
t1 = threading.Thread(target=add, ) |
|||
t2 = threading.Thread(target=sub, ) |
|||
t1.start() |
|||
t2.start() |
|||
t1.join() |
|||
t2.join() |
|||
print(num) |
|||
|
|||
|
|||
@ -0,0 +1,21 @@ |
|||
import multiprocessing as mp |
|||
import time |
|||
from threading import Thread |
|||
|
|||
|
|||
|
|||
api_key_list = ["1","1","1","1","1","1","1","1",] |
|||
|
|||
def job(a): |
|||
time.sleep(2) |
|||
return a |
|||
|
|||
|
|||
if __name__ == '__main__': |
|||
pool = mp.Pool() |
|||
a_list = [] |
|||
for i in range(10): |
|||
res = pool.apply_async(job,(i,)) |
|||
a_list.append(res) |
|||
for i in a_list: |
|||
print(i.get()) |
|||
@ -0,0 +1,23 @@ |
|||
import re |
|||
|
|||
|
|||
pantten_zhang = "第[0-9一二三四五六七八九]章" |
|||
pantten_biaoti = '[1一Ⅰ][、.]\s{0,}?[\u4e00-\u9fa5a-zA-Z]+' |
|||
pantten_zhang_name = "[^0-9.]+" |
|||
pantten_other_biaoti = '[2-9二三四五六七八九ⅡⅢⅣⅤⅥⅦⅧⅨ][、.]\s{0,}?[\u4e00-\u9fa5a-zA-Z]+' |
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
biaoti_list = [] |
|||
|
|||
|
|||
|
|||
text = "1.aaa绪论" |
|||
text_2 = "1.1绪论" |
|||
text_3 = "Ⅶ. asa大标题" |
|||
text_4 = "1.1绪论" |
|||
result_biaoti_list = re.findall(pantten_other_biaoti, text_3) |
|||
print(result_biaoti_list) |
|||
@ -0,0 +1,13 @@ |
|||
import requests |
|||
import time |
|||
|
|||
data = {"title": "大型商业建筑人员疏散设计研究"} |
|||
|
|||
start = time.time() |
|||
|
|||
res = requests.post('http://104.244.90.248:14000/chat', json=data) |
|||
end = time.time() |
|||
print(end - start) |
|||
|
|||
|
|||
print(res.text) |
|||
@ -0,0 +1,5 @@ |
|||
import os |
|||
print(os.path.abspath("new_data_txt")) |
|||
|
|||
uuid_path = "./new_data_txt/{}/".format("1111") |
|||
os.makedirs(uuid_path) |
|||
@ -0,0 +1,12 @@ |
|||
from flask import Flask |
|||
|
|||
app = Flask(__name__) |
|||
|
|||
|
|||
@app.route("/aa") |
|||
def index(): |
|||
return "Hello world!" |
|||
|
|||
|
|||
if __name__ == "__main__": |
|||
app.run('0.0.0.0', port=11000, debug=True) |
|||
Loading…
Reference in new issue