do automate things on Linux
Project description
auto_everything
Linux automation
My package dependence is not stable yet, I am considering switch the base python version back to static python2.7 or tinypy or micropython. (The current one is python3.10, I have a statically amd64 build in: https://gitlab.com/yingshaoxo/use_docker_to_build_static_python3_binary_executable)
But it is hard to make sure everything working fine 20 years later. Because I can't make sure 20 years later you can still buy a hardware that could allow you to run python2.7 or tinypy or old version of micropython. (Maybe you simply can't download a python2.7 binary or source code, or can't compile it since you don't have an old GCC.)
Installation (For Python == 3.10)
python3 -m pip install "git+https://github.com/yingshaoxo/auto_everything.git@dev" --break-system-packages
# Use github on care, you may get banned(404) by saying the 'fuck' word: https://yingshaoxo.xyz/pictures/github/index.html
or
python3 -m pip install auto_everything --break-system-packages
or
Just copy the 'auto_everything' sub_folder, then put it into the root folder of your project, so that you can directly import it.
# For amd64 linux machine, you can get a statically compiled python3.10 by doing following.
sudo su
curl -sSL https://gitlab.com/yingshaoxo/use_docker_to_build_static_python3_binary_executable/-/raw/master/install.sh?ref_type=heads | bash
What the fuck the
debian
is thinking of? Why we can't use pip to directly install a package anymore? debian/ubuntu linux branch want to force people to let their package go through a strict censorship process so that they can decide which software is good, which is not?
npx 'npm install -g *' still working fine, 'export PATH=$PATH:/**/bin/' still working fine.
Those assohle dictators who in charge never want to make things easy, are they?
Where is the freedom? My dear people!
What is the difference between
pip install
andapt install
? Simply because pypi has more freedom?
Installation (For 3.5 <= Python < 3.10)
sudo pip3 install auto_everything==3.9
or
poetry add auto_everything==3.9
# use poetry on care, it won't tell you the path where your package installed
Basic API
Import
from auto_everything.terminal import Terminal
t = Terminal()
Run a command and get reply
reply = t.run_command('uname -a')
print(reply)
Run commands and get direct screen output
commands = """
sudo apt update
uname -a
"""
t.run(commands)
Run a program
t.run_program('firefox')
Run a python script
t.run_py('your_file.py')
Run a bash script
t.run_sh('your_file.sh')
Detect if a program or script is running
status = t.is_running('terminal')
print(status)
Kill it
t.kill('terminal')
For simplifying python development
Import
from auto_everything.python import Python
py = Python()
Turn Python Class
into a Command Line Program
py.fire(your_class_name)
Make it global executable
:
py.make_it_global_runnable(executable_name="Tools")
Example
Let's assume you have a file named Tools.py
:
from auto_everything.base import Python
py = Python()
class Tools():
def push(self, comment):
t.run('git add .')
t.run('git commit -m "{}"'.format(comment))
t.run('git push origin')
def pull(self):
t.run("""
git fetch --all
git reset --hard origin/master
""")
def undo(self):
t.run("""
git reset --mixed HEAD~1
""")
def reset(self):
t.run("""
git reset --hard HEAD^
""")
def hi(self):
print("Hi, Python!")
py.fire(Tools)
py.make_it_global_runnable(executable_name="MyTools")
After the first running of this script by python3 Tools.py hi
, you would be able to use MyTools
to run this script at anywhere within your machine:
yingshaoxo@pop-os:~$ MyTools hi
Hi, Python!
For simplifying general server and client
development
Define YRPC Protocols
service Greeter {
rpc say_hello (hello_request) returns (HelloReply);
}
enum UserStatus {
OFFLINE = 0;
ONLINE = 1;
}
message hello_request {
string name = 1;
UserStatus user_status = 2;
repeated UserStatus user_status_list = 3;
}
message HelloReply {
string message = 1;
}
Generate Python, Flutter, Typescript
code
from auto_everything.develop import YRPC
yrpc = YRPC()
for language in ["python", "dart", "typescript"]:
yrpc.generate_code(
which_language=language,
input_folder="/home/yingshaoxo/CS/protocol_test/protocols",
input_files=["english.proto"],
output_folder="/Users/yingshaoxo/CS/protocol_test/generated_yrpc"
)
Here, we only use python to do the server part job.
Then, you can use it like this:
from generated_yrpc.english_rpc import *
class NewService(Service_english):
async def say_hello(self, item: hello_request) -> HelloReply:
reply = HelloReply()
reply.message = item.name
return reply
service_instance = NewService()
run(service_instance, port="6060")
void main() async {
var client = Client_english(
service_url: "http://127.0.0.1:6060",
error_handle_function: (error_message) {
print(error_message);
},
);
var result = await client.say_hello(
item: hello_request(name: "yingshaoxo")
);
if (result != null) {
print(result);
}
}
Others
Simpler IO
from auto_everything.base import IO
io = IO()
io.write("hi.txt", "Hello, world!")
print(io.read("hi.txt"))
io.append("hi.txt", "\n\nI'm yingshaoxo.")
print(io.read("hi.txt"))
Quick File Operation
from auto_everything.disk import Disk
from pprint import pprint
disk = Disk()
files = disk.get_files(folder=".", type_limiter=[".mp4"])
files = disk.sort_files_by_time(files)
pprint(files)
Easy Store
from auto_everything.disk import Store
store = Store("test")
store.set("author", "yingshaoxo")
store.delete("author")
store.set("author", {"email": "yingshaoxo@gmail.com", "name": "yingshaoxo"})
print(store.get_items())
print(store.has_key("author"))
print(store.get("author", default_value=""))
print(store.get("whatever", default_value="alsjdasdfasdfsakfla"))
store.reset()
print(store.get_items())
Encryption and Decryption
encryption_and_decryption = EncryptionAndDecryption()
a_dict = encryption_and_decryption.get_secret_alphabet_dict("hello, world")
a_sentence = "I'm yingshaoxo."
encrypted_sentence = encryption_and_decryption.encode_message(a_secret_dict=a_dict, message=a_sentence)
print()
print(encrypted_sentence)
> B'i ybjdqahkxk.
decrypted_sentence = encryption_and_decryption.decode_message(a_secret_dict=a_dict, message=encrypted_sentence)
print(decrypted_sentence)
> I'm yingshaoxo.
JWT Tool (Json-Web-Token Tool)
jwt_tool = JWT_Tool()
secret = "I'm going to tell you a secret: yingshaoxo is the best."
a_jwt_string = jwt_tool.my_jwt_encode(data={"name": "yingshaoxo"}, a_secret_string_for_integrity_verifying=secret)
print(a_jwt_string)
> eyJhbGciOiAiTUQ1IiwgInR5cCI6ICJKV1QifQ==.eyJuYW1lIjogInlpbmdzaGFveG8ifQ==.583085987ba46636662dc71ca6227c0a
original_dict = jwt_tool.my_jwt_decode(jwt_string=a_jwt_string, a_secret_string_for_integrity_verifying=secret)
print(original_dict)
> {'name': 'yingshaoxo'}
fake_jwt_string = "aaaaaa.bbbbbb.abcdefg"
original_dict = jwt_tool.my_jwt_decode(jwt_string=fake_jwt_string, a_secret_string_for_integrity_verifying=secret)
print(original_dict)
> None
Web automation
from auto_everything.web import Selenium
my_selenium = Selenium("https://www.google.com", headless=False)
d = my_selenium.driver
# get input box
xpath = '//*[@id="lst-ib"]'
elements = my_selenium.wait_until_elements_exists(xpath)
if len(elements) == 0:
exit()
# text inputing
elements[0].send_keys('\b' * 20, "yingshaoxo")
# click search button
elements = my_selenium.wait_until_elements_exists('//input[@value="Google Search"]')
if len(elements):
elements[0].click()
# exit
my_selenium.sleep(30)
d.quit()
Yingshaoxo machine learning ideas
For natual language process
We treat every char as an id or tensor element
In GPU based machine learning algorithm, you will often do things with [23, 32, 34, 54]
But now, it becomes ['a', 'b', 'c', 'd']
For translation
long sequence (meaning group) -> long sequence (meaning group)
what you do -> 你干什么 It depends on -> 这取决于
(It depends on) (what you do) -> 这取决于 你干什么
meaning group can be get automatically, all you have to do is count continues_words appearance time. the more time a continuse_words appear, the more likely it is a meaning group
It all can be summaryed as "divide and conquer"
For question and answer
For context information extraction, you have to use the question. If one sentence of the context should at the bottom of the question, you keep it, otherwise, you remove it
Then, for the other context, you do a simple sort
For text generation
one char predict next char
two char predict next char
...
one word predict next word
two words predict next word
three words predict next word
...
when you use it, use it from bottom to top, use longest sequence to predict the next word first.
the more level you make, the more accurate it would be.
It is dict based next word generator, so the speed is super quick
This method was created by yingshaoxo. it only need cpu than gpu. it can beat gpt4 with an old computer if you have big dataset (30GB) and big memory to hold the dict.
For general AI
General AI algorithm:
Natural language -> Python programming language -> Go through CPU -> If it is working, add weights to that sentence, if it is not working, minus weights for that sentence -> use long sub_string weights to generate more following natural language sentences -> execute more programming language that was translated from natural language -> it is a never end loop, but if the storage is about to blow, we need to find a way to do compression and find more way to store data.
Those code are generated in real time. For each response, it generate different algorithm or code. It adopts to any situation.
#yingshaoxo
#yingshaoxo: I could give you a template for general AI
import json
from auto_everything.terminal import Terminal
terminal = Terminal()
global_dict = {}
def update_global_dict_based_on_new_information(input_text):
global global_dict
pass
def natual_language_to_task_code(input_text):
# This code will only use global_dict as data source
global global_dict
raw_data = json.dumps(global_dict)
previous_code = f'''
import json
global_dict = json.loads('{raw_data}')\n
'''
code = previous_code + f"print('{input_text}')"
# You have to let the machine generate different code or algorithm for different input_text, so that each time the reply is different.
return code
def execute_code(code):
# For example, execute python code.
result = terminal.run_python_code(code)
return result
previous_context = ""
while True:
input_text = input("What you want to say? ")
code = natual_language_to_task_code(input_text)
result = execute_code(code)
print(result)
new_information = input_text + "\n\n\n" + result
previous_context += new_information
update_global_dict_based_on_new_information(new_information)
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