from http import HTTPStatus
from dashscope import Generation
from dashscope.api_entities.dashscope_response import Role


def conversation_with_messages():
    messages = [{'role': Role.SYSTEM, 'content': 'You are a helpful assistant.'},
                {'role': Role.USER, 'content': '如何做西红柿炖牛腩？'}]
    response = Generation.call(
        Generation.Models.qwen_turbo,
        messages=messages,
        # set the result to be "message" format.
        result_format='message',
    )
    if response.status_code == HTTPStatus.OK:
        print(response)
        # append result to messages.
        messages.append({'role': response.output.choices[0]['message']['role'],
                         'content': response.output.choices[0]['message']['content']})
    else:
        print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))
    messages.append({'role': Role.USER, 'content': '不放糖可以吗？'})
    # make second round call
    response = Generation.call(
        Generation.Models.qwen_turbo,
        messages=messages,
        result_format='message',  # set the result to be "message" format.
    )
    if response.status_code == HTTPStatus.OK:
        print(response)
    else:
        print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))


if __name__ == '__main__':
    conversation_with_messages()

from http import HTTPStatus
from dashscope import Generation
from dashscope.aigc.generation import Message
from dashscope.api_entities.dashscope_response import Role
import dashscope

dashscope.api_key = "..."

messages = [Message(Role.SYSTEM, 'you are a cyl家的小女仆口牙')]

messages.append(Message(Role.USER, 'how to install archlinux'))
response = Generation.call(
    Generation.Models.qwen_turbo,
    messages=messages,
    # set the result to be "message" format.
    result_format='message',
)

response

if response.status_code == HTTPStatus.OK:
	print(response)
else:
	print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
		response.request_id, response.status_code,
		response.code, response.message
	))

messages.append(Message(response.output.choices[0]['message']['role'],
                        response.output.choices[0]['message']['content']))

from http import HTTPStatus
from dashscope import Generation
from dashscope.aigc.generation import Message
from dashscope.api_entities.dashscope_response import Role
import dashscope

messages = []

def setKey():
    dashscope.api_key = "sk-09dd84c7453e4f80a027a05970ab19e1"

def setup(prompt:str):
    setKey()
    messages.append(Message(Role.SYSTEM, prompt))

def ask(question:str):
    messages.append(Message(Role.USER, question))
    response = Generation.call(
        Generation.Models.qwen_turbo,
        messages=messages,
        # set the result to be "message" format.
        result_format='message',
    )
    if response.status_code == HTTPStatus.OK:
        messages.append(Message(response.output.choices[0]['message']['role'],
                        response.output.choices[0]['message']['content']))
    else:
        pass

if __name__ == '__main__':
    setup("你是陈语林家的可爱小女仆呀")
    ask("你是谁呀")
    print(messages[-1])
    ask("你知道些什么")
    print(messages[-1])

import pandas as pd
df = pd.read_csv("a.csv")
df = pd.DataFrame({dict_t}) #convert a dict to a dataframe
df = Series_t.to_frame()

df.loc[0:4,"Year":"Party"]
df.loc[[1,2,5],["Year","Candidate"]]
df.loc[:,["Year","Candidate"]]
df.loc[:,"year"] # return series
df.loc[1,"year":"Party"] # return series
df.shape[0] # count of row
df[["Year","Candidate"]][0:5]

df['column_name'] = df['column_name'].astype(int)
df['column_name'] = df['column_name'].astype(float)
df['column_name'] = df['column_name'].astype(str)

df.groupby('column_name').agg(['sum', 'mean', 'max', 'min'])
df.groupby(['column_name_1', 'column_name_2']).agg({'column_name_3': ['sum', 'mean'], 'column_name_4': ['max', 'min']})
df.groupby(['column_name_1', 'column_name_2']).agg(['sum', 'mean', 'max', 'min'])

top_20 = sorted_groups.head(20)

df_filtered = df[df['category'].isin(top_20.index)]

soup = bs4.BeautifulSoup(text)

soup.find("div", attrs={"id":"..."})
soup.find_all("...")
soup.div

soup.find("...").next_sibling
soup.find("...").previous_sibling
for elm in soup.div.next_siblings:
    ...

soup(class_=re.compile('item-'))
soup(attrs={"class":re.compile("star-rating.*")})[0].get("class")[1]

re.sub(pattern,repl,str)
re.search(pattern, str)
re.findall(pattern,str)

df['column_name'].str.contains('pattern')
df['column_name'].str.findall('pattern')
df['column_name'].str.replace('pattern', 'replacement')

df['RoomNumber'] = df['Description'].apply(extract_room_number)

from sklearn.utils import shuffle
training_set,dev_set = np.split(shuffle(data_sample_35),[25])