streamlit：如何快速构建一个应用，不会前端也能写出好看的界面 - ToB企服应用市场:ToB评测及商务社交产业平台

pip install streamlit
streamlit hello

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import numpy as npimport streamlit as stimport pandas as pdst.title('My Streamlit App')st.write('Hello, world!')
st.header('This is a header')
st.subheader('This is a subheader')
st.text('This is some text.')
st.markdown('**This** is some Markdown *text*.')
from PIL import Imageimage = Image.open('../data/stream_demo/onetwo.jpeg')st.image(image, caption='Sunset', use_column_width=True)import matplotlib.pyplot as pltplt.plot([1, 2, 3])st.pyplot()import altair as altchart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])chart = alt.Chart(chart_data).mark_bar().encode( x='category', y='count')c = ( alt.Chart(chart_data) .mark_circle() .encode(x="a", y="b", size="c", color="c", tooltip=["a", "b", "c"]) )st.altair_chart(c, use_container_width=True)df = pd.DataFrame({'Column 1': [1, 2, 3], 'Column 2': [4, 5, 6]})st.dataframe(df)option = st.selectbox('Choose an option', ['Option 1', 'Option 2', 'Option 3'])
value = st.slider('Select a value', 0, 100, 50)
if st.button('Click me'):
st.write('Button clicked!')

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import streamlit as st
if __name__ == '__main__':
st.title('Chat with me :sunflower:')
# 初始化history
if "messages" not in st.session_state:
st.session_state.messages = []
# 展示对话
for msg in st.session_state.messages:
with st.chat_message(msg['role']):
st.markdown(msg["content"])
# React to user input
if prompt := st.chat_input("Say something"):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
response = f"Echo: {prompt}"
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})

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import os
import sys
import time
import streamlit as st
from llama_cpp import Llama
from dotenv import load_dotenv, find_dotenv
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
# 加载env环境中内容
_ = load_dotenv(find_dotenv())
def get_llm_model(
prompt: str = None,
model: str = None,
temperature: float = 0.0,
max_token: int = 2048,
n_ctx: int = 512,
stream: bool = False):
"""
根据模型名称去加载模型，返回response数据
:param stream:
:param prompt:
:param model:
:param temperature:
:param max_token:
:param n_ctx:
:return:
"""
if model in ['Qwen_q2']:
model_path = os.environ[model]
llm = Llama(model_path=model_path, n_ctx=n_ctx)
start = time.time()
response = llm.create_chat_completion(
messages=[
{
"role": "system",
"content": "你是一个智能超级助手，请用专业的词语回答问题，整体上下文带有逻辑性，如果不知道，请不要乱说",
},
{
"role": "user",
"content": "{}".format(prompt)
},
],
temperature=temperature,
max_tokens=max_token,
stream=stream
)
cost = time.time() - start
print(f"模型生成时间：{cost}")
print(f"大模型回复：\n{response}")
if not stream:
return response['choices'][0]['message']['content']
else:
return response
def get_llm_model_with_stream(
prompt: str = None,
model: str = None,
temperature: float = 0.0,
max_token: int = 2048,
n_ctx: int = 512,
stream: bool = True):
"""
流式输出结果
:param prompt:
:param model:
:param temperature:
:param max_token:
:param n_ctx:
:param stream:
:return:
"""
response = ""
start_time = time.time()
stream_results = get_llm_model(prompt, model, temperature, max_token, n_ctx, stream)
for res in stream_results:
content = res["choices"][0].get("delta", {}).get("content", "")
response += content
yield content
cost_time = time.time() - start_time
print(f"cost_time: {cost_time}, response: {response}")
if __name__ == '__main__':
st.title('Chat with Qwen :sunflower:')
# 初始化history
if "messages" not in st.session_state:
st.session_state.messages = []
# 展示对话
for msg in st.session_state.messages:
with st.chat_message(msg['role']):
st.markdown(msg["content"])
# React to user input
if prompt := st.chat_input("Say something"):
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# 普通方式输出
# if prompt is not None:
# response = get_llm_model(prompt=prompt, model="Qwen_q2")
# # Display assistant response in chat message container
# with st.chat_message("assistant"):
# st.markdown(response)
# # Add assistant response to chat history
# st.session_state.messages.append({"role": "assistant", "content": response})
# 流式输出
if prompt is not None:
stream_res = get_llm_model_with_stream(prompt=prompt, model="Qwen_q2")
with st.chat_message("assistant"):
content = st.write_stream(stream_res)
print("流式输出：", content)
st.session_state.messages.append({"role": "assistant", "content": content})
# streamlit run chat_with_qwen.py

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