import streamlit as st
import pandas as pd
# st.title('🦜🔗 Quickstart App')
st.set_page_config(page_title="Andy Chatbot")
with st.sidebar:
st.title('Andy Chatbot')
# st.success('API key already provided!', icon='✅')
# replicate_api = st.text_input('Enter Replicate API token:', type='password')
# st.warning('Please enter your credentials!', icon='⚠️')
# st.success('Proceed to entering your prompt message!', icon='👉')
# st.subheader('Models and parameters')
# selected_model = st.sidebar.selectbox('Choose a Llama2 model', ['Llama2-7B', 'Llama2-13B', 'Llama2-70B'], key='selected_model')
# if selected_model == 'Llama2-7B':
# llm = 'a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea'
# elif selected_model == 'Llama2-13B':
# llm = 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
# else:
# llm = 'replicate/llama70b-v2-chat:e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48'
# temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=5.0, value=0.1, step=0.01)
# top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01)
# max_length = st.sidebar.slider('max_length', min_value=64, max_value=4096, value=512, step=8)
# st.markdown('📖 Learn how to build this app in this [blog](https://blog.streamlit.io/how-to-build-a-llama-2-chatbot/)!')
# Store LLM generated responses
if "messages" not in st.session_state.keys():
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
# Display or clear chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
def clear_chat_history():
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
# Function for generating LLaMA2 response
def generate_llama2_response(prompt_input):
# string_dialogue = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
# for dict_message in st.session_state.messages:
# if dict_message["role"] == "user":
# string_dialogue += "User: " + dict_message["content"] + "\n\n"
# else:
# string_dialogue += "Assistant: " + dict_message["content"] + "\n\n"
output = "12345"
return output
# User-provided prompt
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = generate_llama2_response(prompt)
placeholder = st.empty()
full_response = ''
for item in response:
full_response += item
placeholder.markdown(full_response)
placeholder.markdown(full_response)
message = {"role": "assistant", "content": full_response}
st.session_state.messages.append(message)
效果如下:文章来源:https://www.toymoban.com/news/detail-837376.html
只需要效果generate_llama2_response 为你的 llm的输出即可。文章来源地址https://www.toymoban.com/news/detail-837376.html
到了这里,关于使用Streamlit 实现一个聊天机器人界面的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!