144 lines
5.9 KiB
Python
144 lines
5.9 KiB
Python
import streamlit as st
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from openai import OpenAI # OpenAI compatibility
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import json
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# reference:
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# - Use OpenAI to connect Ollama: https://ollama.com/blog/openai-compatibility
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# - Build Chatbot with streamlit: https://streamlit.io/generative-ai
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# - Ollama docker: https://hub.docker.com/r/ollama/ollama
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# - [TBD] Finetune: https://docs.loopin.network/tutorials/LLM/llama3-finetune
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# Clear chat history
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def clear_chat():
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st.session_state.messages = []
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st.toast("Chat Cleaned", icon="🧹")
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def buffbot():
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# Set up the Streamlit app
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st.markdown("<h1 style='text-align: center; color: #451002;'>BuffBot🦬</h1>", unsafe_allow_html=True)
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st.markdown("<h5 style='text-align: center;'> Your friendly AI chatbot powered by LLM! 🤖 </h3>", unsafe_allow_html=True)
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# Display info and source code
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with st.expander("See Source Code"):
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with open(__file__, "r", encoding="utf-8") as f:
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st.code(f.read(), language="python")
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st.divider()
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# Select AI model for chatbot
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model_options = ["deepseek-r1:1.5b", "llama3.2:1b", "deepseek-chat", ]
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# on_change callback to clear chat history when model is changed
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selected_model = st.selectbox("**👉Please select a model to start**", model_options, on_change=clear_chat)
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# Initialize session state to store chat history and message count
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Initialize message count
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if "message_count" not in st.session_state:
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st.session_state.message_count = 0
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# Load API credentials from config.json
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# the config file contains the API key and base URL for the selected model
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"""
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{
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"deepseek":{
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"api_url": "https://api.deepseek.com",
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"api_key": "YOUR_API_KEY",
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"model":"deepseek-chat"
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},
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"ollama3.2:1b":{
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"api_url": "http://localhost:11434/v1",
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"api_key": "ollama",
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"model":"llama3.2:1b"
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},
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"deepseek-r1:1.5b":{
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"api_url": "http://localhost:11434/v1",
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"api_key": "ollama",
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"model":"deepseek-r1:1.5b"
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},
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}
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"""
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# The API key and base URL are loaded based on the selected model
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with open('app_config.json') as config_file:
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config = json.load(config_file)
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# deepseek-chat model, online API
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if selected_model == "deepseek-chat":
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api_base_url = config[selected_model]["api_url"]
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api_key = config[selected_model]["api_key"]
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model = config[selected_model]["model"]
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st.info("Powered by the online [DeepSeek](https://www.deepseek.com/) API!\
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Just a heads up, you have 10 messages to use.")
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# Set the maximum number of user messages
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MAX_USER_MESSAGES = 10
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# llama3.2:1b model, local API
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if selected_model == "llama3.2:1b":
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api_base_url = config[selected_model]["api_url"]
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api_key = config[selected_model]["api_key"]
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model = config[selected_model]["model"]
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st.info("Powered by local llama3.2:1b model via [Ollama](https://ollama.com/library/llama3.2:1b)!\
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Just a heads up, you have 100 messages to use.")
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MAX_USER_MESSAGES = 100
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if selected_model == "deepseek-r1:1.5b":
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api_base_url = config[selected_model]["api_url"]
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api_key = config[selected_model]["api_key"]
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model = config[selected_model]["model"]
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st.info("Powered by local deepseek-r1:1.5b model via [Ollama](https://ollama.com/library/deepseek-r1:1.5b)!\
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Just a heads up, you have 100 messages to use.")
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MAX_USER_MESSAGES = 100
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# Initialize OpenAI client to connect with the selected model API
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client = OpenAI(api_key=api_key, base_url=api_base_url)
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# print welcome message
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with st.chat_message("assistant", avatar="🦬"):
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st.markdown("Welcome to BuffBot! What Can I Do for You Today?🌞")
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# Display chat history with different avatars for user and AI assistant
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for message in st.session_state.messages:
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if message["role"] == "user":
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avatar="🤠"
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else:
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avatar="🦬"
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with st.chat_message(message["role"], avatar=avatar):
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st.markdown(message["content"])
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if st.session_state.message_count < MAX_USER_MESSAGES:
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# Get user input
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if prompt := st.chat_input("Type your message here..."):
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.session_state.message_count += 1
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# Display user message with cowboy avatar
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with st.chat_message("user", avatar="🤠"):
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st.markdown(prompt)
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# Generate reply
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with st.chat_message("assistant", avatar="🦬"):
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with st.spinner('Thinking...'):
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# Call the selected model API to generate a response
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stream = client.chat.completions.create(
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model=selected_model,
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messages=[
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{"role": m["role"], "content": m["content"]}
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for m in st.session_state.messages
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],
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stream=True, # stream the response
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)
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# Display the response from the model API
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response = st.write_stream(stream)
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# Add the AI assistant response to the chat history
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st.session_state.messages.append({"role": "assistant", "content": response})
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else:
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st.warning("You have reached the maximum number of messages allowed.\
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Please switch to another model to continue chatting.")
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# Clear chat history
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if st.button("Clear Chat"):
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clear_chat()
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st.rerun()
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