add a AI chatbot, rename course names

webpages/buff_bot.py

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