add chatbot with local llama3.2 1B model on Ollama

chatbot_config.json

@@ -0,0 +1,6 @@
+{
+    "api_url": "https://api.deepseek.com",
+    "api_key": "sk-12165b127043441697a8940918e207ac",
+    "ollama_api_url": "http://localhost:11434/v1",
+    "ollama_api_key": "ollama"
+}

config.json

@@ -1,4 +0,0 @@
-{
-    "api_url": "https://api.deepseek.com",
-    "api_key": "sk-12165b127043441697a8940918e207ac"
-}

deepseek_chatbot.py

@@ -12,7 +12,7 @@ with st.expander("See Source Code"):
                 st.code(f.read(), language="python")
 
 # Load API credentials from config.json
-with open('config.json') as config_file:
+with open('chatbot_config.json') as config_file:
     config = json.load(config_file)
     openai_api_base_url = config["api_url"]
     openai_api_key = config["api_key"]

llama_chatbot.py

@@ -0,0 +1,73 @@
+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
+# - finetune: https://docs.loopin.network/tutorials/LLM/llama3-finetune
+# - Ollama docker: https://hub.docker.com/r/ollama/ollama
+
+
+
+
+# Set up the Streamlit app
+st.markdown("<h1 style='text-align: center; color: #451002;'>BuffBot🦬</h1>", unsafe_allow_html=True)
+# st.subheader()
+st.info("Powered by llama3.2:1b model via [Ollama](https://ollama.com/library/llama3.2:1b)!")
+with st.expander("See Source Code"):
+            with open(__file__, "r") as f:
+                st.code(f.read(), language="python")
+
+# Load API credentials from config.json
+with open('chatbot_config.json') as config_file:
+    config = json.load(config_file)
+    api_base_url = config["ollama_api_url"]
+    api_key = config["ollama_api_key"]
+
+client = OpenAI(api_key=api_key, base_url=api_base_url)
+
+# Initialize session state to store chat history and message count
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# print welcome message
+with st.chat_message("assistant", avatar="🦬"):
+    st.markdown("Welcome to BuffBot! How can I help you today??")
+    
+# Display chat history
+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"])
+
+# Chat input
+if prompt := st.chat_input("Type your message..."):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user", avatar="🤠"):
+        st.markdown(prompt)
+
+    # Call DeepSeek for a response
+    with st.chat_message("assistant", avatar="🦬"):
+        with st.spinner('Thinking...'):
+            stream = client.chat.completions.create(
+                model="llama3.2:1b",
+                messages=[
+                    {"role": m["role"], "content": m["content"]}
+                    for m in st.session_state.messages
+                ],
+                stream=True, 
+            )
+        response = st.write_stream(stream)
+    st.session_state.messages.append({"role": "assistant", "content": response})
+
+    
+if st.button("Clear Chat"):
+    st.session_state.messages = []
+    st.toast("Chat Cleaned", icon="🧹")
+
+
+