added chatbot to main branch

src/streamlit-ollama-chatbot.py

@@ -1,160 +0,0 @@
-import streamlit as st
-from langchain_ollama import ChatOllama
-from langchain.memory import ConversationBufferMemory
-from langchain.memory.chat_message_histories import ChatMessageHistory
-from langchain.prompts import PromptTemplate
-from langchain_core.runnables import RunnableSequence
-
-# Streamlit Setup
-st.set_page_config(layout="wide")
-st.title("My Local Chatbot")
-
-# Sidebar Inputs
-st.sidebar.header("Settings")
-
-# Dropdown for model selection
-model_options = ["llama3:8b", "deepseek-r1:1.5b"]
-MODEL = st.sidebar.selectbox("Choose a Model", model_options, index=0)
-
-# Inputs for history + context size
-MAX_HISTORY = st.sidebar.number_input("Max History", min_value=1, max_value=10, value=2, step=1)
-CONTEXT_SIZE = st.sidebar.number_input("Context Size", min_value=1024, max_value=16384, value=8192, step=1024)
-
-# Advanced Parameters
-st.sidebar.subheader("Model Parameters")
-TEMPERATURE = st.sidebar.slider("Temperature", 0.0, 1.5, 0.7, 0.1)
-TOP_P = st.sidebar.slider("Top-p (nucleus sampling)", 0.0, 1.0, 0.9, 0.05)
-TOP_K = st.sidebar.slider("Top-k", 0, 100, 40, 5)
-MAX_TOKENS = st.sidebar.number_input("Max Tokens", min_value=256, max_value=16384, value=2048, step=256)
-
-# Memory Controls 
-def clear_memory():
-    chat_history = ChatMessageHistory()
-    st.session_state.memory = ConversationBufferMemory(chat_memory=chat_history)
-    st.session_state.chat_history = []
-    st.session_state.summary = ""
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-if "memory" not in st.session_state:
-    st.session_state.memory = ConversationBufferMemory(return_messages=True)
-# NEW: Initialize a summary variable
-if "summary" not in st.session_state:
-    st.session_state.summary = ""
-
-# Button to clear memory manually
-if st.sidebar.button("Clear Conversation History"):
-    clear_memory()
-# LangChain LLM Setup
-llm = ChatOllama(
-    model=MODEL,
-    streaming=True,
-    temperature=TEMPERATURE,
-    top_p=TOP_P,
-    top_k=TOP_K,
-    num_predict=MAX_TOKENS
-)
-
-# ---
-# NEW: Summary Chain and Functions
-
-# Prompt Template for summarization
-summary_prompt_template = PromptTemplate(
-    input_variables=["chat_history"],
-    template="You are a summarizer. Summarize the following conversation to preserve key information and context. \n\n{chat_history}"
-)
-
-# Chain for summarization
-summary_chain = summary_prompt_template | llm
-
-
-def get_summary(chat_history_str):
-    """Generates a summary of the conversation history."""
-    return summary_chain.invoke({"chat_history": chat_history_str})
-
-def summarize_chat():
-    if not st.session_state.chat_history:
-        return "No chat history to summarize."
-    
-    # Pass the full chat history list to the summarization function
-    return get_summary(st.session_state.chat_history)
-
-
-if st.sidebar.button("Summarize Chat"):
-    with st.sidebar:
-        st.markdown("**Chat Summary:**")
-        summary = summarize_chat()
-        st.success(summary)
-# ---
-
-# Main Prompt Template 
-# Now includes a summary variable
-prompt_template = PromptTemplate(
-    input_variables=["summary", "history", "human_input"],
-    template="""You are a helpful assistant.
-    Current conversation summary:
-    {summary}
-
-    Conversation history:
-    {history}
-    
-    User: {human_input}
-    Assistant:"""
-)
-
-chain = prompt_template | llm
-
-# Display Chat History 
-for msg in st.session_state.chat_history:
-    with st.chat_message(msg["role"]):
-        st.markdown(msg["content"])
-
-# NEW & CORRECTED Trim Function
-def trim_memory():
-    # Trim the chat history to the MAX_HISTORY size
-    if len(st.session_state.chat_history) > MAX_HISTORY * 2:
-        # Get the history to be trimmed
-        history_to_summarize = st.session_state.chat_history[:(len(st.session_state.chat_history) - MAX_HISTORY * 2)]
-        
-        # Format the history string for the summary prompt
-        history_str = ""
-        for msg in history_to_summarize:
-            history_str += f"{msg['role']}: {msg['content']}\n"
-        
-        # Get a summary of the old messages and append to the existing summary
-        new_summary = get_summary(history_str)
-        st.session_state.summary += "\n" + new_summary
-        
-        # Remove the old messages from the chat history
-        st.session_state.chat_history = st.session_state.chat_history[(len(st.session_state.chat_history) - MAX_HISTORY * 2):]
-
-
-# Handle User Input 
-if prompt := st.chat_input("Say something"):
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    st.session_state.chat_history.append({"role": "user", "content": prompt})
-    
-    # Call the updated trim_memory function
-    trim_memory()
-
-    # Format the current, non-summarized history for the prompt template
-    formatted_history = ""
-    for msg in st.session_state.chat_history:
-        formatted_history += f"{msg['role']}: {msg['content']}\n"
-
-    with st.chat_message("assistant"):
-        response_container = st.empty()
-        full_response = ""
-
-        # Pass both 'human_input', 'history', and 'summary' to the chain
-        for chunk in chain.stream({
-                "human_input": prompt,
-                "history": formatted_history,
-                "summary": st.session_state.summary
-            }):
-            full_response += chunk.content
-            response_container.markdown(full_response)
-        
-    st.session_state.chat_history.append({"role": "assistant", "content": full_response})