project restructuring so there'll be no submodule

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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.streamlit/config.toml

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+[browser]
+gatherUsageStats = false

Dockerfile

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+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit-ollama-chatbot.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

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+---
+title: My Local Chatbot
+emoji: 
+colorFrom: red
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.25.0
+app_file: streamlit-ollama-chatbot.py
+---

requirements.txt

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+altair
+pandas
+streamlit
+langchain
+langchain-ollama

src/streamlit-ollama-chatbot.py

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+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})

streamlit-ollama-chatbot

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-Subproject commit 7370ae6148dd64375b6b86564cd1b5cd51ef46c3