chatbot

chatbot/.streamlit/config.toml

@@ -0,0 +1,9 @@
+[client]
+showErrorDetails = "none"
+
+[theme]
+primaryColor="#ffa7df"
+backgroundColor="#D89ABF"
+secondaryBackgroundColor="#000000"
+textColor="#ffffff"
+

chatbot/chatbot.py

@@ -0,0 +1,159 @@
+import streamlit as st
+from langchain_community.chat_models import ChatOllama
+from langchain.schema import HumanMessage, AIMessage
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+
+# ---- Streamlit Setup ---- #
+st.set_page_config(layout="wide")
+st.markdown(
+    "<h1 style='text-align: center; color: inherit;'>⎚-⎚ \nMikeyBot</h1>",
+    
+    unsafe_allow_html=True
+)
+st.markdown(
+    
+    """
+    <style>
+    .st-emotion-cache-4zpzjl {
+        background-color: #bf498f;
+        color: #ffffff;
+    }
+            
+    .st-emotion-cache-jmw8un{
+        background-color: #ffd1ec;
+        color: #000000 ;
+    }
+
+    .st-emotion-cache-1k8897g{
+        background-color: #d98dba;
+    }
+   
+    </style>
+""", unsafe_allow_html=True)
+
+# ---- Sidebar Inputs ---- #
+st.sidebar.header("⚙️ Settings")
+
+# Dropdown for model selection
+model_options = ["llama3.2", "deepseek-r1:1.5b"]
+MODEL = st.sidebar.selectbox("Choose a Model", model_options, index=0)
+
+# add advanced settings
+with st.sidebar.expander("Advanced Settings"):
+    temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
+    top_p       = st.slider("Top-P", 0.0, 1.0, 0.9)
+    top_k       = st.slider("Top-K", 1, 100, 40)
+    max_tokens  = st.slider("Max Tokens", 64, 2048, 512)
+
+# max history and 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)
+MAX_HISTORY = 2         
+CONTEXT_SIZE = 8192   
+
+# ---- Function to Clear Memory When Settings Change ---- #
+def clear_memory():
+    st.session_state.chat_history = []
+    st.session_state.memory = ConversationBufferMemory(return_messages=True)  # Reset memory
+
+# Clear memory if settings are changed
+if "prev_context_size" not in st.session_state or st.session_state.prev_context_size != CONTEXT_SIZE:
+    clear_memory()
+    st.session_state.prev_context_size = CONTEXT_SIZE
+
+# ---- Initialize Chat Memory ---- #
+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)
+
+# ---- LangChain LLM Setup ---- #
+llm = ChatOllama(
+    model=MODEL,
+    streaming=True,
+    temperature=temperature,
+    top_p=top_p,
+    top_k=top_k,
+    num_predict=max_tokens,
+    num_ctx=CONTEXT_SIZE,  
+)
+
+# for summarize button
+if st.sidebar.button("Summarize Chat"):
+    with st.spinner("Summarizing..."):
+        # Format history nicely
+        history_text = "\n".join(
+            [f"{m['role']}: {m['content']}" for m in st.session_state.chat_history]
+        )
+
+        # Build prompt
+        summary_prompt = [
+            {"role": "system", "content": "You are a helpful assistant that summarizes conversations."},
+            {"role": "user", "content": f"Please summarize this conversation:\n\n{history_text}"}
+        ]
+
+        # Call model using LangChain
+        summary_result = llm.invoke(summary_prompt[1]["content"])
+        summary = summary_result.content if hasattr(summary_result, 'content') else str(summary_result)
+
+        # Save summary 
+        st.session_state.chat_history.append(
+            {"role": "assistant", "content": summary}
+        )
+
+        # Show in chat
+        with st.chat_message("assistant"):
+            st.markdown(summary)
+
+# ---- Prompt Template ---- #
+prompt_template = PromptTemplate(
+    input_variables=["history", "human_input"],
+    template="{history}\nUser: {human_input}\nAssistant:"
+)
+
+chain = LLMChain(llm=llm, prompt=prompt_template, memory=st.session_state.memory)
+
+# ---- Display Chat History ---- #
+for msg in st.session_state.chat_history:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+# ---- Trim Function (Removes Oldest Messages) ---- #
+def trim_memory():
+    while len(st.session_state.chat_history) > MAX_HISTORY * 2:  # Each cycle has 2 messages (User + AI)
+        st.session_state.chat_history.pop(0)  # Remove oldest User message
+        if st.session_state.chat_history:
+            st.session_state.chat_history.pop(0)  # Remove oldest AI response
+
+# ---- Handle User Input ---- #
+if prompt := st.chat_input("Say something"):
+    # Show User Input Immediately
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    st.session_state.chat_history.append({"role": "user", "content": prompt})  # Store user input
+
+    # Trim chat history before generating response
+    trim_memory()
+
+    # ---- Get AI Response (Streaming) ---- #
+    with st.chat_message("assistant"):
+        response_container = st.empty()
+        full_response = ""
+
+        for chunk in chain.stream({"human_input": prompt}):
+            if isinstance(chunk, dict) and "text" in chunk:
+                text_chunk = chunk["text"]
+                full_response += text_chunk
+                response_container.markdown(full_response)
+
+    # Store response in session_state
+    st.session_state.chat_history.append({"role": "assistant", "content": full_response})
+
+    # Trim history after storing the response
+    trim_memory()
+