File size: 4,014 Bytes
238f0a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import streamlit as st
import re
from langchain.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
st.set_page_config(layout="wide")
st.title("My Local Chatbot")
model_options = ["llama3.2", "deepseek-r1:1.5b"]
model = st.sidebar.selectbox("Choose a Model", model_options, index=0)
st.sidebar.write("Advanced Settings")
max_history = st.sidebar.number_input("Max History", min_value=1, max_value=10, value=2, step=1)
context_size = st.sidebar.number_input("Max Token", min_value=1024, max_value=2048, value=1024, step=1024)
top_p = st.sidebar.number_input("Top-P", min_value=0.8, max_value=0.95, value=0.8, step=0.01)
top_k = st.sidebar.number_input("Top-K", min_value=20, max_value=50, value=20, step=10)
temperature = st.sidebar.number_input("Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.1)
def clear_memory():
st.session_state.memory = ConversationBufferMemory(return_messages=True)
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
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_message=True)
llm = ChatOllama(model=model, streaming=True, temperature=temperature, top_p=top_p, top_k=top_k)
prompt_template = PromptTemplate(
input_variable = ["history", "human_input"],
template="{history}\nJangan tampilkan reasoning internal (<think>...</think>), cukup jawab final saja.\nUser: {human_input}\nAssistant:"
)
chain = LLMChain(llm=llm, prompt=prompt_template, memory=st.session_state.memory)
if "prev_model" not in st.session_state:
st.session_state.prev_model = model
if st.session_state.prev_model != model:
st.session_state.chat_history = []
st.session_state.memory = ConversationBufferMemory(return_messages=True)
st.session_state.prev_model = model
for msg in st.session_state.chat_history:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
def trim_memory():
while len(st.session_state.chat_history) > max_history * 2:
st.session_state.chat_history.pop(0)
if st.session_state.chat_history:
st.session_state.chat_history.pop(0)
def clean(text):
return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()
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})
trim_memory()
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 = clean(chunk["text"])
full_response += text_chunk
response_container.markdown(full_response)
st.session_state.chat_history.append({"role": "assistant", "content": full_response})
trim_memory()
if st.sidebar.button("Summarize Conversation"):
if st.session_state.chat_history:
history_text = "\n".join(
[f"{msg['role'].capitalize()}: {msg['content']}" for msg in st.session_state.chat_history]
)
summarize_prompt = PromptTemplate(
input_variables=["history"],
template="Summarize the following conversation briefly:\n\n{history}\n\nSummary:"
)
summarize_chain = LLMChain(llm=llm, prompt=summarize_prompt)
with st.spinner("Summarizing..."):
summary = summarize_chain.run({"history": history_text})
st.subheader("Summary")
st.write(clean(summary))
else:
st.sidebar.warning("No conversation history to summarize.")
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