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from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain_community.callbacks import StreamlitCallbackHandler
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferWindowMemory
import streamlit as st
import requests
import time
import json
import os
model_service = os.getenv("MODEL_ENDPOINT",
"http://localhost:8001")
model_service = f"{model_service}/v1"
model_service_bearer = os.getenv("MODEL_ENDPOINT_BEARER")
request_kwargs = {}
if model_service_bearer is not None:
request_kwargs = {"headers": {"Authorization": f"Bearer {model_service_bearer}"}}
@st.cache_resource(show_spinner=False)
def checking_model_service():
start = time.time()
print("Checking Model Service Availability...")
ready = False
while not ready:
try:
request_cpp = requests.get(f'{model_service}/models', **request_kwargs)
request_ollama = requests.get(f'{model_service[:-2]}api/tags', **request_kwargs)
if request_cpp.status_code == 200:
server = "Llamacpp_Python"
ready = True
elif request_ollama.status_code == 200:
server = "Ollama"
ready = True
except:
pass
time.sleep(1)
print(f"{server} Model Service Available")
print(f"{time.time()-start} seconds")
return server
def get_models():
try:
response = requests.get(f"{model_service[:-2]}api/tags", **request_kwargs)
return [i["name"].split(":")[0] for i in
json.loads(response.content)["models"]]
except:
return None
with st.spinner("Checking Model Service Availability..."):
server = checking_model_service()
def enableInput():
st.session_state["input_disabled"] = False
def disableInput():
st.session_state["input_disabled"] = True
st.title("💬 Chatbot")
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant",
"content": "How can I help you?"}]
if "input_disabled" not in st.session_state:
enableInput()
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
@st.cache_resource()
def memory():
memory = ConversationBufferWindowMemory(return_messages=True,k=3)
return memory
model_name = os.getenv("MODEL_NAME", "")
if server == "Ollama":
models = get_models()
with st.sidebar:
model_name = st.radio(label="Select Model",
options=models)
llm = ChatOpenAI(base_url=model_service,
api_key="sk-no-key-required" if model_service_bearer is None else model_service_bearer,
model=model_name,
streaming=True,
callbacks=[StreamlitCallbackHandler(st.empty(),
expand_new_thoughts=True,
collapse_completed_thoughts=True)])
prompt = ChatPromptTemplate.from_messages([
("system", "You are world class technical advisor."),
MessagesPlaceholder(variable_name="history"),
("user", "{input}")
])
chain = LLMChain(llm=llm,
prompt=prompt,
verbose=False,
memory=memory())
if prompt := st.chat_input(disabled=st.session_state["input_disabled"],on_submit=disableInput):
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").markdown(prompt)
response = chain.invoke(prompt)
st.chat_message("assistant").markdown(response["text"])
st.session_state.messages.append({"role": "assistant", "content": response["text"]})
enableInput()
st.rerun() |