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import streamlit as st
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain.schema import HumanMessage, SystemMessage, AIMessage
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("Hey, I'm your DeepSeek")
if "sessionMessages" not in st.session_state:
st.session_state.sessionMessages = [
SystemMessage(content="You are a helpful assistant.")
]
def load_answer(question):
st.session_state.sessionMessages.append(HumanMessage
(content=question))
assistant_answer = chat_model.invoke(st.session_state.sessionMessages)
if isinstance(assistant_answer, AIMessage):
response_text = assistant_answer.content
elif isinstance(assistant_answer, dict) and "content" in assistant_answer:
response_text = assistant_answer["content"]
else:
response_text = str(assistant_answer)
st.session_state.sessionMessages.append(AIMessage(content=response_text))
return response_text
def get_text():
input_text = st.text_input("You: ")
return input_text
llm = HuggingFaceEndpoint(
repo_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
task="text-generation",
max_new_tokens=512,
do_sample=True,
temperature=0.7,
repetition_penalty=1.03,
)
chat_model = ChatHuggingFace(llm=llm)
user_input = get_text()
submit = st.button('Generate')
if submit:
response = load_answer(user_input)
st.subheader("Answer: ")
st.write(response) |