Samuele E. Locatelli
67 lines
2.1 KiB
Plaintext
67 lines
2.1 KiB
Plaintext
####
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#### Streamlit Streaming using LM Studio as OpenAI Standin
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#### run with `streamlit run app.py`
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# !pip install pypdf langchain langchain_openai
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import streamlit as st
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_openai import ChatOpenAI
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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# app config
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st.set_page_config(page_title="Egalware Chatbot", page_icon="🤖")
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st.title("Egalware's Chatbot")
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def get_response(user_query, chat_history):
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template = """
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You are a helpful assistant. Answer the following questions considering the history of the conversation:
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Chat history: {chat_history}
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User question: {user_question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# Using LM Studio Local Inference Server
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llm = ChatOpenAI(base_url="http://10.74.83.100:1234/v1",api_key="lm-studio", model="qwen/qwen3-4b-2507")
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chain = prompt | llm | StrOutputParser()
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return chain.stream({
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"chat_history": chat_history,
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"user_question": user_query,
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})
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# session state
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = [
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AIMessage(content="Hello, I am EgalWare's current ChatBot. How can I help you? (puoi fare domande in italiano, ma in inglese funziona meglio...)"),
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]
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# conversation
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for message in st.session_state.chat_history:
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if isinstance(message, AIMessage):
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with st.chat_message("AI"):
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st.write(message.content)
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elif isinstance(message, HumanMessage):
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with st.chat_message("Human"):
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st.write(message.content)
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# user input
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user_query = st.chat_input("Type your message here...")
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if user_query is not None and user_query != "":
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st.session_state.chat_history.append(HumanMessage(content=user_query))
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with st.chat_message("Human"):
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st.markdown(user_query)
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with st.chat_message("AI"):
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response = st.write_stream(get_response(user_query, st.session_state.chat_history))
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st.session_state.chat_history.append(AIMessage(content=response))
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