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from langchain.chains import ConversationChain |
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from langchain.chains.conversation.memory import ConversationBufferWindowMemory |
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from langchain.prompts import ( |
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SystemMessagePromptTemplate, |
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HumanMessagePromptTemplate, |
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ChatPromptTemplate, |
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MessagesPlaceholder |
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) |
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import streamlit as st |
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from streamlit_chat import message |
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from utils import * |
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from langchain_community.llms import HuggingFaceHub |
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import configparser |
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import os |
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huggingfacehub_api_token = os.environ['HF_API_TOKEN'] |
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st.subheader("DSlogic") |
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if 'responses' not in st.session_state: |
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st.session_state['responses'] = ["How can I assist you?"] |
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if 'requests' not in st.session_state: |
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st.session_state['requests'] = [] |
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repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" |
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llm = HuggingFaceHub(huggingfacehub_api_token=huggingfacehub_api_token, |
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repo_id=repo_id, model_kwargs={"temperature":0.5, "max_new_tokens":250}) |
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if 'buffer_memory' not in st.session_state: |
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st.session_state.buffer_memory=ConversationBufferWindowMemory(k=3,return_messages=True) |
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system_msg_template = SystemMessagePromptTemplate.from_template(template="""You are a Data Science expert and are helping data scientists. Answer the question as truthfully as possible using the provided context, |
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and if the answer is not contained within the text below or in your knowledge, say 'I don't know'. Answer in steps if possible.""") |
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human_msg_template = HumanMessagePromptTemplate.from_template(template="{input}") |
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prompt_template = ChatPromptTemplate.from_messages([system_msg_template, MessagesPlaceholder(variable_name="history"), human_msg_template]) |
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conversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True) |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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for message in st.session_state.messages: |
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with st.chat_message(message["role"]): |
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st.markdown(message["content"]) |
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if prompt := st.chat_input("What is up?"): |
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st.session_state.messages.append({"role": "user", "content": prompt}) |
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with st.chat_message("user"): |
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st.markdown(prompt) |
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with st.chat_message("assistant"): |
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message_placeholder = st.empty() |
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query=st.session_state.messages[-1]["content"] |
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context = find_match(query) |
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full_response = "" |
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for response in conversation.predict(input=f"Context:'{context}' \n\n Query:'{query}'"): |
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full_response += response |
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message_placeholder.markdown(full_response + "▌") |
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message_placeholder.markdown(full_response) |
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st.session_state.messages.append({"role": "assistant", "content": full_response}) |
