from langchain_community.vectorstores import FAISS from langchain_community.embeddings import HuggingFaceEmbeddings from langchain.prompts import PromptTemplate from langchain_together import Together import os from langchain.memory import ConversationBufferWindowMemory from langchain.chains import ConversationalRetrievalChain import streamlit as st import time col1, col2, col3 = st.columns([1,4,1]) with col2: st.image("https://github.com/harshitv804/MedChat/assets/100853494/0aa18d7e-5305-4d8e-89d8-09fffce1589e") st.markdown( """ """, unsafe_allow_html=True, ) def reset_conversation(): st.session_state.messages = [] st.session_state.memory.clear() if "messages" not in st.session_state: st.session_state.messages = [] if "memory" not in st.session_state: st.session_state.memory = ConversationBufferWindowMemory(k=3, memory_key="chat_history",return_messages=True) embeddings = HuggingFaceEmbeddings(model_name="BAAI/llm-embedder") db = FAISS.load_local("fdb_pg1_a", embeddings) db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 3}) custom_prompt_template = """This is a chat tempalte and you are a medical practitioner lmm who provides correct medical information. Use the given following pieces of information to answer the user's question correctly. Utilize the provided knowledge base and search for relevant information. Follow the question format closely. The information should be abstract and concise. Understand all the context given here and generate only the answer. If you don't know the answer, just say that you don't know, don't try to make up an answer. CONTEXT: {context} CHAT HISTORY: {chat_history} QUESTION: {question} ANSWER """ prompt = PromptTemplate(template=custom_prompt_template, input_variables=['context', 'question', 'chat_history']) TOGETHER_AI_API= os.environ['TOGETHER_AI'] llm = Together( model="mistralai/Mistral-7B-Instruct-v0.2", temperature=0.7, max_tokens=1024, together_api_key=f"{TOGETHER_AI_API}" ) qa = ConversationalRetrievalChain.from_llm( llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt} ) for message in st.session_state.messages: with st.chat_message(message.get("role")): st.write(message.get("content")) input_prompt = st.chat_input("Say something") if input_prompt: with st.chat_message("user"): st.write(input_prompt) st.session_state.messages.append({"role":"user","content":input_prompt}) with st.chat_message("assistant"): with st.status("Thinking 💡...",expanded=True): result = qa.invoke(input=input_prompt) message_placeholder = st.empty() full_response = "⚠️ **_Note: Information provided may be inaccurate. Consult a qualified doctor for accurate advice._** \n\n\n" for chunk in result["answer"]: full_response+=chunk time.sleep(0.02) message_placeholder.markdown(full_response+" ▌") st.button('Reset All Chat 🗑️', on_click=reset_conversation) st.session_state.messages.append({"role":"assistant","content":result["answer"]})