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| import streamlit as st | |
| from streamlit_chat import message | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.document_loaders import DirectoryLoader | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.llms import CTransformers | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import FAISS | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| st.set_page_config( | |
| page_title="SuJokEase", | |
| page_icon="🩺", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| ) | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| loader = CSVLoader(file_path='data.csv') | |
| documents = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=500,chunk_overlap=50) | |
| text_chunks = text_splitter.split_documents(documents) | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={'device':"cpu"}) | |
| vector_store = FAISS.from_documents(text_chunks,embeddings) | |
| llm = CTransformers(model="model.bin",model_type="llama", | |
| config={'max_new_tokens':128,'temperature':0.01}) | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| chain = ConversationalRetrievalChain.from_llm(llm=llm,chain_type='stuff', | |
| retriever=vector_store.as_retriever(search_kwargs={"k":2}), | |
| memory=memory) | |
| # Sidebar for user input | |
| st.sidebar.title("SuJokEase🩺") | |
| st.sidebar.info("A Conversational Retrieval Chain Chat-Bot for Sujok Therapy Points Conversational Retrieval Chain Chat-Bot for Sujok Therapy weather you are Students, Working Professional or in situations where you need instant relaxation. We are at your Back! ") | |
| github_link = "[GitHub]()" | |
| st.sidebar.info("To contribute and Sponser - " + github_link) | |
| st.title("A Conversational Retrieval Chain Chat-Bot for Sujok Therapy Points🩺") | |
| st.text("Your wellness companion for instant relaxation.") | |
| def conversation_chat(query): | |
| result = chain({"question": query, "chat_history": st.session_state['history']}) | |
| st.session_state['history'].append((query, result["answer"])) | |
| return result["answer"] | |
| def initialize_session_state(): | |
| if 'history' not in st.session_state: | |
| st.session_state['history'] = [] | |
| if 'generated' not in st.session_state: | |
| st.session_state['generated'] = ["Hello! Ask me anything about Pains"] | |
| if 'past' not in st.session_state: | |
| st.session_state['past'] = ["Hello!"] | |
| def display_chat_history(): | |
| reply_container = st.container() | |
| container = st.container() | |
| with container: | |
| with st.form(key='my_form', clear_on_submit=True): | |
| user_input = st.text_input("Question:", placeholder="Ask anything about Sujok Thearpy", key='input') | |
| submit_button = st.form_submit_button(label='Send') | |
| if submit_button and user_input: | |
| output = conversation_chat(user_input) | |
| st.session_state['past'].append(user_input) | |
| st.session_state['generated'].append(output) | |
| if st.session_state['generated']: | |
| with reply_container: | |
| for i in range(len(st.session_state['generated'])): | |
| message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="robot") | |
| message(st.session_state["generated"][i], key=str(i), avatar_style="superhero") | |
| # Initialize session state | |
| initialize_session_state() | |
| # Display chat history | |
| display_chat_history() | |