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import streamlit as st
import urllib.request
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
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
urllib.request.urlretrieve("https://huggingface.co/TheBloke/Llama-2-7B-GGML/resolve/main/llama-2-7b.ggmlv3.q2_K.bin","llama-2-7b-chat.ggmlv3.q2_K.bin")
loader = DirectoryLoader('data/',glob="*.pdf",loader_cls=PyPDFLoader)
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"})
#vectorstore
vector_store = FAISS.from_documents(text_chunks,embeddings)
#create llm
llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.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)
st.title("HealthCare ChatBot 🧑🏽⚕️")
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 🤗"]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hey! 👋"]
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 about your Mental Health", 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="thumbs")
message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
# Initialize session state
initialize_session_state()
# Display chat history
display_chat_history() |