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import streamlit as st
import os
# import openai
from io import StringIO
from langchain.chat_models import ChatOpenAI
from langchain import OpenAI, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
# from langchain.chains import ConversationalRetrievalChain
from langchain.chains.summarize import load_summarize_chain
import tempfile
if "file_uploader_key" not in st.session_state:
st.session_state["file_uploader_key"] = 0
if "uploaded_files" not in st.session_state:
st.session_state["uploaded_files"] = []
# Prompt Template
template = """You are a chatbot having a conversation with a human.
Given the following extracted parts of a long document and a question, create a final answer.
{context}
{chat_history}
Human: {human_input}
Chatbot:"""
# Init Prompt
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"], template=template
)
a = st.container()
with a:
st.title("CHATBOT")
global openai_api_key
openai_api_key = st.text_input('OpenAI API Key', type='password')
if openai_api_key:
@st.cache_resource
def llm():
model = OpenAI(temperature=0.0, openai_api_key=openai_api_key)
embedding=OpenAIEmbeddings(openai_api_key=openai_api_key)
return model, embedding
llm,embedding = llm()
@st.cache_resource
def chain():
global memory
memory = ConversationBufferWindowMemory(memory_key="chat_history", input_key="human_input", return_messages=True, k=3)
chain = LLMChain(
llm=llm, prompt=prompt, memory=memory
)
return chain
global llm_chain
llm_chain = chain()
summarize_template = """Write a concise summary of the given documents:
{text}"""
summarize_PROMPT = PromptTemplate(template=summarize_template, input_variables=["text"])
llm_summarize = load_summarize_chain(llm=llm, chain_type="map_reduce", map_prompt=summarize_PROMPT)
# chain({"input_documents": docs}, return_only_outputs=True)
# llm_summarize = load_summarize_chain(llm, chain_type="map_reduce")
########################################
####### CHATBOT interface#############
########################################
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
with a:
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
global documents
documents = []
with st.sidebar:
uploaded_files = st.file_uploader("Upload file", accept_multiple_files=True,
key=st.session_state["file_uploader_key"],
type=['txt', 'pdf']
# on_change = check
)
if uploaded_files:
# files = set([file.name for file in uploaded_files])
st.session_state["uploaded_files"] = uploaded_files
text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000 , chunk_overlap=10, separators=[" ", ",", "\n"])
for file in uploaded_files:
if file.name.endswith(".pdf"):
# Save the uploaded file to a temporary location
temp_file_path = os.path.join('docs', file.name)
with open(temp_file_path, "wb") as temp_file:
temp_file.write(file.read())
loader = PyPDFLoader(temp_file_path)
# loader = loader.load()
elif file.name.endswith('.txt'):
# To read file as bytes:
bytes_data = file.getvalue()
# To convert to a string based IO:
stringio = StringIO(file.getvalue().decode("utf-8"))
# To read file as string:
loader = stringio.read()
filename = os.path.join("docs",'text.txt')
# filename = 'docs/text.txt'
with open(filename,"wb") as f:
f.write(file.getbuffer())
loader = TextLoader(filename, autodetect_encoding=True)
loader = loader.load()
documents.extend(loader)
documents = text_splitter.split_documents(documents)
# Embedding
global docsearch
docsearch = Chroma.from_documents(documents,
embedding=embedding)
########################################
########## SIDEBAR ###############
########################################
# create a function that sets the value in state back to an empty list
def clear_msg():
st.session_state.messages = []
llm_chain = chain()
st.session_state["file_uploader_key"] += 1
st.experimental_rerun()
if uploaded_files:
if st.sidebar.button('Summarize'):
with a:
query = 'Summarize uploaded documents'
st.chat_message("user").markdown(query)
llm_chain.memory.chat_memory.add_user_message(query)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": query})
response = llm_summarize.run(documents)
# chain({"input_documents": docs}, return_only_outputs=True)
with st.chat_message("assistant"):
st.markdown(response)
llm_chain.memory.chat_memory.add_ai_message(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
st.sidebar.button("Clear", on_click=clear_msg)
########################################
####### React to user input#############
########################################
with a:
if query := st.chat_input():
# Display user message in chat message container
st.chat_message("user").markdown(query)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": query})
if documents:
docs = docsearch.similarity_search(query)
else:
docs = 'No Context provide'
response = llm_chain.run({"context": docs, "human_input": query})
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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