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# importing necessary libraries
import os
import time
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from docx import Document
from docx.text.paragraph import Paragraph
from docx.table import Table
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
# load the environment variables into the python script
load_dotenv()
# fetching the openai_api_key environment variable
openai_api_key = os.getenv("OPENAI_API_KEY")
# Initialize session states
if "vectorDB" not in st.session_state:
st.session_state.vectorDB = None
if "messages" not in st.session_state:
st.session_state.messages = []
if "bot_name" not in st.session_state:
st.session_state.bot_name = ""
if "chain" not in st.session_state:
st.session_state.chain = None
def process_paragraph(paragraph):
"""This Function returns the content of the paragraph present inside the DOC file"""
return paragraph.text
def process_table(table):
"""This function extracts the content from the table present inside the DOC file"""
text = ""
for row in table.rows:
for cell in row.cells:
text += cell.text
return text
def read_docx(file_path):
"""This function extracts the text from the DOC file"""
doc = Document(file_path)
text = []
for element in doc.iter_inner_content():
if isinstance(element, Paragraph):
text.append(process_paragraph(element))
elif isinstance(element, Table):
text.append(process_table(element))
return " ".join(text)
def read_text_file(text_file):
"""This function extracts the text from the TEXT file"""
try:
text = text_file.read().decode("utf-8")
return text
except Exception as e:
st.error(f"Error while reading {text_file.name} file : **{e}**")
return None
def get_pdf_text(pdf):
"""This function extracts the text from the PDF file"""
try:
text = []
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text.append(page.extract_text())
return " ".join(text)
except Exception as e:
st.error(f"Error while reading {pdf.name} file : **{e}**")
return None
def get_vectorstore(text_chunks):
"""This function will create a vector database as well as create & store the embedding of the text chunks into the VectorDB"""
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_text_chunks(text: str):
"""This function will split the text into the smaller chunks"""
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=50,
length_function=len,
is_separator_regex=False,
)
chunks = text_splitter.split_text(text)
return chunks
def processing(files):
"""This function"""
data = []
for file in files:
if file.name.endswith(".docx"):
text = read_docx(file)
elif file.name.endswith(".pdf"):
text = get_pdf_text(file)
else:
text = read_text_file(file)
data.append(text)
raw_text = " ".join(data)
# divinding the raw text into smaller chunks
text_chunks = get_text_chunks(raw_text)
# Creating and storing the chunks in vector database
vectorDB = get_vectorstore(text_chunks)
return vectorDB
def get_response(query: str):
"""This function will return the output of the user query!"""
# getting the context from the database that is similar to the user query
query_context = st.session_state.vectorDB.similarity_search(query=query)
# calling the chain to get the output from the LLM
response = st.session_state.chain.invoke(
{
"human_input": query,
"context": query_context[0].page_content,
"name": st.session_state.bot_name,
}
)["text"]
# Iterate through each word in the 'response' string after splitting it based on whitespace
for word in response.split():
# Yield the current word followed by a space, effectively creating a generator
yield word + " "
# Pause execution for 0.05 seconds (50 milliseconds) to introduce a delay
time.sleep(0.05)
def get_conversation_chain(vectorDB):
"""This function will create and return a LLM-Chain"""
# using OPENAI ChatModel
llm = ChatOpenAI(temperature=0.1, model="gpt-3.5-turbo-16k")
# creating a template to pass into LLM
template = """You are a friendly customer support ChatBot with a name: {name} for the company, aiming to enhance the customer experience by providing tailored assistance and information.
Answer the question as detailed as possible and to the point from the context: {context}\n\n.
If the answer is not in the provided context then only just say, "answer is not available in the context", do not provide the wrong answer\n\n
{chat_history}
Human: {human_input}
AI: """
# creating a prompt that is used to format the input of the user
prompt = PromptTemplate(
template=template,
input_variables=["chat_history", "human_input", "name", "context"],
)
# creating a memory that will store the chat history between chatbot and user
memory = ConversationBufferWindowMemory(
memory_key="chat_history", input_key="human_input", k=5
)
chain = LLMChain(llm=llm, prompt=prompt, memory=memory, verbose=True)
return chain
if __name__ == "__main__":
# setting the config of WebPage
st.set_page_config(page_title="Personalized ChatBot", page_icon="🤖")
st.header("Personalized Customer Support Chatbot 🤖", divider="rainbow")
# taking input( bot name and pdf file) from the user
with st.sidebar:
st.caption("Please enter the **Bot Name** and Upload **PDF** File!")
bot_name = st.text_input(
label="Bot Name", placeholder="Enter the bot name here....", key="bot_name"
)
files = st.file_uploader(
label="Upload Files!",
type=["pdf", "txt", "docx"],
accept_multiple_files=True,
)
# moving forward only when both the inputs are given by the user
if files and bot_name:
# the Process File button will process the pdf file and save the chunks into the vector database
if st.button("Process File"):
# if there is existing chat history we will delete it
if st.session_state.messages != []:
st.session_state.messages = []
with st.spinner("Processing....."):
st.session_state["vectorDB"] = processing(files)
st.session_state["chain"] = get_conversation_chain(
st.session_state["vectorDB"]
)
st.success("File Processed", icon="✅")
# if the vector database is ready to use then only show the chatbot interface
if st.session_state.vectorDB:
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# taking the input i.e. query from the user (walrus operator)
if prompt := st.chat_input(f"Message {st.session_state.bot_name}"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.write(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
response = st.write_stream(get_response(prompt))
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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