DocChatAI / app.py
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import streamlit as st
import os
from langchain_community.document_loaders import PDFMinerLoader
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFacePipeline
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
import torch
st.title("DocChatAI | Chat over PDF Doc")
# Custom CSS for chat messages
st.markdown("""
<style>
.user-message {
text-align: right;
background-color: #3c8ce7;
color: white;
padding: 10px;
border-radius: 10px;
margin-bottom: 10px;
display: inline-block;
width: fit-content;
max-width: 70%;
margin-left: auto;
box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
}
.assistant-message {
text-align: left;
background-color: #d16ba5;
color: white;
padding: 10px;
border-radius: 10px;
margin-bottom: 10px;
display: inline-block;
width: fit-content;
max-width: 70%;
margin-right: auto;
box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
}
</style>
""", unsafe_allow_html=True)
def get_file_size(file):
file.seek(0, os.SEEK_END)
file_size = file.tell()
file.seek(0)
return file_size
# Add a sidebar for model selection and user details
st.sidebar.write("Settings")
st.sidebar.write("-----------")
model_options = ["MBZUAI/LaMini-T5-738M", "google/flan-t5-base", "google/flan-t5-small"]
selected_model = st.sidebar.radio("Choose Model", model_options)
st.sidebar.write("-----------")
uploaded_file = st.sidebar.file_uploader("Upload file", type=["pdf"])
st.sidebar.write("-----------")
st.sidebar.write("About Me")
st.sidebar.write("Name: Deepak Yadav")
st.sidebar.write("Bio: Passionate about AI and machine learning. Enjoys working on innovative projects and sharing knowledge with the community.")
st.sidebar.write("[GitHub](https://github.com/deepak7376)")
st.sidebar.write("[LinkedIn](https://www.linkedin.com/in/dky7376/)")
st.sidebar.write("-----------")
@st.cache_resource
def initialize_qa_chain(filepath, CHECKPOINT):
loader = PDFMinerLoader(filepath)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
splits = text_splitter.split_documents(documents)
# Create embeddings
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = FAISS.from_documents(splits, embeddings)
# Initialize model
TOKENIZER = AutoTokenizer.from_pretrained(CHECKPOINT)
BASE_MODEL = AutoModelForSeq2SeqLM.from_pretrained(CHECKPOINT, device_map=torch.device('cpu'), torch_dtype=torch.float32)
pipe = pipeline(
'text2text-generation',
model=BASE_MODEL,
tokenizer=TOKENIZER,
max_length=256,
do_sample=True,
temperature=0.3,
top_p=0.95,
)
llm = HuggingFacePipeline(pipeline=pipe)
# Build a QA chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectordb.as_retriever(),
)
return qa_chain
def process_answer(instruction, qa_chain):
generated_text = qa_chain.run(instruction)
return generated_text
if uploaded_file is not None:
os.makedirs("docs", exist_ok=True)
filepath = os.path.join("docs", uploaded_file.name)
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
temp_filepath = temp_file.name
with st.spinner('Embeddings are in process...'):
qa_chain = initialize_qa_chain(temp_filepath, selected_model)
else:
qa_chain = None
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
if message["role"] == "user":
st.markdown(f"<div class='user-message'>{message['content']}</div>", unsafe_allow_html=True)
else:
st.markdown(f"<div class='assistant-message'>{message['content']}</div>", unsafe_allow_html=True)
# React to user input
if prompt := st.chat_input("What is up?"):
# Display user message in chat message container
st.markdown(f"<div class='user-message'>{prompt}</div>", unsafe_allow_html=True)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
if qa_chain:
# Generate response
response = process_answer({'query': prompt}, qa_chain)
else:
# Prompt to upload a file
response = "Please upload a PDF file to enable the chatbot."
# Display assistant response in chat message container
st.markdown(f"<div class='assistant-message'>{response}</div>", unsafe_allow_html=True)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})