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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
import torch
import base64
import textwrap
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain.chains import RetrievalQA
from streamlit_chat import message
from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
import os
st.set_page_config(page_title="pdf-GPT", page_icon="📖", layout="wide")
@st.cache_resource
def get_model():
device = torch.device('cpu')
# device = torch.device('cuda:0')
checkpoint = "LaMini-T5-738M"
checkpoint = "MBZUAI/LaMini-T5-738M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
base_model = AutoModelForSeq2SeqLM.from_pretrained(
checkpoint,
device_map=device,
torch_dtype = torch.float32,
# offload_folder= "/model_ck"
)
return base_model,tokenizer
@st.cache_resource
def llm_pipeline():
base_model,tokenizer = get_model()
pipe = pipeline(
'text2text-generation',
model = base_model,
tokenizer=tokenizer,
max_length = 512,
do_sample = True,
temperature = 0.3,
top_p = 0.95,
# device=device
)
local_llm = HuggingFacePipeline(pipeline = pipe)
return local_llm
@st.cache_resource
def qa_llm():
llm = llm_pipeline()
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = Chroma(persist_directory="db", embedding_function = embeddings)
retriever = db.as_retriever()
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type = "stuff",
retriever = retriever,
return_source_documents=True
)
return qa
def process_answer(instruction):
response=''
instruction = instruction
qa = qa_llm()
generated_text = qa(instruction)
answer = generated_text['result']
return answer, generated_text
# Display conversation history using Streamlit messages
def display_conversation(history):
# st.write(history)
for i in range(len(history["generated"])):
message(history["past"][i] , is_user=True, key= str(i) + "_user")
if isinstance(history["generated"][i],str):
message(history["generated"][i] , key= str(i))
else:
message(history["generated"][i][0] , key= str(i))
# sources_list = []
# for source in history["generated"][i][1]['source_documents']:
# # st.write(source.metadata['source'])
# sources_list.append(source.metadata['source'])
# message(str(set(sources_list)) , key="sources_"+str(i))
# function to display the PDF of a given file
@st.cache_data
def displayPDF(file,file_name):
# Opening file from file path
with open(file, "rb") as f:
base64_pdf = base64.b64encode(f.read()).decode('utf-8')
# Embedding PDF in HTML
pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="700" height="900" type="application/pdf"></iframe>'
# pdf_display = f'<iframe src="{file}" width="700" height="900" type="application/pdf"></iframe>'
# st.write()
# pdf_display = f'<embed src="http://localhost:8900/{file_name}" width="700" height="1000" type="application/pdf"></embed>'
# pdf_display = f'<iframe src="http://localhost:8900/{file_name}" width="700" height="900" type="application/pdf"></iframe>'
# st.write(pdf_display)
st.markdown(pdf_display, unsafe_allow_html=True)
@st.cache_resource
def data_ingestion(file_path,persist_directory):
# for root, dirs, files in os.walk("docs"):
# for file in files:
if file_path.endswith(".pdf"):
print(file_path)
loader = PDFMinerLoader(file_path)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
texts = text_splitter.split_documents(documents)
# create embeddings
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# create vector store
db = Chroma.from_documents(texts, embeddings, persist_directory="uploaded/db")
db.persist()
db=None
def main():
st.markdown("<h1 style='text-align:center; color: blue;'>Chat with Your PDF 📑</h1>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align:center; color: grey;'>Built by Vicky</h3>", unsafe_allow_html=True)
st.markdown("<h2 style='text-align:center; color: red;'>Upload your PDF</h2>", unsafe_allow_html=True)
uploaded_file = st.file_uploader("",type=["pdf"])
if uploaded_file is not None:
file_details = {
"name" : uploaded_file.name,
"type" : uploaded_file.type,
"size" : uploaded_file.size
}
print(os.getcwd())
# st.write(os.getcwd())
cwd = os.getcwd()
# st.write(os.listdir(cwd))
filepath = cwd+"/uploaded/"+uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
col1, col2 = st.columns([1,1])
with col1:
# st.markdown("<h2 style='text-align:center; color:grey;'>PDF Details</h2>",unsafe_allow_html=True)
# st.write(file_details)
st.markdown("<h2 style='text-align:center; color: grey;'>PDF Preview</h2>", unsafe_allow_html=True)
displayPDF(filepath,uploaded_file.name)
# displayPDF(uploaded_file)
with col2:
with st.spinner("Embeddings are in process......."):
ingested_data = data_ingestion(filepath,filepath)
st.success('Embeddings are created Successfully!')
st.markdown("<h2 style='text-align:center; color: grey;'>Chat Here</h2>", unsafe_allow_html=True)
user_input = st.text_input(label="Message",key="input")
# user_input = st.chat_input("",key="input")
# styl = f"""
# <style>
# .stTextInput {{
# position: fixed;
# bottom: 3rem;
# }}
# </style>
# """
# st.markdown(styl, unsafe_allow_html=True)
# Initialize session state for generated responses and past messages
if "generated" not in st.session_state:
st.session_state["generated"] = ["I am ready to help you"]
if "past" not in st.session_state:
st.session_state["past"] = ["Hey There!"]
# Search the database for a response based on user input and update session state
if user_input:
answer = process_answer({"query" : user_input})
# answer = user_input
st.session_state["past"].append(user_input)
response = answer
st.session_state["generated"].append(response)
# st.write(st.session_state)
# user_input = st.text_input(label="Message",key="input")
# Display Conversation history using Streamlit messages
if st.session_state["generated"]:
display_conversation(st.session_state)
if __name__ == "__main__":
main()
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