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import streamlit as st
from langchain.text_splitter import CharacterTextSplitter
from langchain.docstore.document import Document
from langchain.chains.summarize import load_summarize_chain
# from langchain_community.llms import CTransformers
# from langchain_community.llms import HuggingFaceHub
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from pypdf import PdfReader
from langchain_community.llms import LlamaCpp
HUGGINGFACEHUB_API_TOKEN = st.secrets["HUGGINGFACEHUB_API_TOKEN"]
# Page title
st.set_page_config(page_title='🦜🔗 Text Summarization App')
st.title('🦜🔗 Text Summarization App')
# Function to read all PDF files and return text
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
# Function to split the text into smaller chunks and convert it into document format
def chunks_and_document(txt):
text_splitter = CharacterTextSplitter()
texts = text_splitter.split_text(txt)
docs = [Document(page_content=t) for t in texts]
return docs
# Loading the Llama 2's LLM
def load_llm():
# We instantiate the callback with a streaming stdout handler
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
# Loading the LLM model
# llm = CTransformers(
# model="llama-2-7b-chat.ggmlv3.q2_K.bin",
# model_type="llama",
# config={'max_new_tokens': 600,
# 'temperature': 0.5,
# 'context_length': 700}
# )
# llm = HuggingFaceHub(
# repo_id="meta-llama/Llama-2-7b-chat-hf", # Official Llama-2 model
# model_kwargs={"temperature": 0.5, "max_length": 500}
# )
llm = LlamaCpp(
model_path="TheBloke/Llama-2-7B-GGUF/llama-2-7b.Q8_0.gguf", # Path to your downloaded model
temperature=0.5,
max_tokens=500,
n_ctx=1024
)
return llm
# Function to apply the LLM model with our document
def chains_and_response(docs):
llm = load_llm()
chain = load_summarize_chain(llm, chain_type='map_reduce')
return chain.invoke(docs)
def main():
# Initialize messages if not already present
if "messages" not in st.session_state.keys():
st.session_state.messages = []
# Sidebar for uploading PDF files
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader(
"Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True
)
if st.button("Submit & Process"):
with st.spinner("Processing..."):
txt_input = get_pdf_text(pdf_docs)
docs = chunks_and_document(txt_input)
response = chains_and_response(docs)
st.title('📝✅ Summarization Result')
for res in response:
st.info(res)
main()
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