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from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
from htmlTemplates import css, bot_template, user_template
from dotenv import load_dotenv
from PyPDF2 import PdfReader
import streamlit as st
import requests
import json
import os
# set this key as an environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
# Page configuration
st.set_page_config(page_title="semaNaPDF", page_icon="📚", layout="wide",)
# Sema Translator
#Public_Url = os.environ["sema_url"] #endpoint
def translate(userinput, target_lang, source_lang=None):
if source_lang:
url = "Public_Url/translate_enter/"
data = {
"userinput": userinput,
"source_lang": source_lang,
"target_lang": target_lang,
}
response = requests.post(url, json=data)
result = response.json()
print(type(result))
source_lange = source_lang
translation = result['translated_text']
return source_lange, translation
else:
url = "Public_Url/translate_detect/"
data = {
"userinput": userinput,
"target_lang": target_lang,
}
response = requests.post(url, json=data)
result = response.json()
source_lange = result['source_language']
translation = result['translated_text']
return source_lange, translation
def get_pdf_text(pdf_docs : list) -> str:
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text:str) ->list:
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks : list) -> FAISS:
model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
encode_kwargs = {
"normalize_embeddings": True
} # set True to compute cosine similarity
embeddings = HuggingFaceBgeEmbeddings(
model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
)
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
llm = HuggingFaceHub(
repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
#repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF"
model_kwargs={"temperature": 0.5, "max_length": 1048},
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm, retriever=vectorstore.as_retriever(), memory=memory
)
return conversation_chain
def main():
st.title("SemaNaPDF📚")
# upload file
pdf = st.file_uploader("Upload a PDF Document and ask questions to get insights", type="pdf")
with st.spinner("Processing"):
# get pdf text
if pdf is not None:
raw_text = get_pdf_text(pdf_docs)
# get the text chunks
text_chunks = get_text_chunks(raw_text)
# create vector store
vectorstore = get_vectorstore(text_chunks)
# create conversation chain
st.session_state.conversation = get_conversation_chain(vectorstore)
st.info("done")
#user_question = st.text_input("Get insights into your finances ...")
# show user input
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if user_question := st.chat_input("Ask your document anything ......?"):
with st.chat_message("user"):
st.markdown(user_question)
st.session_state.messages.append({"role": "user", "content": user_question})
response = st.session_state.conversation({"question": user_question})
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == '__main__':
main()
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