File size: 4,681 Bytes
e762464 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
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="📚",)
# 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_docs = st.file_uploader(
"Upload your PDFs here and click on 'Process'", accept_multiple_files=True
)
if pdf_docs is not None:
with st.spinner("processing"):
# get pdf text
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})
st.session_state.chat_history = response["chat_history"]
with st.chat_message("assistant"):
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == '__main__':
main()
|