Spaces:
Running
Running
File size: 2,368 Bytes
fe5256f 060fa1e fe5256f |
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 |
# import required libraries
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.vectorstores import Chroma
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
#from langchain.text_splitter import NLTKTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
import streamlit as st
import sys,yaml,Utilities as ut
def get_data(query):
chat_history = []
initdict={}
initdict = ut.get_tokens()
hf_token = initdict["hf_token"]
embedding_model_id = initdict["embedding_model"]
chromadbpath = initdict["chatPDF_chroma_db"]
llm_repo_id = initdict["llm_repoid"]
# We will use HuggingFace embeddings
embeddings = HuggingFaceEmbeddings(model_name=embedding_model_id)
#retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 1})
# load from disk
db = Chroma(persist_directory=chromadbpath, embedding_function=embeddings)
retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 2})
"""llm = HuggingFaceHub(huggingfacehub_api_token=hf_token,
repo_id=llm_repo_id, model_kwargs={"temperature":0.2, "max_new_tokens":50})"""
llm = HuggingFaceHub(repo_id=llm_repo_id, model_kwargs={"temperature":0.2, "max_new_tokens":50})
# Create the Conversational Retrieval Chain
qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever,return_source_documents=True)
result = qa_chain({'question': query, 'chat_history': chat_history})
chat_history.append(result)
print('Answer: ' + result['answer'] + '\n')
print (result)
return result['answer']
st.title("PatentGuru Document Reader")
# Main chat form
with st.form("chat_form"):
query = st.text_input("Chat with PDF: ")
clear_history = st.checkbox('Clear Chat History')
submit_button = st.form_submit_button("Send")
if submit_button:
if clear_history:
st.write("Cleared previous chat history")
response = get_data(query)
if len(response)>0:
response = str(response.partition("Answer: ")[-1])
else: response = "No results"
# write results
st.write (response)
|