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import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import pipeline
import torch
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFacePipeline
#from constants import CHROMA_SETTINGS
from streamlit_chat import message
import safetensors
checkpoint = "LaMini-Flan-T5-77M"
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
base_model = T5ForConditionalGeneration.from_pretrained(
checkpoint,
device_map = 'auto',
torch_dtype = torch.float32,
)
@st.cache_resource
def llm_pipeline():
pipe = pipeline(
'text2text-generation',
model = base_model,
tokenizer = tokenizer,
temperature = 0.5
)
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 = FAISS.load_local("vector_data",embeddings)
#db = Chroma(persist_directory="db", embedding_function = embeddings, client_settings=CHROMA_SETTINGS)
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
# Display conversation history using Streamlit messages
def display_conversation(history):
for i in range(len(history["generated"])):
message(history["past"][i], is_user=True, key=str(i) + "_user")
message(history["generated"][i],key=str(i))
def main():
st.title('Chat with Your Data 🦜📄')
with st.expander("About the Chatbot"):
st.markdown(
"""
This is a Generative AI powered Chatbot that interacts with you and you can ask followup questions.
"""
)
user_input = st.text_input("Question:", placeholder="Ask about your PDF", key='input')
with st.form(key='my_form', clear_on_submit=True):
submit_button = st.form_submit_button(label='Send')
# 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!👋"]
if submit_button and user_input or user_input :
st.session_state['past'].append(user_input)
with st.spinner('Generating response...'):
answer = process_answer({'query': user_input})
st.session_state['generated'].append(answer)
if st.session_state["generated"]:
display_conversation(st.session_state)
if __name__ == '__main__':
main() |