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import os
import torch
import streamlit as st
from streamlit_chat import message
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.llms import HuggingFacePipeline
from langchain.document_loaders import PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from constants import CHROMA_SETTINGS
st.set_page_config(layout="centered")
checkpoint = "meta-llama/Llama-2-7b-chat-hf"
token = os.getenv("HF_TOKEN")
tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_auth_token=token)
model = AutoModelForCausalLM.from_pretrained(
checkpoint,
use_auth_token=token,
device_map="auto",
torch_dtype=torch.float32
)
@st.cache_resource
def data_ingestion(filepath):
loader = PDFMinerLoader(filepath)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
def embedding_function(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(model.device)
with torch.no_grad():
embeddings = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
return embeddings
db = Chroma.from_documents(texts, embedding_function=embedding_function, persist_directory="db")
db.persist()
db = None
@st.cache_resource
def llm_pipeline():
pipe = pipeline(
'text-generation',
model=model,
tokenizer=tokenizer,
max_length=256,
do_sample=True,
temperature=0.3,
top_p=0.95
)
local_llm = HuggingFacePipeline(pipeline=pipe)
return local_llm
@st.cache_resource
def qa_llm():
llm = llm_pipeline()
def embedding_function(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(model.device)
with torch.no_grad():
embeddings = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
return embeddings
db = Chroma(persist_directory="db", embedding_function=embedding_function)
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):
qa = qa_llm()
generated_text = qa(instruction)
answer = generated_text['result']
return answer
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.markdown("<h1 style='text-align: center;'>Chat with your PDF</h1>", unsafe_allow_html=True)
st.markdown("<h2 style='text-align: center;'>Upload your PDF</h2>", unsafe_allow_html=True)
uploaded_file = st.file_uploader("", type=["pdf"])
if uploaded_file is not None:
filepath = "docs/" + uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
with st.spinner('Embeddings are creating...'):
data_ingestion(filepath)
st.success('Embeddings are created successfully!')
user_input = st.text_input("", key="input")
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 user_input:
answer = process_answer({'query': user_input})
st.session_state["past"].append(user_input)
st.session_state["generated"].append(answer)
display_conversation(st.session_state)
if __name__ == "__main__":
main()
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