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from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.storage import LocalFileStore
from langchain.embeddings import CacheBackedEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
import streamlit as st
import os
import shutil
from dotenv import load_dotenv
from streamlit_chat import message
load_dotenv()
os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API')
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = os.getenv('LANGSMITH_API')
UPLOAD_DIR = "uploaded_files"
def cleanup_files():
if os.path.isdir(UPLOAD_DIR):
shutil.rmtree(UPLOAD_DIR, ignore_errors=True)
if 'file_handle' in st.session_state:
st.session_state.file_handle.close()
if 'cleanup_done' not in st.session_state:
st.session_state.cleanup_done = False
if not st.session_state.cleanup_done:
cleanup_files()
if not os.path.exists(UPLOAD_DIR):
os.makedirs(UPLOAD_DIR)
st.title("Chat with Your PDF!!")
uploaded_file = st.file_uploader("Upload a file")
if uploaded_file is not None:
file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)
file_path = os.path.abspath(file_path)
with open(file_path, 'wb') as f:
f.write(uploaded_file.getbuffer())
st.write("You're Ready For a Chat with your PDF")
docs = PyPDFLoader(file_path).load_and_split()
embedding = HuggingFaceBgeEmbeddings(
model_name='BAAI/llm-embedder',
)
store = LocalFileStore("./cache/")
cached_embedder = CacheBackedEmbeddings.from_bytes_store(
embedding, store, namespace='embeddings'
)
vector_base = FAISS.from_documents(
docs,
embedding
)
template = '''You are an Experienced Business Person Having a
great Knowledge About Various Types of Business Activities
but here you are hired to Give the answers to the {question} only based on {context}
.if you are unaware of the question Just reply it with I\'m Unaware of your Query.
Use three sentences maximum and keep the answer concise.'''
prompt = ChatPromptTemplate.from_template(template)
retriever = vector_base.as_retriever()
llm = ChatGroq(
model='mixtral-8x7b-32768',
temperature=0,
)
if 'history' not in st.session_state:
st.session_state.history = []
query = st.text_input("Enter your question")
if st.button("Submit !"):
if query:
chain = (
{'context': retriever, 'question': RunnablePassthrough()}
| prompt | llm | StrOutputParser()
)
answer = chain.invoke(query)
st.session_state.history.append({'question': query, 'answer': answer})
if st.session_state.history:
st.write("### Previous Questions and Answers")
for idx, entry in enumerate(st.session_state.history):
message(f"**Q{idx + 1}:** {entry['question']}", is_table=True, key=f"question_{idx}",avatar_style='no-avatar')
message(f"**A{idx + 1}:** {entry['answer']}", is_table=True, key=f"answer_{idx}",avatar_style='no-avatar')
if st.session_state.cleanup_done:
cleanup_files()
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