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from dotenv import load_dotenv
import streamlit as st
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_text_splitters.character import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_groq import ChatGroq
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import os
import nltk
nltk.download('punkt_tab')
nltk.download('averaged_perceptron_tagger_eng')
# Install Poppler and Tesseract in the runtime environment
os.system("apt-get update && apt-get install -y poppler-utils tesseract-ocr")
secret = os.getenv('GROQ_API_KEY')
working_dir = os.path.dirname(os.path.abspath(__file__))
def load_documents(file_path):
# Specify poppler_path and tesseract_path to ensure compatibility
loader = UnstructuredPDFLoader(
file_path,
poppler_path="/usr/bin",
tesseract_path="/usr/bin/tesseract"
)
documents = loader.load()
return documents
def setup_vectorstore(documents):
embeddings = HuggingFaceEmbeddings()
text_splitter = CharacterTextSplitter(
separator="/n",
chunk_size=1000,
chunk_overlap=200
)
doc_chunks = text_splitter.split_documents(documents)
vectorstores = FAISS.from_documents(doc_chunks, embeddings)
return vectorstores
def create_chain(vectorstores):
llm = ChatGroq(
api_key=secret,
model="deepseek-r1-distill-llama-70b",
temperature=0
)
retriever = vectorstores.as_retriever()
memory = ConversationBufferMemory(
llm=llm,
output_key="answer",
memory_key="chat_history",
return_messages=True
)
chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True
)
return chain
# Streamlit page configuration
st.set_page_config(
page_title="Chat with your documents",
page_icon="📑",
layout="centered"
)
st.title("📝Chat With your docs 😎")
# Initialize session states
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
uploaded_file = st.file_uploader(label="Upload your PDF")
if uploaded_file:
file_path = f"{working_dir}/{uploaded_file.name}"
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
if "vectorstores" not in st.session_state:
st.session_state.vectorstores = setup_vectorstore(load_documents(file_path))
if "conversation_chain" not in st.session_state:
st.session_state.conversation_chain = create_chain(st.session_state.vectorstores)
# Display chat history
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# User input handling
user_input = st.chat_input("Ask any questions relevant to uploaded pdf")
if user_input:
st.session_state.chat_history.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.markdown(user_input)
with st.chat_message("assistant"):
response = st.session_state.conversation_chain({"question": user_input})
assistant_response = response["answer"]
st.markdown(assistant_response)
st.session_state.chat_history.append({"role": "assistant", "content": assistant_response}) |