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import streamlit as st
import random
from langchain_components.replier import get_context_from_vectorstore,get_vectorstore_from_postgres,prepare_prompt_and_chain_with_history,get_vectorstore_from_pinecone
import fitz
def display_pdf(pdf_path):
try:
pdf_document = fitz.open(pdf_path)
num_pages = pdf_document.page_count
st.sidebar.write(f"Total pages: {num_pages}")
for page_num in range(num_pages):
page = pdf_document.load_page(page_num)
image = page.get_pixmap()
st.sidebar.image(image.tobytes(), caption=f"Page {page_num + 1}", use_column_width=True)
except Exception as e:
st.sidebar.error(f"Error loading PDF: {e}")
def main():
st.header('Interact with your PDF that includes images, tables, and graphs.')
if "activate_chat" not in st.session_state:
st.session_state.activate_chat = False
if "messages" not in st.session_state:
st.session_state.messages = []
with st.sidebar:
username = st.text_input("Please enter your name here")
if st.button('Press Button to Start chat with Narendra AI'):
if "user_id" not in st.session_state:
st.session_state.user_id = username
if "session_id" not in st.session_state:
random_number = random.randint(1, 1000000)
st.session_state.session_id = str(random_number)
if "vectorstore" not in st.session_state:
collection_name="fy2024_chunk_2000"
pinecone_collection_name="fy2024"
#st.session_state.vectorstore = get_vectorstore_from_postgres(collection_name)
st.session_state.vectorstore = get_vectorstore_from_pinecone(pinecone_collection_name)
if "chain" not in st.session_state:
st.session_state.chain = prepare_prompt_and_chain_with_history()
st.session_state.activate_chat = True
st.subheader("PDF Viewer")
pdf_path = "data/fy2024.pdf"
if st.button('Show PDF'):
st.session_state.pdf_path = pdf_path
if "pdf_path" in st.session_state:
pdf_path = st.session_state.pdf_path
display_pdf(pdf_path)
for message in st.session_state.messages:
with st.chat_message(message["role"], avatar = message['avatar']):
st.markdown(message["content"])
if st.session_state.activate_chat == True:
if prompt := st.chat_input("Ask your question from the PDF? "):
with st.chat_message("user", avatar = '👨🏻'):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "avatar" :'👨🏻', "content": prompt})
user_id = st.session_state.user_id
session_id = st.session_state.session_id
vectorstore = st.session_state.vectorstore
chain = st.session_state.chain
print("chain Done")
data=get_context_from_vectorstore(vectorstore,prompt)
ai_msg =chain.invoke({"data": data, "input": prompt}, config={"configurable": {"user_id": user_id, "session_id": session_id}})
cleaned_response=ai_msg.content
with st.chat_message("assistant", avatar='🤖'):
st.markdown(cleaned_response)
st.session_state.messages.append({"role": "assistant", "avatar" :'🤖', "content": cleaned_response})
if __name__ == '__main__':
main() |