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import streamlit as st
import PyPDF2
from io import StringIO
import PyPDF2
from RAG_GGUF import RAG_Chain
from llama_cpp import Llama
# Function to send pdf file to RAG pipeline
def read_pdf(file):
pdf_reader = PyPDF2.PdfReader(file)
text = ""
for page_num in range(len(pdf_reader.pages) ):
page = pdf_reader.pages[page_num]
text += page.extract_text()
return text
st.title("Talk with Your PDF")
# PDF Upload
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
if uploaded_file is not None:
# Display the file name
st.write(f"File uploaded: {uploaded_file.name}")
# Read and display the content of the uploaded PDF file
try:
pdf_content = read_pdf(uploaded_file)
st.text_area("PDF Content", pdf_content, height=300)
except Exception as e:
st.error(f"Error reading PDF: {e}")
# Input field for user messages
user_input = st.text_input("You:", "")
else:
st.text_area("PDF Content","Please Upload File",height=300)
# Initialize a session state for chat history
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
model_path = "/home/mona/Downloads/Pubmed_model_GGUF"
llama_model = Llama(
model_path,
n_ctx=2048, # Context length
#n_threads=8, # Number of CPU threads to use
temperature=0.7, # Sampling temperature
n_gpu_layers=4
)
# Handle user input
if st.button("Send"):
#import chromadb.api
#chromadb.api.client.SharedSystemClient.clear_system_cache()
if user_input:
# Get the GPT response
gpt_response = RAG_Chain(uploaded_file,user_input,llama_model)
# Store the conversation
st.session_state.chat_history.append(("User", user_input))
st.session_state.chat_history.append(("BOT", gpt_response))
# Clear the input box
user_input = ""
# Display chat history
for speaker, message in st.session_state.chat_history:
if speaker == "User":
st.markdown(f"**{speaker}:** {message}")
else:
st.markdown(f"**{speaker}:** {message}")
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