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from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from transformers import pipeline
import streamlit as st
import requests
from io import BytesIO
# Set up Hugging Face model pipeline for text generation
pipe = pipeline("text-generation", model="meta-llama/Llama-Guard-3-8B-INT8")
# List of GitHub PDF URLs
PDF_URLS = [
"https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi%20pat%20graphs.pdf",
"https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi-partite.pdf",
# Add more document links as needed
]
def fetch_pdf_text_from_github(urls):
text = ""
for url in urls:
response = requests.get(url)
if response.status_code == 200:
pdf_file = BytesIO(response.content)
pdf_reader = PdfReader(pdf_file)
for page in pdf_reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text
else:
st.error(f"Failed to fetch PDF from URL: {url}")
return text
@st.cache_data
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
chunks = text_splitter.split_text(text)
return chunks
@st.cache_resource
def load_or_create_vector_store(text_chunks):
embeddings = FAISS.get_default_embeddings()
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
return vector_store
def generate_answer(user_question, context_text):
# Format the input message for the pipeline
messages = [
{"role": "user", "content": f"Context: {context_text}\nQuestion: {user_question}"}
]
# Generate response using the pipeline
response = pipe(messages, max_length=250, do_sample=True)
return response[0]['generated_text'][:250] # Limit response to 250 characters
def user_input(user_question, vector_store):
docs = vector_store.similarity_search(user_question)
context_text = " ".join([doc.page_content for doc in docs])
return generate_answer(user_question, context_text)
def main():
st.set_page_config(page_title="RAG-based PDF Chat", layout="centered", page_icon="📄")
st.title("📄 Query PDF Documents on GitHub")
# Load documents from GitHub
raw_text = fetch_pdf_text_from_github(PDF_URLS)
text_chunks = get_text_chunks(raw_text)
vector_store = load_or_create_vector_store(text_chunks)
# User question input
user_question = st.text_input("Ask a Question:", placeholder="Type your question here...")
if st.button("Get Response"):
if not user_question:
st.warning("Please enter a question before submitting.")
else:
with st.spinner("Generating response..."):
answer = user_input(user_question, vector_store)
st.markdown(f"**🤖 AI:** {answer}")
if __name__ == "__main__":
main()
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