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import os
import faiss
import streamlit as st
from groq import Groq
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np
from streamlit_option_menu import option_menu
# Groq API Key
client = Groq(api_key="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")
# Load embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
# FAISS Index
dimension = 384
index = faiss.IndexFlatL2(dimension)
# Sidebar navigation
with st.sidebar:
selected = option_menu(
"Research Article Helper",
["Home", "Upload PDF", "Summary", "About"],
icons=["house", "file-earmark-arrow-up", "file-earmark-text", "info-circle"],
menu_icon="cast",
default_index=0,
)
# App title
st.title("📚 Research Article Helper")
st.write("Interact with research articles by uploading PDFs and asking questions.")
# "Upload PDF" Section
if selected == "Upload PDF":
st.subheader("📂 Upload a PDF")
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file:
pdf_reader = PdfReader(uploaded_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
# Split text into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = text_splitter.split_text(text)
# Encode chunks and store embeddings in FAISS
embeddings = embedding_model.encode(chunks)
for i, embedding in enumerate(embeddings):
index.add(np.array([embedding]))
st.success(f"✅ Processed {len(chunks)} chunks and stored embeddings in FAISS.")
st.session_state['chunks'] = chunks
# "Summary" Section
if selected == "Summary":
st.subheader("📑 Get a Summary of the Uploaded PDF")
if 'chunks' in st.session_state:
if st.button("Generate Summary"):
full_text = " ".join(st.session_state['chunks'][:5]) # Summarizing first 5 chunks as an example
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": f"Provide a concise summary of the following text:\n\n{full_text}",
}
],
model="llama3-8b-8192",
)
summary = chat_completion.choices[0].message.content
st.write("### 📋 Summary:")
st.write(summary)
else:
st.info("Click the button above to generate a summary.")
else:
st.warning("Please upload a PDF in the 'Upload PDF' section first.")
# "About" Section
if selected == "About":
st.subheader("ℹ️ About")
st.write("**Research Article Helper** is a tool to interact with research articles by uploading PDFs. Use it to ask questions, generate summaries, and gain insights from scientific documents.")
st.write("Built using Streamlit, FAISS, and Groq AI.")
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