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import os
import io
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
from groq import Groq
# Load environment variables
load_dotenv()
#GROQ_API_KEY = "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"
# Initialize Groq client
client = Groq(api_key=os.getenv("MY_API_KEY"))
# Load the embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
# Streamlit UI
st.set_page_config(page_title="RAG-Based Application", layout="wide")
st.title("RAG-Based Application")
st.sidebar.header("Upload Your PDF")
uploaded_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
try:
# Extract text from PDF
st.write("Extracting text from the PDF...")
reader = PdfReader(io.BytesIO(uploaded_file.read()))
text = "".join([page.extract_text() for page in reader.pages])
if not text.strip():
st.error("The uploaded PDF contains no text. Please upload a valid document.")
st.stop()
# Split the text into chunks
st.write("Processing the PDF into chunks...")
chunk_size = 500
chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
# Create embeddings for the chunks
st.write("Creating embeddings for text chunks...")
embeddings = embedding_model.encode(chunks)
if len(embeddings.shape) == 1:
embeddings = embeddings.reshape(1, -1)
# Store embeddings in FAISS
st.write("Storing embeddings in FAISS...")
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)
st.write(f"Stored {len(chunks)} chunks in FAISS.")
# Ask a question
st.subheader("Ask a Question")
user_query = st.text_input("Enter your question:")
if user_query:
query_embedding = embedding_model.encode([user_query])
distances, indices = index.search(query_embedding, k=1)
best_chunk = chunks[indices[0][0]]
# Use Groq API to interact with the LLM
st.write("Interacting with the LLM...")
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": f"Using this context: {best_chunk}, answer the following question: {user_query}",
}
],
model="llama3-8b-8192",
)
# Display the response
st.subheader("LLM Response")
st.write(chat_completion.choices[0].message.content)
except Exception as e:
st.error(f"An error occurred: {e}")
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