Update app.py

app.py

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+import os
+import fitz  # PyMuPDF for PDF processing
+import faiss
+import numpy as np
+import streamlit as st
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+from dotenv import load_dotenv
+
+
+
+
+# Load API key
+load_dotenv()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# Initialize Groq client
+client = Groq(api_key= GROQ_API_KEY)
+
+# Load sentence transformer model for embedding
+embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+def extract_text_from_pdf(pdf_path):
+    """Extract text from a PDF file using PyMuPDF."""
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text("text") + "\n"
+    return text.strip()
+def extract_text_from_pdf(pdf_path):
+    """Extract text from a PDF file using PyMuPDF."""
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text("text") + "\n"
+    return text.strip()
+def create_text_chunks(text, chunk_size=500, chunk_overlap=100):
+    """Split text into chunks of specified size with overlap."""
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+def create_faiss_index(chunks):
+    """Generate embeddings for text chunks and store them in FAISS."""
+    embeddings = embedding_model.encode(chunks, convert_to_numpy=True)
+    dimension = embeddings.shape[1]
+
+    index = faiss.IndexFlatL2(dimension)  # L2 (Euclidean) distance
+    index.add(embeddings)  # Add embeddings to FAISS index
+
+    return index, embeddings, chunks
+def retrieve_similar_chunks(query, index, embeddings, chunks, top_k=3):
+    """Retrieve the most relevant text chunks using FAISS."""
+    query_embedding = embedding_model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_embedding, top_k)
+
+    results = [chunks[idx] for idx in indices[0]]
+    return results
+def query_groq_api(query, context):
+    """Send the query along with retrieved context to Groq API."""
+    prompt = f"Use the following context to answer the question:\n\n{context}\n\nQuestion: {query}\nAnswer:"
+
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3.3-70b-versatile",
+    )
+
+    return chat_completion.choices[0].message.content
+import streamlit as st
+
+st.title("📚 RAG-based PDF Query Application")
+st.write("Upload a PDF and ask questions!")
+
+# File Upload
+uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+
+if uploaded_file is not None:
+    pdf_path = "uploaded_document.pdf"
+
+    # Save file temporarily
+    with open(pdf_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Process the PDF
+    st.write("Processing PDF...")
+    text = extract_text_from_pdf(pdf_path)
+    chunks = create_text_chunks(text)
+    index, embeddings, chunk_texts = create_faiss_index(chunks)
+
+    st.success("PDF processed! Now you can ask questions.")
+
+    # User Query
+    query = st.text_input("Ask a question about the PDF:")
+
+    if st.button("Get Answer"):
+        if query:
+            # Retrieve top chunks
+            relevant_chunks = retrieve_similar_chunks(query, index, embeddings, chunk_texts)
+            context = "\n\n".join(relevant_chunks)
+
+            # Query Groq API
+            response = query_groq_api(query, context)
+
+            st.subheader("Answer:")
+            st.write(response)
+        else:
+            st.warning("Please enter a question.")
+