Create app.py

app.py

@@ -0,0 +1,40 @@
+# app.py
+import streamlit as st
+from utils.pdf_processing import extract_text_from_pdf, split_into_chunks
+from utils.embeddings import generate_embeddings
+from utils.database import initialize_faiss, insert_embeddings, search_embeddings
+
+def main():
+    st.title("PDF Retrieval-Augmented Generation (RAG) Application")
+
+    # Initialize FAISS index
+    faiss_index = initialize_faiss()
+
+    # Upload PDF file
+    uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+    if uploaded_file is not None:
+        # Extract text from the uploaded PDF
+        with st.spinner("Processing PDF..."):
+            text = extract_text_from_pdf(uploaded_file)
+            chunks = split_into_chunks(text)
+
+        # Generate embeddings for text chunks
+        with st.spinner("Generating embeddings..."):
+            embeddings = generate_embeddings(chunks)
+
+        # Insert embeddings into FAISS index
+        with st.spinner("Inserting embeddings into FAISS..."):
+            insert_embeddings(faiss_index, embeddings, chunks)
+
+        st.success("PDF processed and embeddings stored successfully!")
+
+    # Search functionality
+    query = st.text_input("Enter a query to search:")
+    if query:
+        with st.spinner("Searching..."):
+            query_embedding = generate_embeddings([query])[0]
+            results = search_embeddings(faiss_index, query_embedding)
+            st.write("Results:", results)
+
+if __name__ == "__main__":
+    main()