update

app.py

@@ -1,6 +1,8 @@
 import streamlit as st
-import os
+from googlesearch import search
+import pandas as pd
 from pathlib import Path
+import os
 from PIL import Image
 from rag_sec.document_search_system import DocumentSearchSystem
 from chainguard.blockchain_logger import BlockchainLogger
@@ -34,10 +36,28 @@ os.makedirs(UPLOAD_DIR, exist_ok=True)
 st.title("Memora: Secure File Upload and Search with Blockchain & Neo4j")
 st.subheader("Securely upload, organize, and query your files")
 
+# Google Search Section
+st.subheader("Find User Information via Google Search")
+search_query = st.text_input("Enter a name or topic to search on Google")
+
+if st.button("Google Search"):
+    if search_query:
+        try:
+            results = list(search(search_query, num_results=5))  # Fetch top 5 results
+            if results:
+                st.success(f"Top {len(results)} results for '{search_query}':")
+                result_data = {"URL": results}
+                df = pd.DataFrame(result_data)
+                st.dataframe(df)
+            else:
+                st.warning("No results found for the search query.")
+        except Exception as e:
+            st.error(f"An error occurred during the search: {str(e)}")
+    else:
+        st.warning("Please enter a search query.")
+
 # File Upload Section
-uploaded_files = st.file_uploader(
-    "Upload your files", accept_multiple_files=True, type=['jpg', 'jpeg', 'png', 'mp4', 'avi']
-)
+uploaded_files = st.file_uploader("Upload your files", accept_multiple_files=True, type=['jpg', 'jpeg', 'png', 'mp4', 'avi'])
 
 if uploaded_files:
     for uploaded_file in uploaded_files:

rag_sec/document_search_system.py

@@ -7,10 +7,10 @@ import sys
 from os import path
 
 sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
-from .bad_query_detector import BadQueryDetector
-from .query_transformer import QueryTransformer
-from .document_retriver import DocumentRetriever
-from .senamtic_response_generator import SemanticResponseGenerator
+from bad_query_detector import BadQueryDetector
+from query_transformer import QueryTransformer
+from document_retriver import DocumentRetriever
+from senamtic_response_generator import SemanticResponseGenerator
 
 
 class DataTransformer: