from pathlib import Path import streamlit as st from googlesearch import search import pandas as pd import os from rag_sec.document_search_system import DocumentSearchSystem from chainguard.blockchain_logger import BlockchainLogger from PIL import Image from itertools import cycle # Blockchain Logger blockchain_logger = BlockchainLogger() # Directory for storing uploaded files UPLOAD_DIR = "uploaded_files" os.makedirs(UPLOAD_DIR, exist_ok=True) # Initialize DocumentSearchSystem @st.cache_resource def initialize_system(): """Initialize the DocumentSearchSystem and load documents.""" system = DocumentSearchSystem( neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io", neo4j_user="neo4j", neo4j_password="HwGDOxyGS1-79nLeTiX5bx5ohoFSpvHCmTv8IRgt-lY" ) system.retriever.load_documents() return system # Initialize the system system = initialize_system() st.title("Memora: Secure File Upload and Search with Blockchain & Neo4j") st.subheader("Personalized news and global updates at your fingertips") # File Upload Section 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: # Save file locally file_path = os.path.join(UPLOAD_DIR, uploaded_file.name) with open(file_path, "wb") as f: f.write(uploaded_file.getbuffer()) st.success(f"File saved locally: {file_path}") # Display uploaded file details if uploaded_file.type.startswith('image'): image = Image.open(uploaded_file) st.image(image, caption=uploaded_file.name, use_column_width=True) # Metadata Input album = st.text_input(f"Album for {uploaded_file.name}", "Default Album") tags = st.text_input(f"Tags for {uploaded_file.name} (comma-separated)", "") # Log Metadata and Transaction if st.button(f"Log Metadata for {uploaded_file.name}"): metadata = {"file_name": uploaded_file.name, "tags": tags.split(','), "album": album} blockchain_details = blockchain_logger.log_data(metadata) blockchain_hash = blockchain_details.get("block_hash", "N/A") # Use Neo4jHandler from DocumentSearchSystem to log the transaction system.neo4j_handler.log_relationships(uploaded_file.name, tags, blockchain_hash, [album]) st.write(f"Metadata logged successfully! Blockchain Details: {blockchain_details}") # Blockchain Integrity Validation if st.button("Validate Blockchain Integrity"): is_valid = blockchain_logger.is_blockchain_valid() st.write("Blockchain Integrity:", "Valid ✅" if is_valid else "Invalid ❌") # Document Search Section st.subheader("Search Documents") # Google Search: User-Specific News st.subheader("1. Latest News About You") user_name = st.text_input("Enter your name or handle to search for recent news", value="Talex Maxim") if st.button("Search News About Me"): if user_name: st.write(f"Searching Google for news about **{user_name}**...") try: results = list(search(user_name, num_results=5)) if results: st.success(f"Top {len(results)} results for '{user_name}':") user_news_data = {"URL": results} df_user_news = pd.DataFrame(user_news_data) st.dataframe(df_user_news) else: st.warning("No recent news found about you.") except Exception as e: st.error(f"An error occurred during the search: {str(e)}") else: st.warning("Please enter your name or handle to search.") # Google Search: Global News Categories categories = ["Technology", "Sports", "Politics", "Entertainment", "Science"] st.title("Global News Insights") # News Results Dictionary news_results = {} try: # Fetch News for Each Category for category in categories: try: news_results[category] = list(search(f"latest {category} news", num_results=3)) except Exception as e: news_results[category] = [f"Error fetching news: {str(e)}"] # Display Results with Styled Buttons for category, articles in news_results.items(): st.subheader(f"{category} News") cols = st.columns(3) # Create 3 columns for the layout if articles and "Error fetching news" not in articles[0]: for idx, article in enumerate(articles): with cols[idx % 3]: # Cycle through columns st.markdown( f"""
""", unsafe_allow_html=True, ) else: st.warning(f"Could not fetch news for **{category}**.") except Exception as e: st.error(f"An unexpected error occurred: {str(e)}") # # Display results # for category, articles in news_results.items(): # st.write(f"### Top News in {category}:") # for idx, article in enumerate(articles, start=1): # st.write(f"{idx}. [Read here]({article})") # except Exception as e: # st.error(f"An error occurred while fetching global news: {str(e)}") # Document Search st.subheader("3. Search Documents") query = st.text_input("Enter your query (e.g., 'sports news', 'machine learning')") if st.button("Search Documents"): if query: result = system.process_query(query) if result["status"] == "success": st.success(f"Query processed successfully!") st.write("### Query Response:") st.write(result["response"]) st.write("### Retrieved Documents:") for idx, doc in enumerate(result["retrieved_documents"], start=1): st.write(f"**Document {idx}:**") st.write(doc[:500]) # Display the first 500 characters st.write("### Blockchain Details:") st.json(result["blockchain_details"]) elif result["status"] == "no_results": st.warning("No relevant documents found for your query.") elif result["status"] == "rejected": st.error(result["message"]) else: st.warning("Please enter a query to search.") # Debugging Section if st.checkbox("Show Debug Information"): st.write(f"Total documents loaded: {len(system.retriever.documents)}")