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| import streamlit as st | |
| import os | |
| from pathlib import Path | |
| from PIL import Image | |
| from rag_sec.document_search_system import DocumentSearchSystem | |
| from chainguard.blockchain_logger import BlockchainLogger | |
| import requests | |
| import pandas as pd | |
| # SerpAPI Key (Replace with your SerpAPI key) | |
| SERPAPI_KEY = "your_serpapi_api_key" | |
| # Blockchain Logger | |
| blockchain_logger = BlockchainLogger() | |
| # Initialize DocumentSearchSystem | |
| def initialize_system(): | |
| """Initialize the DocumentSearchSystem and load documents.""" | |
| home_dir = Path(os.getenv("HOME", "/")) | |
| 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() | |
| # Function to Fetch News from SerpAPI | |
| def fetch_news(query, num_results=5): | |
| """Fetch search results using SerpAPI.""" | |
| url = "https://serpapi.com/search" | |
| params = { | |
| "engine": "google", | |
| "q": query, | |
| "api_key": SERPAPI_KEY, | |
| "num": num_results | |
| } | |
| try: | |
| response = requests.get(url, params=params) | |
| response.raise_for_status() | |
| search_results = response.json().get("organic_results", []) | |
| return [{"title": result["title"], "link": result["link"]} for result in search_results] | |
| except Exception as e: | |
| return [{"error": f"An error occurred: {str(e)}"}] | |
| # Mock User Information (Replace with actual Google Login integration if needed) | |
| def get_user_info(): | |
| """Fetch or mock user details.""" | |
| return {"name": "Talex Maxim", "email": "taimax13@gmail.com"} # Replace with dynamic user info | |
| # Directory for storing uploaded files | |
| UPLOAD_DIR = "uploaded_files" | |
| os.makedirs(UPLOAD_DIR, exist_ok=True) | |
| # Streamlit Layout | |
| st.title("Memora: Advanced File Upload and News Insights") | |
| st.subheader("Securely upload, organize, and query your files while staying informed.") | |
| # User-Specific Information | |
| user_info = get_user_info() | |
| if user_info: | |
| st.sidebar.write("### Logged in as:") | |
| st.sidebar.write(f"**Name:** {user_info['name']}") | |
| st.sidebar.write(f"**Email:** {user_info['email']}") | |
| else: | |
| st.sidebar.write("### Not Logged In") | |
| st.sidebar.write("We invite you on the journey! Please log in with your Google account.") | |
| # Google Search: User-Specific News | |
| if user_info: | |
| st.subheader("1. Latest News About You") | |
| user_name = user_info["name"] | |
| st.write(f"Fetching latest news for **{user_name}**...") | |
| user_news = fetch_news(user_name, num_results=5) | |
| if user_news and "error" not in user_news[0]: | |
| st.success(f"Top {len(user_news)} results for '{user_name}':") | |
| user_news_df = pd.DataFrame(user_news) | |
| st.dataframe(user_news_df) | |
| else: | |
| st.error(user_news[0].get("error", "No news found.")) | |
| else: | |
| st.warning("Please log in with your Google account to fetch personalized news.") | |
| # Google Search: Global News Categories | |
| st.subheader("2. Global News Insights") | |
| categories = ["Technology", "Sports", "Politics", "Entertainment", "Science"] | |
| for category in categories: | |
| st.write(f"Fetching news for **{category}**...") | |
| category_results = fetch_news(f"latest {category} news", num_results=3) | |
| if category_results and "error" not in category_results[0]: | |
| st.success(f"Top {len(category_results)} results for '{category}':") | |
| for idx, result in enumerate(category_results, start=1): | |
| st.write(f"{idx}. [{result['title']}]({result['link']})") | |
| else: | |
| st.error(category_results[0].get("error", "No news found.")) | |
| # File Upload Section | |
| st.subheader("3. Upload and Organize Files") | |
| 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 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 | |
| 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 transaction | |
| system.neo4j_handler.log_relationships(uploaded_file.name, album, blockchain_hash, []) | |
| 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 ❌") | |
| # Query System | |
| st.subheader("4. 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)}") | |