app.py

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 for News
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"""
                        <div style="padding: 10px; border: 1px solid #ccc; border-radius: 5px; margin: 10px; text-align: center;">
                            <a href="{article}" target="_blank" style="text-decoration: none;">
                                <button style="background-color: #4CAF50; color: white; border: none; padding: 10px 20px; text-align: center; display: inline-block; font-size: 16px; border-radius: 5px;">
                                    {category} Article {idx + 1}
                                </button>
                            </a>
                        </div>
                        """,
                        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)}")