import streamlit as st
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

# Blockchain Logger
blockchain_logger = BlockchainLogger()

# Initialize DocumentSearchSystem
@st.cache_resource
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 and load system
st.write("Initializing the Document Search System...")
system = initialize_system()
st.success("System initialized and documents loaded!")

# Directory for storing uploaded files
UPLOAD_DIR = "uploaded_files"
os.makedirs(UPLOAD_DIR, exist_ok=True)

# Streamlit Layout
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'])

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")

# Query Input
query = st.text_input("Enter your query (e.g., 'sports news', 'machine learning')")

if st.button("Search"):
    if query:
        # Process query through the DocumentSearchSystem
        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)}")