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
from rag_sec.document_search_system import main
import streamlit as st
from rag_sec.document_retriver import DocumentRetriever

# Blockchain Logger
blockchain_logger = BlockchainLogger()

# Initialize DocumentSearchSystem
home_dir = Path(os.getenv("HOME", "/"))
data_dir = home_dir / "data-sets/aclImdb/train"
system = DocumentSearchSystem(
    neo4j_uri="neo4j+s://0ca71b10.databases.neo4j.io",
    neo4j_user="neo4j",
    neo4j_password="HwGDOxyGS1-79nLeTiX5bx5ohoFSpvHCmTv8IRgt-lY"
)

system.retriever.load_documents()

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

# Streamlit Layout
st.title("Memora: Secure File Upload with Blockchain & Neo4j")
st.subheader("Securely upload, organize, and query your files")

# File Upload
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_transaction(uploaded_file.name, file_path, 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("Query Files")

# Initialize DocumentRetriever
retriever = DocumentRetriever()

@st.cache(allow_output_mutation=True)
def load_retriever():
    """Load documents into the retriever."""
    retriever.load_documents()
    return retriever

# Load the retriever and documents
st.write("Loading documents...")
retriever = load_retriever()
st.write("Documents successfully loaded!")

# Streamlit UI
st.title("Document Search App")
st.subheader("Enter a query to search for related documents")

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

if st.button("Search"):
    if query:
        # Retrieve documents
        results = retriever.retrieve(query)
        if results == ["Document retrieval is not initialized."]:
            st.error("Document retrieval is not initialized. Please reload the app.")
        elif not results:
            st.warning("No relevant documents found for your query.")
        else:
            st.success(f"Found {len(results)} relevant document(s).")
            for idx, doc in enumerate(results, start=1):
                st.write(f"### Document {idx}")
                st.write(doc[:500])  # Display first 500 characters of each document
    else:
        st.warning("Please enter a query to search.")

# Debugging Section
if st.checkbox("Show Debug Information"):
    st.write(f"Total documents loaded: {len(retriever.documents)}")