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import streamlit as st
import os
import tempfile
from pathlib import Path
import torch
from pdf_parser import PDFParser
from embedder import ChromaDBManager
from rag_pipeline import RAGPipeline


# ============================================================================
# PAGE CONFIGURATION
# ============================================================================

st.set_page_config(
    page_title="Multimodal PDF RAG System",
    page_icon="📄",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ============================================================================
# CUSTOM STYLING
# ============================================================================

st.markdown("""
<style>
.main {
    padding: 2rem;
}
.stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
    font-size: 1.2rem;
}
.upload-area {
    border: 2px dashed #ccc;
    border-radius: 5px;
    padding: 20px;
    text-align: center;
}
.success-box {
    background-color: #d4edda;
    border: 1px solid #28a745;
    border-radius: 4px;
    padding: 10px;
    margin: 10px 0;
}
.error-box {
    background-color: #f8d7da;
    border: 1px solid #f5c6cb;
    border-radius: 4px;
    padding: 10px;
    margin: 10px 0;
}
</style>
""", unsafe_allow_html=True)

# ============================================================================
# SESSION STATE INITIALIZATION
# ============================================================================

@st.cache_resource
def initialize_system():
    """Initialize RAG system components once."""
    try:
        parser = PDFParser(extraction_dir="./pdf_extractions")
        chroma = ChromaDBManager(db_dir="./chroma_db")
        device = "cuda" if torch.cuda.is_available() else "cpu"
        rag = RAGPipeline(chroma, device=device)
        return parser, chroma, rag, device
    except Exception as e:
        st.error(f"Error initializing system: {e}")
        return None, None, None, None


# Initialize
pdf_parser, chroma_manager, rag_pipeline, device = initialize_system()

if pdf_parser is None:
    st.error("Failed to initialize RAG system. Please check your installation.")
    st.stop()

# Initialize session state for uploaded files
if 'uploaded_files' not in st.session_state:
    st.session_state.uploaded_files = []

if 'processing_status' not in st.session_state:
    st.session_state.processing_status = {}

# ============================================================================
# MAIN UI
# ============================================================================

st.title("📄 Multimodal PDF RAG System")
st.markdown("**Local AI-powered document analysis with Qwen2.5-VL and ChromaDB**")
st.markdown("*Upload PDFs directly and ask questions about them*")

# Create main tabs
tab_upload, tab_query, tab_manage, tab_about = st.tabs(["📤 Upload PDFs", "🔍 Ask Questions", "🛠️ Manage", "ℹ️ About"])

# ============================================================================
# TAB 1: UPLOAD PDFs
# ============================================================================

with tab_upload:
    st.header("📤 Upload PDF Documents")
    
    col1, col2 = st.columns([3, 1])
    
    with col1:
        st.markdown("**Upload your PDF files below. They will be automatically processed and stored.**")
        
        # File uploader
        uploaded_files = st.file_uploader(
            "Choose PDF files",
            type=["pdf"],
            accept_multiple_files=True,
            help="You can upload multiple PDF files at once"
        )
    
    with col2:
        st.info(f"📊 Documents in DB: {chroma_manager.get_collection_info()['document_count']}")
    
    # Process uploaded files
    if uploaded_files:
        st.divider()
        st.subheader("Processing Uploaded Files")
        
        # Create a temporary directory for uploads
        temp_dir = tempfile.mkdtemp()
        
        progress_bar = st.progress(0)
        status_text = st.empty()
        results_container = st.container()
        
        total_files = len(uploaded_files)
        processed_files = []
        failed_files = []
        
        for idx, uploaded_file in enumerate(uploaded_files):
            try:
                # Update progress
                status_text.text(f"Processing {idx + 1}/{total_files}: {uploaded_file.name}")
                
                # Save uploaded file to temp directory
                temp_file_path = os.path.join(temp_dir, uploaded_file.name)
                with open(temp_file_path, "wb") as f:
                    f.write(uploaded_file.getbuffer())
                
                # Process PDF
                with st.spinner(f"Extracting content from {uploaded_file.name}..."):
                    try:
                        result = pdf_parser.process_pdf(temp_file_path)
                        
                        # Add to ChromaDB
                        chroma_manager.add_documents([result])
                        
                        processed_files.append({
                            'name': uploaded_file.name,
                            'size': uploaded_file.size,
                            'text_length': len(result.get('text', '')),
                            'tables': len(result.get('tables', [])),
                            'images': len(result.get('images', []))
                        })
                        
                        st.success(f"✅ {uploaded_file.name} processed successfully")
                    
                    except Exception as e:
                        failed_files.append({
                            'name': uploaded_file.name,
                            'error': str(e)
                        })
                        st.error(f"❌ Error processing {uploaded_file.name}: {e}")
                
                # Update progress
                progress_bar.progress((idx + 1) / total_files)
            
            except Exception as e:
                failed_files.append({
                    'name': uploaded_file.name,
                    'error': str(e)
                })
                st.error(f"❌ Error with {uploaded_file.name}: {e}")
        
        # Show summary
        st.divider()
        st.subheader("Upload Summary")
        
        col1, col2, col3 = st.columns(3)
        
        with col1:
            st.metric("Successfully Processed", len(processed_files))
        
        with col2:
            st.metric("Failed", len(failed_files))
        
        with col3:
            collection_info = chroma_manager.get_collection_info()
            st.metric("Total in Database", collection_info['document_count'])
        
        # Show details of processed files
        if processed_files:
            st.markdown("#### ✅ Processed Files:")
            for file_info in processed_files:
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.text(file_info['name'])
                with col2:
                    st.text(f"{file_info['size'] / 1024:.1f} KB")
                with col3:
                    st.text(f"{file_info['text_length']:,} chars")
                with col4:
                    st.text(f"{file_info['tables']} tables, {file_info['images']} imgs")
        
        # Show failed files
        if failed_files:
            st.markdown("#### ❌ Failed Files:")
            for file_info in failed_files:
                st.error(f"**{file_info['name']}**: {file_info['error']}")

# ============================================================================
# TAB 2: ASK QUESTIONS
# ============================================================================

with tab_query:
    st.header("🔍 Ask Questions About Your Documents")
    
    collection_info = chroma_manager.get_collection_info()
    
    if collection_info['document_count'] == 0:
        st.warning("⚠️ No documents uploaded yet. Please upload PDFs in the 'Upload PDFs' tab first.")
    else:
        st.success(f"✅ {collection_info['document_count']} documents in database")
        
        col1, col2, col3 = st.columns([2, 1, 1])
        
        with col1:
            query = st.text_input(
                "Enter your question:",
                placeholder="Например: Какие ключевые моменты описаны в документе?",
                help="Ask any question about your uploaded documents"
            )
        
        with col2:
            n_docs = st.number_input("Retrieved docs:", value=3, min_value=1, max_value=10)
        
        with col3:
            max_tokens = st.number_input("Max tokens:", value=256, min_value=128, max_value=512, step=128)
        
        if st.button("🚀 Get Answer", use_container_width=True, type="primary"):
            if not query:
                st.warning("⚠️ Please enter a question.")
            else:
                try:
                    with st.spinner("🤖 Generating answer... (this may take 10-30 seconds)"):
                        st.info("Processing query - please wait...")
                        
                        # Generate answer with error handling
                        try:
                            result = rag_pipeline.answer_question(
                                query=query,
                                n_retrieved=n_docs,
                                max_new_tokens=max_tokens
                            )
                            
                            # Check for errors
                            if "error" in result and result["error"]:
                                st.error(f"⚠️ {result['error']}")
                            
                            # Display answer
                            st.success("✅ Answer Generated")
                            st.markdown("### Answer")
                            st.write(result['answer'])
                            
                            # Display retrieved documents
                            with st.expander("📚 Retrieved Documents", expanded=False):
                                st.markdown(f"#### {result['doc_count']} Relevant Document Chunks:")
                                for idx, doc in enumerate(result['retrieved_docs'], 1):
                                    with st.container():
                                        col_rel, col_score = st.columns([3, 1])
                                        with col_rel:
                                            st.markdown(f"**Document {idx}**")
                                        with col_score:
                                            st.caption(f"Score: {doc['relevance_score']:.1%}")
                                        
                                        # Truncate for display
                                        preview = doc['document'][:400]
                                        if len(doc['document']) > 400:
                                            preview += "..."
                                        st.write(preview)
                                        
                                        if doc['metadata']:
                                            st.caption(f"Source: {doc['metadata'].get('filename', 'Unknown')}")
                        
                        except Exception as e:
                            st.error(f"❌ Error during generation: {e}")
                            st.info("Possible causes:")
                            st.write("- Out of memory (try reducing 'Max tokens' or 'Retrieved docs')")
                            st.write("- Model inference timeout")
                            st.write("- Invalid input format")
                
                except Exception as e:
                    st.error(f"❌ Unexpected error: {e}")

# ============================================================================
# TAB 3: MANAGE DATABASE
# ============================================================================

with tab_manage:
    st.header("🛠️ Database Management")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        if st.button("ℹ️ Database Info", use_container_width=True):
            try:
                info = chroma_manager.get_collection_info()
                st.json(info)
            except Exception as e:
                st.error(f"Error: {e}")
    
    with col2:
        if st.button("📋 List Documents", use_container_width=True):
            try:
                all_docs = chroma_manager.collection.get(include=['documents'])
                if all_docs['ids']:
                    st.write(f"Total documents: {len(all_docs['ids'])}")
                    
                    col1_list, col2_list = st.columns(2)
                    
                    with col1_list:
                        st.write("**First 10:**")
                        for idx, doc_id in enumerate(all_docs['ids'][:10], 1):
                            st.write(f"{idx}. {doc_id[:50]}...")
                    
                    with col2_list:
                        if len(all_docs['ids']) > 10:
                            st.write(f"**... and {len(all_docs['ids']) - 10} more**")
                else:
                    st.info("No documents in database")
            except Exception as e:
                st.error(f"Error: {e}")
    
    with col3:
        if st.button("🗑️ Clear Database", use_container_width=True):
            try:
                collection_info = chroma_manager.get_collection_info()
                if collection_info['document_count'] > 0:
                    chroma_manager.clear_collection()
                    st.success("✅ Database cleared!")
                    st.rerun()
                else:
                    st.info("Database is already empty")
            except Exception as e:
                st.error(f"Error: {e}")
    
    st.divider()
    
    st.markdown("### Storage Information")
    col1, col2 = st.columns(2)
    
    with col1:
        extraction_size = sum(
            os.path.getsize(os.path.join(dirpath, filename))
            for dirpath, dirnames, filenames in os.walk("./pdf_extractions")
            for filename in filenames
        ) / (1024 * 1024) if os.path.exists("./pdf_extractions") else 0
        st.metric("PDF Extractions", f"{extraction_size:.1f} MB")
    
    with col2:
        chroma_size = sum(
            os.path.getsize(os.path.join(dirpath, filename))
            for dirpath, dirnames, filenames in os.walk("./chroma_db")
            for filename in filenames
        ) / (1024 * 1024) if os.path.exists("./chroma_db") else 0
        st.metric("ChromaDB Storage", f"{chroma_size:.1f} MB")

# ============================================================================
# TAB 4: ABOUT
# ============================================================================

with tab_about:
    st.header("ℹ️ About This System")
    
    st.markdown("""
    ### Multimodal RAG System with PDF Upload
    
    This is a **local, privacy-first AI document analysis system** that allows you to:
    
    #### ✨ Features
    - **📤 Easy PDF Upload**: Drag & drop or select multiple PDF files
    - **🔍 Smart Search**: Semantic search across documents with CLIP embeddings
    - **🤖 AI-Powered Answers**: Ask questions and get answers from Qwen2.5-VL-3B
    - **🌐 Russian & English**: Full support for both languages
    - **💾 Local Storage**: All data stays on your machine
    - **⚡ Fast Processing**: Automatic caching to avoid re-processing
    
    #### 🏗️ How It Works
    1. Upload PDF documents
    2. System extracts text, tables, and images
    3. Content is embedded with CLIP and stored in ChromaDB
    4. Ask questions about your documents
    5. AI retrieves relevant sections and generates answers
    
    #### 🔐 Privacy & Security
    - ✅ All processing happens locally
    - ✅ No internet required (after model download)
    - ✅ No cloud APIs used
    - ✅ Full data control
    - ✅ Open-source code
    
    #### 💻 Technology Stack
    - **LLM**: Qwen2.5-VL-3B (multimodal)
    - **Embeddings**: CLIP (clip-vit-base-patch32)
    - **Vector DB**: ChromaDB
    - **UI**: Streamlit
    - **PDF Processing**: pdfplumber + PyMuPDF
    
    #### 📊 System Info
    """)
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        device_name = "GPU (CUDA)" if torch.cuda.is_available() else "CPU"
        st.metric("Device", device_name)
    
    with col2:
        collection_info = chroma_manager.get_collection_info()
        st.metric("Documents in DB", collection_info['document_count'])
    
    with col3:
        st.metric("Version", "1.2 (Upload)")
    
    st.divider()
    
    st.markdown("""
    #### 📝 How to Use
    
    1. **Upload PDFs**: Go to the "Upload PDFs" tab and select your files
    2. **Wait for Processing**: System automatically extracts content
    3. **Ask Questions**: Switch to "Ask Questions" tab and type your query
    4. **Review Results**: See generated answers and relevant document chunks
    5. **Manage**: Use "Manage" tab to view or clear database
    
    #### ⚙️ Tips for Best Results
    - Start with smaller PDFs to test
    - Ask specific questions for better answers
    - Reduce "Retrieved docs" if responses are slow
    - Use Russian for Russian documents (better accuracy)
    
    #### 🔧 Performance Tuning
    - **Slow responses**: Reduce "Max tokens" from 512 to 256
    - **Out of memory**: Use fewer "Retrieved docs" (1-3)
    - **Better quality**: Increase "Max tokens" to 512
    
    #### ❓ Troubleshooting
    - **App closes**: Reduce "Max tokens" and "Retrieved docs"
    - **Slow processing**: First upload takes time (model loading)
    - **Memory issues**: Use CPU mode (edit in sidebar)
    """)

# ============================================================================
# FOOTER
# ============================================================================

st.divider()
st.markdown("""
<div style='text-align: center; color: #666; font-size: 0.9rem;'>
    Multimodal RAG System with PDF Upload | Qwen2.5-VL + ChromaDB + Streamlit | v1.2
</div>
""", unsafe_allow_html=True)

	import streamlit as st
	import os
	import tempfile
	from pathlib import Path
	import torch
	from pdf_parser import PDFParser
	from embedder import ChromaDBManager
	from rag_pipeline import RAGPipeline


	# ============================================================================
	# PAGE CONFIGURATION
	# ============================================================================

	st.set_page_config(
	page_title="Multimodal PDF RAG System",
	page_icon="📄",
	layout="wide",
	initial_sidebar_state="expanded"
	)

	# ============================================================================
	# CUSTOM STYLING
	# ============================================================================

	st.markdown("""
	<style>
	.main {
	padding: 2rem;
	}
	.stTabs [data-baseweb="tab-list"] button [data-testid="stMarkdownContainer"] p {
	font-size: 1.2rem;
	}
	.upload-area {
	border: 2px dashed #ccc;
	border-radius: 5px;
	padding: 20px;
	text-align: center;
	}
	.success-box {
	background-color: #d4edda;
	border: 1px solid #28a745;
	border-radius: 4px;
	padding: 10px;
	margin: 10px 0;
	}
	.error-box {
	background-color: #f8d7da;
	border: 1px solid #f5c6cb;
	border-radius: 4px;
	padding: 10px;
	margin: 10px 0;
	}
	</style>
	""", unsafe_allow_html=True)

	# ============================================================================
	# SESSION STATE INITIALIZATION
	# ============================================================================

	@st.cache_resource
	def initialize_system():
	"""Initialize RAG system components once."""
	try:
	parser = PDFParser(extraction_dir="./pdf_extractions")
	chroma = ChromaDBManager(db_dir="./chroma_db")
	device = "cuda" if torch.cuda.is_available() else "cpu"
	rag = RAGPipeline(chroma, device=device)
	return parser, chroma, rag, device
	except Exception as e:
	st.error(f"Error initializing system: {e}")
	return None, None, None, None


	# Initialize
	pdf_parser, chroma_manager, rag_pipeline, device = initialize_system()

	if pdf_parser is None:
	st.error("Failed to initialize RAG system. Please check your installation.")
	st.stop()

	# Initialize session state for uploaded files
	if 'uploaded_files' not in st.session_state:
	st.session_state.uploaded_files = []

	if 'processing_status' not in st.session_state:
	st.session_state.processing_status = {}

	# ============================================================================
	# MAIN UI
	# ============================================================================

	st.title("📄 Multimodal PDF RAG System")
	st.markdown("Local AI-powered document analysis with Qwen2.5-VL and ChromaDB")
	st.markdown("Upload PDFs directly and ask questions about them")

	# Create main tabs
	tab_upload, tab_query, tab_manage, tab_about = st.tabs(["📤 Upload PDFs", "🔍 Ask Questions", "🛠️ Manage", "ℹ️ About"])

	# ============================================================================
	# TAB 1: UPLOAD PDFs
	# ============================================================================

	with tab_upload:
	st.header("📤 Upload PDF Documents")

	col1, col2 = st.columns([3, 1])

	with col1:
	st.markdown("Upload your PDF files below. They will be automatically processed and stored.")

	# File uploader
	uploaded_files = st.file_uploader(
	"Choose PDF files",
	type=["pdf"],
	accept_multiple_files=True,
	help="You can upload multiple PDF files at once"
	)

	with col2:
	st.info(f"📊 Documents in DB: {chroma_manager.get_collection_info()['document_count']}")

	# Process uploaded files
	if uploaded_files:
	st.divider()
	st.subheader("Processing Uploaded Files")

	# Create a temporary directory for uploads
	temp_dir = tempfile.mkdtemp()

	progress_bar = st.progress(0)
	status_text = st.empty()
	results_container = st.container()

	total_files = len(uploaded_files)
	processed_files = []
	failed_files = []

	for idx, uploaded_file in enumerate(uploaded_files):
	try:
	# Update progress
	status_text.text(f"Processing {idx + 1}/{total_files}: {uploaded_file.name}")

	# Save uploaded file to temp directory
	temp_file_path = os.path.join(temp_dir, uploaded_file.name)
	with open(temp_file_path, "wb") as f:
	f.write(uploaded_file.getbuffer())

	# Process PDF
	with st.spinner(f"Extracting content from {uploaded_file.name}..."):
	try:
	result = pdf_parser.process_pdf(temp_file_path)

	# Add to ChromaDB
	chroma_manager.add_documents([result])

	processed_files.append({
	'name': uploaded_file.name,
	'size': uploaded_file.size,
	'text_length': len(result.get('text', '')),
	'tables': len(result.get('tables', [])),
	'images': len(result.get('images', []))
	})

	st.success(f"✅ {uploaded_file.name} processed successfully")

	except Exception as e:
	failed_files.append({
	'name': uploaded_file.name,
	'error': str(e)
	})
	st.error(f"❌ Error processing {uploaded_file.name}: {e}")

	# Update progress
	progress_bar.progress((idx + 1) / total_files)

	except Exception as e:
	failed_files.append({
	'name': uploaded_file.name,
	'error': str(e)
	})
	st.error(f"❌ Error with {uploaded_file.name}: {e}")

	# Show summary
	st.divider()
	st.subheader("Upload Summary")

	col1, col2, col3 = st.columns(3)

	with col1:
	st.metric("Successfully Processed", len(processed_files))

	with col2:
	st.metric("Failed", len(failed_files))

	with col3:
	collection_info = chroma_manager.get_collection_info()
	st.metric("Total in Database", collection_info['document_count'])

	# Show details of processed files
	if processed_files:
	st.markdown("#### ✅ Processed Files:")
	for file_info in processed_files:
	col1, col2, col3, col4 = st.columns(4)
	with col1:
	st.text(file_info['name'])
	with col2:
	st.text(f"{file_info['size'] / 1024:.1f} KB")
	with col3:
	st.text(f"{file_info['text_length']:,} chars")
	with col4:
	st.text(f"{file_info['tables']} tables, {file_info['images']} imgs")

	# Show failed files
	if failed_files:
	st.markdown("#### ❌ Failed Files:")
	for file_info in failed_files:
	st.error(f"{file_info['name']}: {file_info['error']}")

	# ============================================================================
	# TAB 2: ASK QUESTIONS
	# ============================================================================

	with tab_query:
	st.header("🔍 Ask Questions About Your Documents")

	collection_info = chroma_manager.get_collection_info()

	if collection_info['document_count'] == 0:
	st.warning("⚠️ No documents uploaded yet. Please upload PDFs in the 'Upload PDFs' tab first.")
	else:
	st.success(f"✅ {collection_info['document_count']} documents in database")

	col1, col2, col3 = st.columns([2, 1, 1])

	with col1:
	query = st.text_input(
	"Enter your question:",
	placeholder="Например: Какие ключевые моменты описаны в документе?",
	help="Ask any question about your uploaded documents"
	)

	with col2:
	n_docs = st.number_input("Retrieved docs:", value=3, min_value=1, max_value=10)

	with col3:
	max_tokens = st.number_input("Max tokens:", value=256, min_value=128, max_value=512, step=128)

	if st.button("🚀 Get Answer", use_container_width=True, type="primary"):
	if not query:
	st.warning("⚠️ Please enter a question.")
	else:
	try:
	with st.spinner("🤖 Generating answer... (this may take 10-30 seconds)"):
	st.info("Processing query - please wait...")

	# Generate answer with error handling
	try:
	result = rag_pipeline.answer_question(
	query=query,
	n_retrieved=n_docs,
	max_new_tokens=max_tokens
	)

	# Check for errors
	if "error" in result and result["error"]:
	st.error(f"⚠️ {result['error']}")

	# Display answer
	st.success("✅ Answer Generated")
	st.markdown("### Answer")
	st.write(result['answer'])

	# Display retrieved documents
	with st.expander("📚 Retrieved Documents", expanded=False):
	st.markdown(f"#### {result['doc_count']} Relevant Document Chunks:")
	for idx, doc in enumerate(result['retrieved_docs'], 1):
	with st.container():
	col_rel, col_score = st.columns([3, 1])
	with col_rel:
	st.markdown(f"Document {idx}")
	with col_score:
	st.caption(f"Score: {doc['relevance_score']:.1%}")

	# Truncate for display
	preview = doc['document'][:400]
	if len(doc['document']) > 400:
	preview += "..."
	st.write(preview)

	if doc['metadata']:
	st.caption(f"Source: {doc['metadata'].get('filename', 'Unknown')}")

	except Exception as e:
	st.error(f"❌ Error during generation: {e}")
	st.info("Possible causes:")
	st.write("- Out of memory (try reducing 'Max tokens' or 'Retrieved docs')")
	st.write("- Model inference timeout")
	st.write("- Invalid input format")

	except Exception as e:
	st.error(f"❌ Unexpected error: {e}")

	# ============================================================================
	# TAB 3: MANAGE DATABASE
	# ============================================================================

	with tab_manage:
	st.header("🛠️ Database Management")

	col1, col2, col3 = st.columns(3)

	with col1:
	if st.button("ℹ️ Database Info", use_container_width=True):
	try:
	info = chroma_manager.get_collection_info()
	st.json(info)
	except Exception as e:
	st.error(f"Error: {e}")

	with col2:
	if st.button("📋 List Documents", use_container_width=True):
	try:
	all_docs = chroma_manager.collection.get(include=['documents'])
	if all_docs['ids']:
	st.write(f"Total documents: {len(all_docs['ids'])}")

	col1_list, col2_list = st.columns(2)

	with col1_list:
	st.write("First 10:")
	for idx, doc_id in enumerate(all_docs['ids'][:10], 1):
	st.write(f"{idx}. {doc_id[:50]}...")

	with col2_list:
	if len(all_docs['ids']) > 10:
	st.write(f"... and {len(all_docs['ids']) - 10} more")
	else:
	st.info("No documents in database")
	except Exception as e:
	st.error(f"Error: {e}")

	with col3:
	if st.button("🗑️ Clear Database", use_container_width=True):
	try:
	collection_info = chroma_manager.get_collection_info()
	if collection_info['document_count'] > 0:
	chroma_manager.clear_collection()
	st.success("✅ Database cleared!")
	st.rerun()
	else:
	st.info("Database is already empty")
	except Exception as e:
	st.error(f"Error: {e}")

	st.divider()

	st.markdown("### Storage Information")
	col1, col2 = st.columns(2)

	with col1:
	extraction_size = sum(
	os.path.getsize(os.path.join(dirpath, filename))
	for dirpath, dirnames, filenames in os.walk("./pdf_extractions")
	for filename in filenames
	) / (1024 * 1024) if os.path.exists("./pdf_extractions") else 0
	st.metric("PDF Extractions", f"{extraction_size:.1f} MB")

	with col2:
	chroma_size = sum(
	os.path.getsize(os.path.join(dirpath, filename))
	for dirpath, dirnames, filenames in os.walk("./chroma_db")
	for filename in filenames
	) / (1024 * 1024) if os.path.exists("./chroma_db") else 0
	st.metric("ChromaDB Storage", f"{chroma_size:.1f} MB")

	# ============================================================================
	# TAB 4: ABOUT
	# ============================================================================

	with tab_about:
	st.header("ℹ️ About This System")

	st.markdown("""
	### Multimodal RAG System with PDF Upload

	This is a local, privacy-first AI document analysis system that allows you to:

	#### ✨ Features
	- 📤 Easy PDF Upload: Drag & drop or select multiple PDF files
	- 🔍 Smart Search: Semantic search across documents with CLIP embeddings
	- 🤖 AI-Powered Answers: Ask questions and get answers from Qwen2.5-VL-3B
	- 🌐 Russian & English: Full support for both languages
	- 💾 Local Storage: All data stays on your machine
	- ⚡ Fast Processing: Automatic caching to avoid re-processing

	#### 🏗️ How It Works
	1. Upload PDF documents
	2. System extracts text, tables, and images
	3. Content is embedded with CLIP and stored in ChromaDB
	4. Ask questions about your documents
	5. AI retrieves relevant sections and generates answers

	#### 🔐 Privacy & Security
	- ✅ All processing happens locally
	- ✅ No internet required (after model download)
	- ✅ No cloud APIs used
	- ✅ Full data control
	- ✅ Open-source code

	#### 💻 Technology Stack
	- LLM: Qwen2.5-VL-3B (multimodal)
	- Embeddings: CLIP (clip-vit-base-patch32)
	- Vector DB: ChromaDB
	- UI: Streamlit
	- PDF Processing: pdfplumber + PyMuPDF

	#### 📊 System Info
	""")

	col1, col2, col3 = st.columns(3)

	with col1:
	device_name = "GPU (CUDA)" if torch.cuda.is_available() else "CPU"
	st.metric("Device", device_name)

	with col2:
	collection_info = chroma_manager.get_collection_info()
	st.metric("Documents in DB", collection_info['document_count'])

	with col3:
	st.metric("Version", "1.2 (Upload)")

	st.divider()

	st.markdown("""
	#### 📝 How to Use

	1. Upload PDFs: Go to the "Upload PDFs" tab and select your files
	2. Wait for Processing: System automatically extracts content
	3. Ask Questions: Switch to "Ask Questions" tab and type your query
	4. Review Results: See generated answers and relevant document chunks
	5. Manage: Use "Manage" tab to view or clear database

	#### ⚙️ Tips for Best Results
	- Start with smaller PDFs to test
	- Ask specific questions for better answers
	- Reduce "Retrieved docs" if responses are slow
	- Use Russian for Russian documents (better accuracy)

	#### 🔧 Performance Tuning
	- Slow responses: Reduce "Max tokens" from 512 to 256
	- Out of memory: Use fewer "Retrieved docs" (1-3)
	- Better quality: Increase "Max tokens" to 512

	#### ❓ Troubleshooting
	- App closes: Reduce "Max tokens" and "Retrieved docs"
	- Slow processing: First upload takes time (model loading)
	- Memory issues: Use CPU mode (edit in sidebar)
	""")

	# ============================================================================
	# FOOTER
	# ============================================================================

	st.divider()
	st.markdown("""
	<div style='text-align: center; color: #666; font-size: 0.9rem;'>
	Multimodal RAG System with PDF Upload \| Qwen2.5-VL + ChromaDB + Streamlit \| v1.2
	</div>
	""", unsafe_allow_html=True)