app.py

"""
Intelligent Audit Report Chatbot UI
"""

import os
import warnings

# Silence Streamlit deprecation warnings (use_column_width -> use_container_width)
warnings.filterwarnings("ignore", message=".*use_column_width.*")
warnings.filterwarnings("ignore", category=DeprecationWarning, module="streamlit")

import time
import json
import uuid
import logging
import traceback
from pathlib import Path

from collections import Counter
from typing import List, Dict, Any, Optional


import pandas as pd
import streamlit as st
import plotly.express as px
from langchain_core.messages import HumanMessage, AIMessage


from src.agents import (
    get_multi_agent_chatbot,
    get_smart_chatbot,
    get_gemini_chatbot,
    get_visual_chatbot,
    get_visual_multi_agent_chatbot
)
from src.feedback import FeedbackManager
from src.ui_components import (
    get_custom_css, 
    display_chunk_statistics_charts, 
    display_chunk_statistics_table, 
    extract_chunk_statistics,
    display_visual_search_results
)

from src.config.paths import (
    IS_DEPLOYED,
    PROJECT_DIR,
    HF_CACHE_DIR,
    FEEDBACK_DIR,
    CONVERSATIONS_DIR,
)


# ===== CRITICAL: Fix OMP_NUM_THREADS FIRST, before ANY other imports =====
# Some libraries load at import time and will fail if OMP_NUM_THREADS is invalid
omp_threads = os.environ.get("OMP_NUM_THREADS", "")
try:
    if omp_threads:
        # Handle invalid formats like "3500m" by extracting just the number
        # Remove any non-numeric suffix and convert to int
        cleaned = ''.join(filter(str.isdigit, omp_threads))
        if cleaned:
            threads = int(cleaned)
            if threads <= 0:
                os.environ["OMP_NUM_THREADS"] = "1"
            else:
                # Set the cleaned integer value back
                os.environ["OMP_NUM_THREADS"] = str(threads)
        else:
            os.environ["OMP_NUM_THREADS"] = "1"
    else:
        os.environ["OMP_NUM_THREADS"] = "1"
except (ValueError, TypeError):
    os.environ["OMP_NUM_THREADS"] = "1"

# ===== Setup HuggingFace cache directories BEFORE any model imports =====
# CRITICAL: Set these before any imports that might use HuggingFace (like sentence-transformers)
# Only override cache directories in deployed environment (local uses defaults)
if IS_DEPLOYED and HF_CACHE_DIR:
    cache_dir = str(HF_CACHE_DIR)
    os.environ["HF_HOME"] = cache_dir
    os.environ["TRANSFORMERS_CACHE"] = cache_dir
    os.environ["HF_DATASETS_CACHE"] = cache_dir
    os.environ["HF_HUB_CACHE"] = cache_dir
    os.environ["SENTENCE_TRANSFORMERS_HOME"] = cache_dir
    
    # Ensure cache directory exists (created in Dockerfile, but ensure it's there)
    try:
        os.makedirs(cache_dir, mode=0o755, exist_ok=True)
    except (PermissionError, OSError):
        # If we can't create it, log but continue (might already exist from Dockerfile)
        pass
        
else:
    from dotenv import load_dotenv
    load_dotenv()

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Log environment setup for debugging
# Informational logs (commented out to reduce noise)
# logger.info(f"📁 PROJECT_DIR: {PROJECT_DIR}")
# logger.info(f"🌍 Environment: {'DEPLOYED' if IS_DEPLOYED else 'LOCAL'}")
# logger.info(f"🔧 OMP_NUM_THREADS: {os.environ.get('OMP_NUM_THREADS', 'NOT SET')}")
# logger.info(f"📁 HuggingFace cache: {os.environ.get('HF_HOME', 'DEFAULT (not overridden)')}")


# Page config
st.set_page_config(
    layout="wide",
    page_icon="🤖",
    initial_sidebar_state="expanded",
    page_title="Intelligent Audit Report Chatbot"
)


# GPU check - only log once at startup
import torch, sys
if "gpu_check" not in st.session_state:
    try:
        cuda_ = torch.cuda.is_available()
        mps_ = torch.backends.mps.is_available() if hasattr(torch.backends, 'mps') else False
        if cuda_:
            print(f"🎮 CUDA available: {torch.cuda.get_device_name(0)}")
        elif mps_:
            print("🍎 MPS (Apple Silicon) available")
        else:
            print("💻 CPU only (no GPU acceleration)")
    except Exception as e:
        print(f"⚠️ GPU check error: {e}", file=sys.stderr)
    finally:
        st.session_state.gpu_check = True
            

st.markdown(get_custom_css(), unsafe_allow_html=True)


def get_system_type():
    """Get the current system type"""
    system = os.environ.get('CHATBOT_SYSTEM', 'multi-agent')
    if system == 'smart':
        return "Smart Chatbot System"
    else:
        return "Multi-Agent System"

def get_chatbot(version: str = "v1"):
    """Initialize and return the chatbot based on version"""
    if version == "beta":
        return get_gemini_chatbot()
    elif version == "visual":
        # Use multi-agent architecture for visual mode (same sophisticated logic as v1)
        return get_visual_multi_agent_chatbot()
    else:
        # Check environment variable for system type (v1)
        system = os.environ.get('CHATBOT_SYSTEM', 'multi-agent')
        if system == 'smart':
            return get_smart_chatbot()
        else:
            return get_multi_agent_chatbot()

def serialize_messages(messages):
    """Serialize LangChain messages to dictionaries"""
    serialized = []
    for msg in messages:
        if hasattr(msg, 'content'):
            serialized.append({
                "type": type(msg).__name__,
                "content": str(msg.content)
            })
    return serialized

def serialize_documents(sources):
    """Serialize document objects to dictionaries with deduplication"""
    serialized = []
    seen_content = set()
    
    for doc in sources:
        content = getattr(doc, 'page_content', getattr(doc, 'content', ''))
        
        # Skip if we've seen this exact content before
        if content in seen_content:
            continue
            
        seen_content.add(content)
        
        doc_dict = {
            "content": content,
            "metadata": getattr(doc, 'metadata', {}),
            "score": getattr(doc, 'metadata', {}).get('reranked_score', getattr(doc, 'metadata', {}).get('original_score', 0.0)),
            "id": getattr(doc, 'metadata', {}).get('_id', 'unknown'),
            "source": getattr(doc, 'metadata', {}).get('source', 'unknown'),
            "year": getattr(doc, 'metadata', {}).get('year', 'unknown'),
            "district": getattr(doc, 'metadata', {}).get('district', 'unknown'),
            "page": getattr(doc, 'metadata', {}).get('page', 'unknown'),
            "chunk_id": getattr(doc, 'metadata', {}).get('chunk_id', 'unknown'),
            "page_label": getattr(doc, 'metadata', {}).get('page_label', 'unknown'),
            "original_score": getattr(doc, 'metadata', {}).get('original_score', 0.0),
            "reranked_score": getattr(doc, 'metadata', {}).get('reranked_score', None)
        }
        serialized.append(doc_dict)
    
    return serialized


feedback_manager = FeedbackManager()


@st.cache_data
def load_filter_options():
    try:
        filter_options_path = PROJECT_DIR / "src" / "config" / "filter_options.json"
        with open(filter_options_path, "r") as f:
            return json.load(f)
    except FileNotFoundError:
        st.info(f"Looking for filter_options.json in: {PROJECT_DIR / 'src' / 'config'}")
        st.error("filter_options.json not found. Please run the metadata analysis script.")
        return {"sources": [], "years": [], "districts": [], 'filenames': []}

def main():
    # Initialize session state
    if 'messages' not in st.session_state:
        st.session_state.messages = []
    if 'conversation_id' not in st.session_state:
        st.session_state.conversation_id = f"session_{uuid.uuid4().hex[:8]}"
    if 'session_start_time' not in st.session_state:
        st.session_state.session_start_time = time.time()
    if 'active_filters' not in st.session_state:
        st.session_state.active_filters = {'sources': [], 'years': [], 'districts': [], 'filenames': []}
    # Track RAG retrieval history for feedback
    if 'rag_retrieval_history' not in st.session_state:
        st.session_state.rag_retrieval_history = []
    # Version selection (v1, beta, or visual)
    if 'chatbot_version' not in st.session_state:
        st.session_state.chatbot_version = "v1"
    
    # Initialize chatbot based on version (only if not already initialized for this version)
    chatbot_version_key = f"chatbot_{st.session_state.chatbot_version}"
    
    # Check if we need to initialize: chatbot doesn't exist OR version changed
    needs_init = (
        chatbot_version_key not in st.session_state or 
        st.session_state.get('_last_version') != st.session_state.chatbot_version
    )
    
    if needs_init:
        try:
            # Different spinner messages for different versions
            if st.session_state.chatbot_version == "beta":
                spinner_msg = "🔄 Initializing Gemini FSA..."
            elif st.session_state.chatbot_version == "visual":
                spinner_msg = "🎨 Initializing Visual Search ... This may take 20-30 seconds..."
            else:
                spinner_msg = "🔄 Loading AI models and connecting to database..."
            
            with st.spinner(spinner_msg):
                st.session_state[chatbot_version_key] = get_chatbot(st.session_state.chatbot_version)
                st.session_state['_last_version'] = st.session_state.chatbot_version
                st.session_state.chatbot = st.session_state[chatbot_version_key]
            print("✅ AI system ready!")
        except Exception as e:
            st.error(f"❌ Failed to initialize chatbot: {str(e)}")
            # Show version-specific error messages
            if st.session_state.chatbot_version == "beta":
                st.error("Please check your environment variables (GEMINI_API_KEY, GEMINI_FILESTORE_NAME for beta)")
            elif st.session_state.chatbot_version == "visual":
                st.error("Please check your environment variables (QDRANT_URL, QDRANT_API_KEY, OPENAI_API_KEY for visual)")
                with st.expander("🐛 Debug Info"):
                    import traceback
                    st.code(traceback.format_exc())
            else:
                st.error("Please check your configuration and ensure all required models and databases are accessible.")
            # Reset to v1 to prevent infinite loop
            st.session_state.chatbot_version = "v1"
            st.session_state['_last_version'] = "v1"
            if 'chatbot' in st.session_state:
                del st.session_state['chatbot']
            st.stop()  # Stop execution to prevent infinite loop
    else:
        # Chatbot already initialized for this version, just use it
        st.session_state.chatbot = st.session_state[chatbot_version_key]
    
    # Reset conversation history if needed (but keep chatbot cached)
    if 'reset_conversation' in st.session_state and st.session_state.reset_conversation:
        st.session_state.messages = []
        st.session_state.conversation_id = f"session_{uuid.uuid4().hex[:8]}"
        st.session_state.session_start_time = time.time()
        st.session_state.rag_retrieval_history = []
        st.session_state.feedback_submitted = False
        st.session_state.reset_conversation = False
        st.rerun()


    # Version selection radio button (top right)
    col1, col2 = st.columns([3, 1])
    with col1:
        st.markdown('<p class="subtitle">Ask questions about audit reports. Use the sidebar filters to narrow down your search!</p>', unsafe_allow_html=True)
    with col2:
        st.markdown("<br>", unsafe_allow_html=True)  # Add some spacing
        selected_version = st.radio(
            "**Version:**",
            options=["v1", "visual", "beta"],
            index=0 if st.session_state.chatbot_version == "v1" else (1 if st.session_state.chatbot_version == "visual" else 2),
            horizontal=True,
            key="version_selector",
            help="Select v1 (default RAG), visual (ColPali visual search), or beta (Gemini FSA)"
        )
        
        # Update version if changed
        if selected_version != st.session_state.chatbot_version:
            # Store the old version to check if we need to switch
            old_version = st.session_state.chatbot_version
            st.session_state.chatbot_version = selected_version
            
            # If chatbot for new version already exists, just switch to it
            new_chatbot_key = f"chatbot_{selected_version}"
            if new_chatbot_key in st.session_state:
                # Chatbot already exists, just switch
                st.session_state.chatbot = st.session_state[new_chatbot_key]
                st.session_state['_last_version'] = selected_version
            else:
                # Need to initialize new version - will be handled by initialization logic above
                st.session_state['_last_version'] = old_version  # Set to old to trigger init check
            
            st.rerun()
    
    # Show version info
    if st.session_state.chatbot_version == "beta":
        st.info("🔬 **Beta Mode**: Using Google Gemini FSA")
    elif st.session_state.chatbot_version == "visual":
        st.info("🎨 **Visual Mode**: Using Visual Search (Multi-Modal Embeddings)")
    
    # Session info
    duration = int(time.time() - st.session_state.session_start_time)
    duration_str = f"{duration // 60}m {duration % 60}s"
    st.markdown(f'''
    <div class="session-info">
        <strong>Session Info:</strong> Messages: {len(st.session_state.messages)} | Duration: {duration_str} | Status: Active | ID: {st.session_state.conversation_id}
    </div>
    ''', unsafe_allow_html=True)

    # Load filter options
    filter_options = load_filter_options()

    # Sidebar for filters
    with st.sidebar:
        # Instructions section (collapsible)
        with st.expander("📖 How to Use", expanded=True):
            st.markdown("""
            #### 🎯 Using Filters
            
            1. **Select filters** from the sidebar to narrow your search:
            
            2. **Leave filters empty** to search across all data
            
            3. **Type your question** in the chat input at the bottom
            
            4. **Click "Send"** to submit your question
            
            #### 💡 Tips
            
            - Use specific questions for better results
            - Combine multiple filters for precise searches
            - Check the "Retrieved Documents" tab to see source material
            
            #### ⚠️ Important
            
            **When finished, please close the browser window** to free up computational resources.
            
            ---
            
            For more detailed help, see the example questions at the bottom of the page.
            """)
        
        # Filters in a collapsed expander by default
        with st.expander("🔍 Search Filters", expanded=False):
            st.caption("Select filters to narrow down your search. Leave empty to search all data.")
            
            st.markdown('<div class="filter-section">', unsafe_allow_html=True)
            st.markdown('<div class="filter-title">📄 Specific Reports (Filename Filter)</div>', unsafe_allow_html=True)
            st.markdown('<p style="font-size: 0.85em; color: #666;">⚠️ Selecting specific reports will ignore all other filters</p>', unsafe_allow_html=True)
            selected_filenames = st.multiselect(
                    "Select specific reports:",
                    options=filter_options.get('filenames', []),
                    default=st.session_state.active_filters.get('filenames', []),
                    key="filenames_filter",
                    help="Choose specific reports to search. When enabled, all other filters are ignored."
                )
            st.markdown('</div>', unsafe_allow_html=True)
            
            # Determine if filename filter is active
            filename_mode = len(selected_filenames) > 0
            
            # Sources filter
            st.markdown('<div class="filter-title">📊 Sources</div>', unsafe_allow_html=True)
            selected_sources = st.multiselect(
                "Select sources:",
                options=filter_options['sources'],
                default=st.session_state.active_filters['sources'],
                disabled = filename_mode,
                key="sources_filter",
                help="Choose which types of reports to search"
            )
            st.markdown('</div>', unsafe_allow_html=True)
            
            # Years filter
            st.markdown('<div class="filter-title">📅 Years</div>', unsafe_allow_html=True)
            selected_years = st.multiselect(
                "Select years:",
                options=filter_options['years'],
                default=st.session_state.active_filters['years'],
                disabled = filename_mode,
                key="years_filter",
                help="Choose which years to search"
            )
            st.markdown('</div>', unsafe_allow_html=True)
            
            # Districts filter
            st.markdown('<div class="filter-title">🏘️ Districts</div>', unsafe_allow_html=True)
            selected_districts = st.multiselect(
                "Select districts:",
                options=filter_options['districts'],
                default=st.session_state.active_filters['districts'],
                disabled = filename_mode,
                key="districts_filter",
                help="Choose which districts to search"
            )
            st.markdown('</div>', unsafe_allow_html=True)
            
            # Clear filters button
            if st.button("🗑️ Clear All Filters", key="clear_filters_button"):
                st.session_state.active_filters = {'sources': [], 'years': [], 'districts': [], 'filenames': []}
                st.rerun()
        
        # Update active filters (outside expander so it always runs)
        st.session_state.active_filters = {
            'sources': selected_sources if not filename_mode else [],
            'years': selected_years if not filename_mode else [],
            'districts': selected_districts if not filename_mode else [],
            'filenames': selected_filenames
        }
        
        # Saliency settings (only for visual mode)
        if st.session_state.chatbot_version == "visual":
            with st.expander("🔥 Saliency Maps", expanded=False):
                st.caption("Visualize which image regions are relevant to your query")
                
                show_saliency = st.checkbox(
                    "Enable Saliency Maps",
                    value=st.session_state.get('show_saliency', False),
                    key="saliency_toggle",
                    help="Generate heatmaps showing which parts of each document are most relevant"
                )
                st.session_state.show_saliency = show_saliency
                
                if show_saliency:
                    # Colormap selection (hot is default)
                    colormap_options = ["hot", "jet", "viridis", "plasma", "coolwarm", "RdYlGn"]
                    saliency_colormap = st.selectbox(
                        "Colormap",
                        options=colormap_options,
                        index=colormap_options.index(st.session_state.get('saliency_colormap', 'hot')),
                        key="saliency_colormap_select",
                        help="Color scheme for the heatmap. 'hot' recommended for visibility."
                    )
                    st.session_state.saliency_colormap = saliency_colormap
                    
                    saliency_alpha = st.slider(
                        "Overlay Transparency",
                        min_value=0.1,
                        max_value=0.8,
                        value=st.session_state.get('saliency_alpha', 0.4),
                        step=0.1,
                        key="saliency_alpha_slider",
                        help="0.1 = subtle, 0.8 = intense"
                    )
                    st.session_state.saliency_alpha = saliency_alpha
                    
                    saliency_threshold = st.slider(
                        "Threshold (%)",
                        min_value=0,
                        max_value=80,
                        value=st.session_state.get('saliency_threshold', 50),
                        step=10,
                        key="saliency_threshold_slider",
                        help="Hide patches below this percentile"
                    )
                    st.session_state.saliency_threshold = saliency_threshold

    # Main content area with tabs
    tab1, tab2 = st.tabs(["💬 Chat", "📄 Retrieved Documents"])
    
    with tab1:
        # Chat container
        chat_container = st.container()
        
        with chat_container:
            # Display conversation history
            for message in st.session_state.messages:
                if isinstance(message, HumanMessage):
                    st.markdown(f'<div class="user-message">{message.content}</div>', unsafe_allow_html=True)
                elif isinstance(message, AIMessage):
                    st.markdown(f'<div class="bot-message">{message.content}</div>', unsafe_allow_html=True)
        
        # Input area
        st.markdown("<br>", unsafe_allow_html=True)
        
        # Create two columns for input and button
        col1, col2 = st.columns([4, 1])
        
        with col1:
            # Use a counter to force input clearing
            if 'input_counter' not in st.session_state:
                st.session_state.input_counter = 0
            
            # Handle pending question from example questions section
            if 'pending_question' in st.session_state and st.session_state.pending_question:
                default_value = st.session_state.pending_question
                # Increment counter to force new input widget
                st.session_state.input_counter = (st.session_state.get('input_counter', 0) + 1) % 1000
                del st.session_state.pending_question
                key_suffix = st.session_state.input_counter
            else:
                default_value = ""
                key_suffix = st.session_state.input_counter
            
            user_input = st.text_input(
                "Type your message here...",
                placeholder="Ask about budget allocations, expenditures, or audit findings...",
                key=f"user_input_{key_suffix}",
                label_visibility="collapsed",
                value=default_value if default_value else None
            )
        
        with col2:
            send_button = st.button("Send", key="send_button", use_container_width=True)
        
        # Clear chat button
        if st.button("🗑️ Clear Chat", key="clear_chat_button"):
            st.session_state.reset_conversation = True
            # Clear all conversation files
            conversations_path = CONVERSATIONS_DIR
            if conversations_path.exists():
                for file in conversations_path.iterdir():
                    if file.suffix == '.json':
                        file.unlink()
            st.rerun()
        
        # Handle user input
        if send_button and user_input:
            # Construct filter context string
            filter_context_str = ""
            if selected_filenames:
                filter_context_str += "FILTER CONTEXT:\n"
                filter_context_str += f"Filenames: {', '.join(selected_filenames)}\n"
                filter_context_str += "USER QUERY:\n"
            elif selected_sources or selected_years or selected_districts:
                filter_context_str += "FILTER CONTEXT:\n"
                if selected_sources:
                    filter_context_str += f"Sources: {', '.join(selected_sources)}\n"
                if selected_years:
                    filter_context_str += f"Years: {', '.join(selected_years)}\n"
                if selected_districts:
                    filter_context_str += f"Districts: {', '.join(selected_districts)}\n"
                filter_context_str += "USER QUERY:\n"
            
            full_query = filter_context_str + user_input
            
            # Add user message to history
            st.session_state.messages.append(HumanMessage(content=user_input))
            
            # Get chatbot response
            with st.spinner("🤔 Thinking..."):
                try:
                    # Pass the full query with filter context
                    chat_result = st.session_state.chatbot.chat(full_query, st.session_state.conversation_id)
                    
                    # Handle both old format (string) and new format (dict)
                    if isinstance(chat_result, dict):
                        response = chat_result['response']
                        rag_result = chat_result.get('rag_result')
                        st.session_state.last_rag_result = rag_result
                        
                        # Track RAG retrieval for feedback
                        if rag_result:
                            sources = rag_result.get('sources', []) if isinstance(rag_result, dict) else (rag_result.sources if hasattr(rag_result, 'sources') else [])
                            
                            # For Gemini, also check gemini_result for sources
                            if not sources or len(sources) == 0:
                                gemini_result = chat_result.get('gemini_result')
                                print(f"🔍 DEBUG: Checking gemini_result for sources...")
                                print(f"   gemini_result exists: {gemini_result is not None}")
                                if gemini_result:
                                    print(f"   gemini_result type: {type(gemini_result)}")
                                    print(f"   has sources attr: {hasattr(gemini_result, 'sources')}")
                                    if hasattr(gemini_result, 'sources'):
                                        print(f"   sources length: {len(gemini_result.sources) if gemini_result.sources else 0}")
                                
                                if gemini_result and hasattr(gemini_result, 'sources'):
                                    # Format Gemini sources for display
                                    if hasattr(st.session_state.chatbot, 'gemini_client'):
                                        sources = st.session_state.chatbot.gemini_client.format_sources_for_display(gemini_result)
                                        print(f"✅ Formatted {len(sources)} sources from gemini_client")
                                    elif hasattr(st.session_state.chatbot, '_format_gemini_sources'):
                                        sources = st.session_state.chatbot._format_gemini_sources(gemini_result)
                                        print(f"✅ Formatted {len(sources)} sources from _format_gemini_sources")
                            
                            # Update rag_result with sources if we found them
                            if sources and len(sources) > 0:
                                if isinstance(rag_result, dict):
                                    rag_result['sources'] = sources
                                elif hasattr(rag_result, 'sources'):
                                    rag_result.sources = sources
                                # Update last_rag_result with sources
                                st.session_state.last_rag_result = rag_result
                                print(f"✅ Updated rag_result with {len(sources)} sources")
                            
                            # Get the actual RAG query
                            actual_rag_query = chat_result.get('actual_rag_query', '')
                            if actual_rag_query:
                                # Format it like the log message
                                timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
                                formatted_query = f"{timestamp} - INFO - 🔍 ACTUAL RAG QUERY: '{actual_rag_query}'"
                            else:
                                formatted_query = "No RAG query available"
                            
                            # Extract filters from active filters
                            filters_used = {
                                "sources": st.session_state.active_filters.get('sources', []),
                                "years": st.session_state.active_filters.get('years', []),
                                "districts": st.session_state.active_filters.get('districts', []),
                                "filenames": st.session_state.active_filters.get('filenames', [])
                            }
                            
                            retrieval_entry = {
                                "conversation_up_to": serialize_messages(st.session_state.messages),
                                "rag_query_expansion": formatted_query,
                                "docs_retrieved": serialize_documents(sources),
                                "filters_applied": filters_used,
                                "timestamp": time.time()
                            }
                            st.session_state.rag_retrieval_history.append(retrieval_entry)
                            
                            # Debug logging
                            print(f"📊 RETRIEVAL TRACKING: {len(sources)} sources stored in retrieval history")
                    else:
                        response = chat_result
                        st.session_state.last_rag_result = None
                    
                    # Add bot response to history
                    st.session_state.messages.append(AIMessage(content=response))
                    
                except Exception as e:
                    error_msg = f"Sorry, I encountered an error: {str(e)}"
                    st.session_state.messages.append(AIMessage(content=error_msg))
            
            # Clear input and rerun
            st.session_state.input_counter += 1  # This will clear the input
            st.rerun()
    
    with tab2:
        # Document retrieval panel
        if hasattr(st.session_state, 'last_rag_result') and st.session_state.last_rag_result:
            rag_result = st.session_state.last_rag_result
            
            # Handle both PipelineResult object and dictionary formats
            sources = None
            if hasattr(rag_result, 'sources'):
                # PipelineResult object format
                sources = rag_result.sources
            elif isinstance(rag_result, dict) and 'sources' in rag_result:
                # Dictionary format from multi-agent system or visual search
                sources = rag_result['sources']
            
            # For Gemini, also check if we need to format sources from gemini_result
            if (not sources or len(sources) == 0) and isinstance(rag_result, dict):
                gemini_result = rag_result.get('gemini_result')
                if gemini_result and hasattr(gemini_result, 'sources'):
                    # Format Gemini sources for display
                    if hasattr(st.session_state.chatbot, 'gemini_client'):
                        sources = st.session_state.chatbot.gemini_client.format_sources_for_display(gemini_result)
                    elif hasattr(st.session_state.chatbot, '_format_gemini_sources'):
                        sources = st.session_state.chatbot._format_gemini_sources(gemini_result)
            
            # Check if this is visual search results (has visual metadata)
            is_visual_search = False
            if sources and len(sources) > 0:
                first_doc_metadata = getattr(sources[0], 'metadata', {})
                is_visual_search = 'num_tiles' in first_doc_metadata or 'num_visual_tokens' in first_doc_metadata
            
            if sources and len(sources) > 0:
                # Use visual display for visual search results
                if is_visual_search and st.session_state.chatbot_version == "visual":
                    st.markdown("### 🎨 Visual Search Results")
                    
                    # Get saliency settings from session state
                    show_saliency = st.session_state.get('show_saliency', False)
                    saliency_alpha = st.session_state.get('saliency_alpha', 0.4)
                    saliency_threshold = st.session_state.get('saliency_threshold', 50)
                    saliency_colormap = st.session_state.get('saliency_colormap', 'hot')
                    
                    # Get Qdrant client and query embedding for saliency
                    qdrant_client = None
                    collection_name = None
                    query_embedding = None
                    
                    if show_saliency:
                        try:
                            # Access the visual search adapter from the chatbot
                            chatbot = st.session_state.get('chatbot')
                            if chatbot and hasattr(chatbot, 'visual_search'):
                                visual_search = chatbot.visual_search
                                qdrant_client = visual_search.client
                                collection_name = visual_search.collection_name
                                query_embedding = visual_search.last_query_embedding
                                
                                if query_embedding is None:
                                    st.warning("⚠️ Query embedding not available for saliency")
                                    show_saliency = False
                                else:
                                    logger.info(f"✅ Saliency enabled: colormap={saliency_colormap}, alpha={saliency_alpha}, threshold={saliency_threshold}")
                        except Exception as e:
                            logger.error(f"Failed to get saliency requirements: {e}")
                            st.warning(f"⚠️ Saliency unavailable: {str(e)[:50]}")
                            show_saliency = False
                    
                    # Extract statistics for charts (same as v1)
                    stats = extract_chunk_statistics(sources)
                    
                    # Show charts for visual RAG too (like v1)
                    if len(sources) >= 5:
                        display_chunk_statistics_charts(stats, "Retrieval Statistics")
                        st.markdown("---")
                    
                    display_visual_search_results(
                        sources=sources,
                        show_statistics=True,
                        show_images=True,  # Show Cloudinary images
                        show_saliency=show_saliency,
                        qdrant_client=qdrant_client,
                        collection_name=collection_name,
                        query_embedding=query_embedding,
                        saliency_alpha=saliency_alpha,
                        saliency_colormap=saliency_colormap,  # Use selected colormap
                        saliency_threshold=saliency_threshold,
                        max_display=20
                    )
                else:
                    # Standard display for v1/beta results
                    # Count unique filenames
                    unique_filenames = set()
                    for doc in sources:
                        filename = getattr(doc, 'metadata', {}).get('filename', 'Unknown')
                        unique_filenames.add(filename)
                    
                    st.markdown(f"**Found {len(sources)} document chunks from {len(unique_filenames)} unique documents (showing top 20):**")
                    if len(unique_filenames) < len(sources):
                        st.info(f"💡 **Note**: Each document is split into multiple chunks. You're seeing {len(sources)} chunks from {len(unique_filenames)} documents.")
                    
                    # Extract and display statistics
                    stats = extract_chunk_statistics(sources)
                    
                    # Show charts for 10+ results, tables for fewer
                    if len(sources) >= 10:
                        display_chunk_statistics_charts(stats, "Retrieval Statistics")
                        # Also show tables below charts for detailed view
                        st.markdown("---")
                        display_chunk_statistics_table(stats, "Retrieval Distribution")
                    else:
                        display_chunk_statistics_table(stats, "Retrieval Distribution")
                    
                    st.markdown("---")
                    st.markdown("### 📄 Document Details")
                    
                    for i, doc in enumerate(sources):  # Show all documents
                        # Get relevance score and ID if available
                        metadata = getattr(doc, 'metadata', {})
                        # Handle both standard RAG scores and Gemini scores
                        score = metadata.get('reranked_score') or metadata.get('original_score') or metadata.get('score')
                        chunk_id = metadata.get('_id') or metadata.get('chunk_id', 'Unknown')
                        if score is not None:
                            try:
                                score_text = f" (Score: {float(score):.3f})"
                            except (ValueError, TypeError):
                                score_text = ""
                        else:
                            score_text = ""
                        if chunk_id and chunk_id != 'Unknown':
                            score_text += f" (ID: {str(chunk_id)[:8]}...)" if score_text else f" (ID: {str(chunk_id)[:8]}...)"
                        
                        with st.expander(f"📄 Document {i+1}: {getattr(doc, 'metadata', {}).get('filename', 'Unknown')[:50]}...{score_text}"):
                            # Display document metadata with emojis
                            metadata = getattr(doc, 'metadata', {})
                            col1, col2, col3, col4 = st.columns([2, 1.5, 1, 1])
                            
                            with col1:
                                st.write(f"📄 **File:** {metadata.get('filename', 'Unknown')}")
                            with col2:
                                st.write(f"🏛️ **Source:** {metadata.get('source', 'Unknown')}")
                            with col3:
                                st.write(f"📅 **Year:** {metadata.get('year', 'Unknown')}")
                            with col4:
                                # Display page number and chunk ID
                                page = metadata.get('page_label', metadata.get('page', 'Unknown'))
                                chunk_id = metadata.get('_id', 'Unknown')
                                st.write(f"📖 **Page:** {page}")
                                st.write(f"🆔 **ID:** {chunk_id}")
                            
                            # Display full content (no truncation)
                            content = getattr(doc, 'page_content', 'No content available')
                            st.write(f"**Full Content:**")
                            st.text_area("Full Content", value=content, height=300, disabled=True, label_visibility="collapsed", key=f"preview_{i}")
            else:
                st.info("No documents were retrieved for the last query.")
        else:
            st.info("No documents have been retrieved yet. Start a conversation to see retrieved documents here.")
    
    # Feedback Dashboard Section
    st.markdown("---")
    st.markdown("### 💬 Feedback Dashboard")
    
    # Check if there's any conversation to provide feedback on
    has_conversation = len(st.session_state.messages) > 0
    has_retrievals = len(st.session_state.rag_retrieval_history) > 0
    
    if not has_conversation:
        st.info("💡 Start a conversation to provide feedback!")
        st.markdown("The feedback dashboard will be enabled once you begin chatting.")
    else:
        st.markdown("Help us improve by providing feedback on this conversation.")
        
        # Initialize feedback state if not exists
        if 'feedback_submitted' not in st.session_state:
            st.session_state.feedback_submitted = False
        
        # Feedback form - only show if feedback not already submitted
        if not st.session_state.feedback_submitted:
            with st.form("feedback_form", clear_on_submit=False):
                col1, col2 = st.columns([1, 1])
                
                with col1:
                    feedback_score = st.slider(
                        "Rate this conversation (1-5)",
                        min_value=1,
                        max_value=5,
                        help="How satisfied are you with the conversation?"
                    )
                
                with col2:
                    is_feedback_about_last_retrieval = st.checkbox(
                        "Feedback about last retrieval only",
                        value=True,
                        help="If checked, feedback applies to the most recent document retrieval"
                    )
                
                open_ended_feedback = st.text_area(
                    "Your feedback (optional)",
                    placeholder="Tell us what went well or what could be improved...",
                    height=100
                )
                
                # Disable submit if no score selected
                submit_disabled = feedback_score is None
                
                submitted = st.form_submit_button(
                    "📤 Submit Feedback", 
                    use_container_width=True,
                    disabled=submit_disabled
                )
                
                if submitted:
                    # Log the feedback data being submitted
                    print("=" * 80)
                    print("🔄 FEEDBACK SUBMISSION: Starting...")
                    print("=" * 80)
                    st.write("🔍 **Debug: Feedback Data Being Submitted:**")
                    
                    # Extract transcript from messages
                    transcript = feedback_manager.extract_transcript(st.session_state.messages)
                    
                    # Build retrievals structure
                    retrievals = feedback_manager.build_retrievals_structure(

                        st.session_state.rag_retrieval_history.copy() if st.session_state.rag_retrieval_history else [],
                        st.session_state.messages
                    )
                    
                    # Build feedback_score_related_retrieval_docs

                    feedback_score_related_retrieval_docs = feedback_manager.build_feedback_score_related_retrieval_docs(
                        is_feedback_about_last_retrieval,
                        st.session_state.messages,
                        st.session_state.rag_retrieval_history.copy() if st.session_state.rag_retrieval_history else []
                    )
                    
                    # Preserve old retrieved_data format for backward compatibility
                    retrieved_data_old_format = st.session_state.rag_retrieval_history.copy() if st.session_state.rag_retrieval_history else []
                    
                    # Create feedback data dictionary
                    feedback_dict = {
                        "open_ended_feedback": open_ended_feedback,
                        "score": feedback_score,
                        "is_feedback_about_last_retrieval": is_feedback_about_last_retrieval,
                        "conversation_id": st.session_state.conversation_id,
                        "timestamp": time.time(),
                        "message_count": len(st.session_state.messages),
                        "has_retrievals": has_retrievals,
                        "retrieval_count": len(st.session_state.rag_retrieval_history) if st.session_state.rag_retrieval_history else 0,
                        "transcript": transcript,
                        "retrievals": retrievals,
                        "feedback_score_related_retrieval_docs": feedback_score_related_retrieval_docs,
                        "retrieved_data": retrieved_data_old_format  # Preserved old column
                    }
                    
                    print(f"📝 FEEDBACK SUBMISSION: Score={feedback_score}, Retrievals={len(st.session_state.rag_retrieval_history) if st.session_state.rag_retrieval_history else 0}")
                    
                    # Create UserFeedback dataclass instance
                    feedback_obj = None  # Initialize outside try block
                    try:
                        feedback_obj = feedback_manager.create_feedback_from_dict(feedback_dict)
                        print(f"✅ FEEDBACK SUBMISSION: Feedback object created - ID={feedback_obj.feedback_id}")
                        st.write(f"✅ **Feedback Object Created**")
                        st.write(f"- Feedback ID: {feedback_obj.feedback_id}")
                        st.write(f"- Score: {feedback_obj.score}/5")
                        st.write(f"- Has Retrievals: {feedback_obj.has_retrievals}")
                        
                        # Convert back to dict for JSON serialization
                        feedback_data = feedback_obj.to_dict()
                    except Exception as e:
                        print(f"❌ FEEDBACK SUBMISSION: Failed to create feedback object: {e}")
                        st.error(f"Failed to create feedback object: {e}")
                        feedback_data = feedback_dict
                    
                    # Display the data being submitted
                    st.json(feedback_data)
                    
                    # Save feedback to file - use PROJECT_DIR to ensure writability
                    feedback_dir = FEEDBACK_DIR
                    try:
                        # Ensure directory exists with write permissions (777 for compatibility)
                        feedback_dir.mkdir(parents=True, mode=0o777, exist_ok=True)
                    except (PermissionError, OSError) as e:
                        logger.warning(f"Could not create feedback directory at {feedback_dir}: {e}")
                        # Fallback to relative path
                        feedback_dir = Path("feedback")
                        feedback_dir.mkdir(parents=True, mode=0o777, exist_ok=True)
                    
                    feedback_file = feedback_dir / f"feedback_{st.session_state.conversation_id}_{int(time.time())}.json"
                    
                    try:
                        # Ensure parent directory exists before writing
                        feedback_file.parent.mkdir(parents=True, mode=0o777, exist_ok=True)
                        
                        # Save to local file first
                        print(f"💾 FEEDBACK SAVE: Saving to local file: {feedback_file}")
                        with open(feedback_file, 'w') as f:
                            json.dump(feedback_data, f, indent=2, default=str)
                        
                        print(f"✅ FEEDBACK SAVE: Local file saved successfully")
                        
                        # Save to Snowflake if enabled and credentials available
                        logger.info("🔄 FEEDBACK SAVE: Starting Snowflake save process...")
                        logger.info(f"📊 FEEDBACK SAVE: feedback_obj={'exists' if feedback_obj else 'None'}")
                        
                        snowflake_success = False
                        try:
                            snowflake_enabled = os.getenv("SNOWFLAKE_ENABLED", "false").lower() == "true"
                            logger.info(f"🔍 SNOWFLAKE CHECK: enabled={snowflake_enabled}")
                            
                            if snowflake_enabled:
                                if feedback_obj:
                                    try:
                                        logger.info("📤 SNOWFLAKE UI: Attempting to save feedback to Snowflake...")
                                        print("📤 SNOWFLAKE UI: Attempting to save feedback to Snowflake...")
                                        
                                        # Show spinner while saving to Snowflake (can take 10-15 seconds)
                                        # This includes: connection establishment (~5s), data preparation, and SQL execution (~5s)
                                        with st.spinner("💾 Saving feedback to Snowflake... This may take 10-15 seconds (connecting to database, preparing data, and executing query)"):
                                            snowflake_success = feedback_manager.save_to_snowflake(feedback_obj)
                                        
                                        if snowflake_success:
                                            logger.info("✅ SNOWFLAKE UI: Successfully saved to Snowflake")
                                            print("✅ SNOWFLAKE UI: Successfully saved to Snowflake")
                                        else:
                                            logger.warning("⚠️ SNOWFLAKE UI: Save failed")
                                            print("⚠️ SNOWFLAKE UI: Save failed")
                                    except Exception as e:
                                        logger.error(f"❌ SNOWFLAKE UI ERROR: {e}")
                                        print(f"❌ SNOWFLAKE UI ERROR: {e}")
                                        traceback.print_exc()
                                        snowflake_success = False
                                else:
                                    logger.warning("⚠️ SNOWFLAKE UI: Skipping (feedback object not created)")
                                    print("⚠️ SNOWFLAKE UI: Skipping (feedback object not created)")
                                    snowflake_success = False
                            else:
                                logger.info("💡 SNOWFLAKE UI: Integration disabled")
                                print("💡 SNOWFLAKE UI: Integration disabled")
                                # If Snowflake is disabled, consider it successful (local save only)
                                snowflake_success = True
                        
                        except Exception as e:
                            logger.error(f"❌ Exception in Snowflake save: {type(e).__name__}: {e}")
                            print(f"❌ Exception in Snowflake save: {type(e).__name__}: {e}")
                            snowflake_success = False
                        
                        # Only show success if Snowflake save succeeded (or if Snowflake is disabled)
                        if snowflake_success:
                            st.success("✅ Thank you for your feedback! It has been saved successfully.")
                            st.balloons()
                        else:
                            st.warning("⚠️ Feedback saved locally, but Snowflake save failed. Please check logs.")
                        
                        # Mark feedback as submitted to prevent resubmission
                        st.session_state.feedback_submitted = True
                        
                        print("=" * 80)
                        print(f"✅ FEEDBACK SUBMISSION: Completed successfully")
                        print("=" * 80)
                        
                        # Log file location
                        st.info(f"📁 Feedback saved to: {feedback_file}")
                        
                    except Exception as e:
                        print(f"❌ FEEDBACK SUBMISSION: Error saving feedback: {e}")
                        print(f"❌ FEEDBACK SUBMISSION: Error type: {type(e).__name__}")
                        traceback.print_exc()
                        st.error(f"❌ Error saving feedback: {e}")
                        st.write(f"Debug error: {str(e)}")
        else:
            # Feedback already submitted - show success message and reset option
            st.success("✅ Feedback already submitted for this conversation!")
            col1, col2 = st.columns([1, 1])
            with col1:
                if st.button("🔄 Submit New Feedback", key="new_feedback_button", use_container_width=True):
                    try:
                        st.session_state.feedback_submitted = False
                        st.rerun()
                    except Exception as e:
                        # Handle any Streamlit API exceptions gracefully
                        logger.error(f"Error resetting feedback state: {e}")
                        st.error(f"Error resetting feedback. Please refresh the page.")
            with col2:
                if st.button("📋 View Conversation", key="view_conversation_button", use_container_width=True):
                    # Scroll to conversation - this is handled by the auto-scroll at bottom
                    pass
    
    # Display retrieval history stats
    if st.session_state.rag_retrieval_history:
        st.markdown("---")
        st.markdown("#### 📊 Retrieval History")
        
        with st.expander(f"View {len(st.session_state.rag_retrieval_history)} retrieval entries", expanded=False):
            for idx, entry in enumerate(st.session_state.rag_retrieval_history, 1):
                st.markdown(f"### **Retrieval #{idx}**")
                
                # Display timestamp if available
                if entry.get("timestamp"):
                    timestamp_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(entry["timestamp"]))
                    st.caption(f"🕐 {timestamp_str}")
                
                # Display the actual RAG query
                rag_query_expansion = entry.get("rag_query_expansion", "No query available")
                st.markdown("**🔍 RAG Query:**")
                st.code(rag_query_expansion, language="text")
                
                # Display filters used
                filters_applied = entry.get("filters_applied", {})
                if filters_applied and any(filters_applied.values()):
                    st.markdown("**🎯 Filters Applied:**")
                    filter_display = {}
                    if filters_applied.get("sources"):
                        filter_display["Sources"] = filters_applied["sources"]
                    if filters_applied.get("years"):
                        filter_display["Years"] = filters_applied["years"]
                    if filters_applied.get("districts"):
                        filter_display["Districts"] = filters_applied["districts"]
                    if filters_applied.get("filenames"):
                        filter_display["Filenames"] = filters_applied["filenames"]
                    
                    if filter_display:
                        st.json(filter_display)
                    else:
                        st.info("No filters applied")
                else:
                    st.info("No filters applied")
                
                # Display conversation history up to retrieval point
                conversation_up_to = entry.get("conversation_up_to", [])
                if conversation_up_to:
                    st.markdown("**💬 Conversation History (up to retrieval point):**")
                    with st.expander(f"View {len(conversation_up_to)} messages", expanded=False):
                        for msg_idx, msg in enumerate(conversation_up_to, 1):
                            role = msg.get("type", "unknown")
                            content = msg.get("content", "")
                            
                            if role == "HumanMessage" or role == "human":
                                st.markdown(f"**👤 User {msg_idx}:** {content[:200]}{'...' if len(content) > 200 else ''}")
                            elif role == "AIMessage" or role == "ai":
                                st.markdown(f"**🤖 Assistant {msg_idx}:** {content[:200]}{'...' if len(content) > 200 else ''}")
                else:
                    st.info("No conversation history available")
                
                # Display documents retrieved
                docs_retrieved = entry.get("docs_retrieved", [])
                if docs_retrieved:
                    st.markdown(f"**📄 Documents Retrieved ({len(docs_retrieved)}):**")
                    with st.expander(f"View {len(docs_retrieved)} documents", expanded=False):
                        for doc_idx, doc in enumerate(docs_retrieved, 1):
                            st.markdown(f"**Document {doc_idx}:**")
                            
                            # Display metadata
                            metadata = doc.get("metadata", {})
                            if metadata:
                                col1, col2, col3 = st.columns(3)
                                with col1:
                                    st.write(f"📄 **File:** {metadata.get('filename', 'Unknown')}")
                                with col2:
                                    st.write(f"🏛️ **Source:** {metadata.get('source', 'Unknown')}")
                                with col3:
                                    st.write(f"📅 **Year:** {metadata.get('year', 'Unknown')}")
                                
                                # Additional metadata
                                if metadata.get('district'):
                                    st.write(f"📍 **District:** {metadata.get('district')}")
                                if metadata.get('page'):
                                    st.write(f"📖 **Page:** {metadata.get('page')}")
                                if metadata.get('score') is not None:
                                    st.write(f"⭐ **Score:** {metadata.get('score'):.3f}" if isinstance(metadata.get('score'), (int, float)) else f"⭐ **Score:** {metadata.get('score')}")
                            
                            # Display content preview (first 200 chars)
                            content = doc.get("content", doc.get("page_content", ""))
                            if content:
                                st.markdown("**Content Preview:**")
                                st.text_area(
                                    "Content Preview",
                                    value=content[:200] + ("..." if len(content) > 200 else ""),
                                    height=100,
                                    disabled=True,
                                    label_visibility="collapsed",
                                    key=f"retrieval_{idx}_doc_{doc_idx}_preview"
                                )
                            
                            if doc_idx < len(docs_retrieved):
                                st.markdown("---")
                else:
                    st.info("No documents retrieved")
                
                # Display summary stats
                st.markdown("**📊 Summary:**")
                st.json({
                    "conversation_length": len(conversation_up_to),
                    "documents_retrieved": len(docs_retrieved)
                })
                
                if idx < len(st.session_state.rag_retrieval_history):
                    st.markdown("---")
    
    # Example Questions Section - Compact layout
    st.markdown("---")
    
    # Initialize example question state
    if 'custom_question_1' not in st.session_state:
        st.session_state.custom_question_1 = "How were administrative costs managed in the PDM implementation, and what issues arose with budget execution regarding staff salaries?"
    if 'custom_question_2' not in st.session_state:
        st.session_state.custom_question_2 = "What did the National Coordinator say about the release of funds for PDM administrative costs in the letter dated 29th September 2022 and how did the funding received affect the activities of the PDCs and PDM SACCOs in the FY 2022/23?"
    
    # Row 1: Header on left, Question 1 (file insights) on right
    header_col, q1_col = st.columns([1, 2])
    
    with header_col:
        st.markdown("### 💡 Example Questions")
        st.caption("      Click **Use ...** or edit")
    
    with q1_col:
        example_q1 = "List couple of insights from the filename."
        st.markdown("**📄 File Insights** _(select a file first)_")
        q1_inner1, q1_inner2 = st.columns([3, 1])
        with q1_inner1:
            st.code(example_q1, language=None)
        with q1_inner2:
            if st.button("📋 Use question !", key="use_example_1", use_container_width=True):
                st.session_state.pending_question = example_q1
                st.session_state.input_counter = (st.session_state.get('input_counter', 0) + 1) % 1000
                st.rerun()
    
    st.markdown("---")
    
    # Row 2: Questions 2 & 3 side by side
    st.markdown("#### ✏️ Customizable Questions")
    q_col1, q_col2 = st.columns(2)
    
    # Question 2 - Left column (will trigger follow-up)
    with q_col1:
        st.caption("🔄 _This question will trigger follow-up prompts for year/district_")
        custom_q1 = st.text_area(
            "Question 2:",
            value=st.session_state.custom_question_1,
            height=100,
            key="edit_question_2",
            help="Modify this question to fit your needs"
        )
        if st.button("📋 Use Question 2", key="use_custom_1", use_container_width=True):
            if custom_q1.strip():
                st.session_state.pending_question = custom_q1.strip()
                st.session_state.custom_question_1 = custom_q1.strip()
                st.session_state.input_counter = (st.session_state.get('input_counter', 0) + 1) % 1000
                st.rerun()
            else:
                st.warning("Please enter a question first!")
    
    # Question 3 - Right column (has all info, no follow-up)
    with q_col2:
        st.caption("✅ _Complete question - has year & context, no follow-up needed_")
        custom_q2 = st.text_area(
            "Question 3:",
            value=st.session_state.custom_question_2,
            height=100,
            key="edit_question_3",
            help="Modify this question to fit your needs"
        )
        if st.button("📋 Use Question 3", key="use_custom_2", use_container_width=True):
            if custom_q2.strip():
                st.session_state.pending_question = custom_q2.strip()
                st.session_state.custom_question_2 = custom_q2.strip()
                st.session_state.input_counter = (st.session_state.get('input_counter', 0) + 1) % 1000
                st.rerun()
            else:
                st.warning("Please enter a question first!")
    

    # Store selected question for next render (handled in input section above)
    # This ensures the question populates the input field correctly
    
    # Auto-scroll to bottom
    st.markdown("""
    <script>
        window.scrollTo(0, document.body.scrollHeight);
    </script>
    """, unsafe_allow_html=True)


if __name__ == "__main__":
    # Check if running in Streamlit context
    try:
        from streamlit.runtime.scriptrunner import get_script_run_ctx
        if get_script_run_ctx() is None:
            # Not in Streamlit runtime - show helpful message
            print("=" * 80)
            print("⚠️  WARNING: This is a Streamlit app!")
            print("=" * 80)
            print("\nPlease run this app using:")
            print("  streamlit run app.py")
            print("\nNot: python app.py")
            print("\nThe app will not function correctly when run with 'python app.py'")
            print("=" * 80)
            import sys
            sys.exit(1)
    except ImportError:
        # Streamlit not installed or not in Streamlit context
        print("=" * 80)
        print("⚠️  WARNING: This is a Streamlit app!")
        print("=" * 80)
        print("\nPlease run this app using:")
        print("  streamlit run app.py")
        print("\nNot: python app.py")
        print("=" * 80)
        import sys
        sys.exit(1)
    main()