"""
Shared helper functions used by both HuggingFace Space and Local environments.
Contains: configuration, memory management, vectorstore operations, PDF helpers, and UI utilities.
"""

import os
from typing import List, Optional
from datetime import datetime
from collections import deque
import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_core.tools import tool

# ============================================================================
# CONFIGURATION - All settings in one place
# ============================================================================

def setup():
    """
    Central configuration for the RAG Agent application.
    Modify these values to customize the application behavior.
    Returns a config dictionary with all settings.
    """
    return {
        # Model Configuration
        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
        "ollama_model": "qwen2m:latest",      # Local Ollama model
            "hf_model": "mistralai/Mistral-7B-Instruct-v0.2",  # HuggingFace cloud model
        "ollama_base_url": "http://localhost:11434",
        
        # Text Splitting Configuration
        "chunk_size": 1000,
        "chunk_overlap": 200,
        
        # Search Configuration
        "search_k": 5,                    # Number of documents to retrieve
        "search_content_limit": 500,      # Max chars to show per chunk
        
        # LLM Generation Configuration
        "max_tokens": 512,
        "temperature": 0.1,               # Lower = more deterministic
        "temperature_fallback": 0.7,      # For text_generation fallback
        
        # Memory Configuration
        "max_memory_turns": 50,           # Max conversation turns to store
        "memory_context_limit": 500,      # Max chars per memory entry
        
        # Server Configuration
        "server_port": 7860,
        "server_host": "0.0.0.0",
        
        # UI Configuration
        "chatbot_height": 600,
        "progress_bar_length": 20,
        "chat_progress_bar_length": 15,
    }

# Initialize configuration
CONFIG = setup()

# ============================================================================
# ENVIRONMENT DETECTION
# ============================================================================

IS_HF_SPACE = os.getenv("SPACE_ID") is not None

# Directories - use persistent storage on HF Spaces if available
DATA_DIR = "/data" if (IS_HF_SPACE and os.path.exists("/data")) else "data"
EMBEDDINGS_DIR = os.path.join(DATA_DIR, "embeddings")

# Check storage persistence status
HAS_PERSISTENT_STORAGE = IS_HF_SPACE and os.path.exists("/data")
STORAGE_WARNING = "" if not IS_HF_SPACE else (
    "✅ Persistent storage enabled - files will survive restarts" if HAS_PERSISTENT_STORAGE else 
    "⚠️ Temporary storage - uploaded files will be lost when Space restarts"
)

# Initialize embeddings (shared across environments)
embeddings = HuggingFaceEmbeddings(model_name=CONFIG["embedding_model"])

# Global vectorstore (will be set by build_vectorstore)
vs = None

# ============================================================================
# CONVERSATION MEMORY
# ============================================================================

conversation_memory: deque = deque(maxlen=CONFIG["max_memory_turns"])

def add_to_memory(role: str, content: str):
    """Add a message to conversation memory with timestamp."""
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    conversation_memory.append({
        "timestamp": timestamp,
        "role": role,
        "content": content
    })
    print(f"💾 Memory updated: {role} message added (total: {len(conversation_memory)} turns)")

def get_memory_context(last_n: int = 10) -> str:
    """Get the last N conversation turns as context."""
    if not conversation_memory:
        return "No previous conversation history."
    
    recent = list(conversation_memory)[-last_n:]
    context_parts = []
    for msg in recent:
        role_emoji = "👤" if msg["role"] == "user" else "🤖"
        context_parts.append(f"[{msg['timestamp']}] {role_emoji} {msg['role'].upper()}: {msg['content'][:CONFIG['memory_context_limit']]}")
    
    return "\n\n".join(context_parts)

def search_memory(query: str) -> str:
    """Search conversation memory for relevant past discussions."""
    if not conversation_memory:
        return "No conversation history to search."
    
    query_lower = query.lower()
    matches = []
    
    for msg in conversation_memory:
        content_lower = msg["content"].lower()
        # Simple keyword matching
        if any(word in content_lower for word in query_lower.split()):
            role_emoji = "👤" if msg["role"] == "user" else "🤖"
            matches.append(f"[{msg['timestamp']}] {role_emoji} {msg['role'].upper()}: {msg['content'][:CONFIG['memory_context_limit'] - 200]}...")
    
    if matches:
        return f"Found {len(matches)} relevant conversation(s):\n\n" + "\n\n---\n\n".join(matches[:5])
    else:
        return f"No conversations found matching '{query}'."

def clear_memory():
    """Clear all conversation memory."""
    conversation_memory.clear()
    print("🧹 Conversation memory cleared")

# ============================================================================
# UTILITY FUNCTIONS
# ============================================================================

def get_timestamp() -> str:
    """Get current timestamp in HH:MM:SS format."""
    return datetime.now().strftime("%H:%M:%S")

def create_elapsed_timer(start_time: datetime):
    """Create an elapsed time function for tracking duration."""
    def get_elapsed() -> str:
        elapsed = datetime.now() - start_time
        return f"⏱️ {elapsed.total_seconds():.1f}s"
    return get_elapsed

def format_progress_bar(elapsed_time: str, percentage: int, message: str, bar_length: int = 20) -> str:
    """Format progress with visual progress bar using Unicode blocks."""
    filled_length = int(bar_length * percentage / 100)
    bar = '█' * filled_length + '░' * (bar_length - filled_length)
    return f"{elapsed_time} | [{percentage:3d}%] {bar} {message}"

# =========================================================================
# FLOATING PROGRESS BAR HTML/JS (for Gradio UI)
# =========================================================================
def floating_progress_bar_html():
    """Return HTML+JS for a floating, borderless, fit-content progress bar overlay."""
    return '''
    <div id="floating-progress" style="
        display: none;
        position: fixed;
        top: 20px; left: 50%; transform: translateX(-50%);
        background: #222; color: #fff; padding: 8px 0; border-radius: 8px; z-index: 9999;
        font-family: monospace; font-size: 1.2em; box-shadow: none; border: none;
        width: fit-content; min-width: 0; max-width: none;
    ">
        [....................................................................................................]
    </div>
    <script>
    function showProgressBar(barText) {
        var el = document.getElementById('floating-progress');
        el.innerText = barText;
        el.style.display = 'block';
    }
    function hideProgressBar() {
        document.getElementById('floating-progress').style.display = 'none';
    }
    // Example usage (remove or replace with Python/Gradio event):
    // showProgressBar('[|||||||||||||.............]');
    // setTimeout(hideProgressBar, 2000);
    </script>
    '''

# ============================================================================
# PDF HELPERS
# ============================================================================

def get_pdf_list() -> List[str]:
    """Get list of PDF files in data folder."""
    return [f for f in os.listdir(DATA_DIR) if f.endswith(".pdf")]

def get_pdf_list_ui() -> List[str]:
    """Get PDF list for UI dropdown (with error handling)."""
    try:
        return get_pdf_list()
    except Exception as e:
        print(f"Error getting PDF list: {e}")
        return []

def make_pdf_dropdown(value=None):
    """Create a PDF dropdown with current file list."""
    return gr.Dropdown(choices=get_pdf_list_ui(), value=value)

# ============================================================================
# VECTORSTORE OPERATIONS
# ============================================================================

def build_vectorstore(force_rebuild: bool = False) -> Optional[FAISS]:
    """Build or load FAISS vectorstore from PDFs.
    
    Args:
        force_rebuild: If True, rebuild from scratch even if existing vectorstore found
    """
    global vs
    
    # Check if we should load existing vectorstore
    if not force_rebuild and os.path.exists(os.path.join(EMBEDDINGS_DIR, "index.faiss")):
        try:
            print("📚 Loading existing vectorstore...")
            vectorstore = FAISS.load_local(EMBEDDINGS_DIR, embeddings, allow_dangerous_deserialization=True)
            print("✅ Vectorstore loaded successfully")
            vs = vectorstore
            return vectorstore
        except Exception as e:
            print(f"❌ Error loading vectorstore: {e}, rebuilding...")
    
    # Build new vectorstore from PDFs
    pdf_files = get_pdf_list()
    if not pdf_files:
        print("No PDF files found to build embeddings")
        vs = None
        return None
        
    print(f"🔨 Building vectorstore from {len(pdf_files)} PDF(s): {pdf_files}")
    
    docs: List[Document] = []
    for filename in pdf_files:
        try:
            filepath = os.path.join(DATA_DIR, filename)
            print(f"📖 Loading {filename}...")
            loader = PyPDFLoader(filepath)
            file_docs = loader.load()
            docs.extend(file_docs)
            print(f"✅ Loaded {len(file_docs)} pages from {filename}")
        except Exception as e:
            print(f"❌ Error loading {filename}: {e}")
            continue
    
    if not docs:
        print("⚠️ No documents could be loaded")
        vs = None
        return None
    
    print(f"✂️ Splitting {len(docs)} pages into chunks...")
    splitter = RecursiveCharacterTextSplitter(
        chunk_size=CONFIG["chunk_size"], 
        chunk_overlap=CONFIG["chunk_overlap"]
    )
    splits = splitter.split_documents(docs)
    print(f"🧩 Created {len(splits)} text chunks")
    
    print("🤖 Creating FAISS embeddings...")
    try:
        vectorstore = FAISS.from_documents(splits, embeddings)
        
        print(f"💾 Saving vectorstore to {EMBEDDINGS_DIR}...")
        vectorstore.save_local(EMBEDDINGS_DIR)
        
        vs = vectorstore
        print("✅ Vectorstore built and saved successfully")
        return vectorstore
        
    except Exception as e:
        print(f"✗ Failed to build vectorstore: {e}")
        import traceback
        traceback.print_exc()
        vs = None
        return None

def get_vectorstore():
    """Get the current vectorstore instance."""
    global vs
    return vs

def set_vectorstore(vectorstore):
    """Set the vectorstore instance."""
    global vs
    vs = vectorstore

# ============================================================================
# RAG AGENT TOOLS (LangChain @tool decorator pattern)
# ============================================================================

@tool
def list_documents() -> str:
    """List all available PDF documents in the system. Use this tool when the user asks what documents are available, what files they have, or wants to see the document list."""
    pdfs = get_pdf_list()
    if pdfs:
        return f"📁 Available documents: {', '.join(pdfs)}"
    else:
        return "📁 No documents are currently uploaded."

@tool
def count_documents() -> str:
    """Count the total number of uploaded PDF documents. Use this tool when the user asks how many documents they have or wants a document count."""
    count = len(get_pdf_list())
    return f"📊 Total documents: {count}"

@tool
def search_documents(query: str) -> str:
    """Search document content using RAG (Retrieval Augmented Generation). Use this tool to find information within the uploaded PDF documents based on a search query."""
    global vs
    
    # Check if we have any PDF files first
    pdf_files = get_pdf_list()
    if not pdf_files:
        return "🔍 No documents are currently uploaded. Please upload PDF files first."
    
    # Force reload vectorstore from disk if files exist
    print(f"🔍 Checking vectorstore for {len(pdf_files)} PDF files...")
    
    # Check if FAISS files exist on disk
    faiss_path = os.path.join(EMBEDDINGS_DIR, "index.faiss")
    pkl_path = os.path.join(EMBEDDINGS_DIR, "index.pkl")
    
    if os.path.exists(faiss_path) and os.path.exists(pkl_path):
        print(f"📁 Found vectorstore files, loading...")
        try:
            # Force reload from disk
            vs = FAISS.load_local(EMBEDDINGS_DIR, embeddings, allow_dangerous_deserialization=True)
            print(f"✅ Vectorstore loaded successfully from disk")
        except Exception as e:
            print(f"❌ Error loading vectorstore: {e}")
            vs = None
    else:
        print(f"📁 No vectorstore files found, attempting to build...")
        vs = build_vectorstore()
        
    if vs is None:
        return f"🔍 Found {len(pdf_files)} document(s) but search index could not be created. Please try re-uploading your files."
    
    try:
        # Extract key search terms from query (remove common words)
        search_query = query
        print(f"🔍 Searching vectorstore for: {search_query}")
        
        # Use similarity_search_with_score to filter by relevance
        docs_with_scores = vs.similarity_search_with_score(search_query, k=CONFIG["search_k"])
        
        if docs_with_scores:
            # Filter by score (lower is better for L2 distance) - adjust threshold as needed
            # Show more content from each chunk for better context
            context_parts = []
            for doc, score in docs_with_scores:
                # Get source file from metadata
                source = doc.metadata.get('source', 'Unknown').split('/')[-1]
                page = doc.metadata.get('page', '?')
                # Include score and source in debug output
                print(f"  📄 Score: {score:.3f} | Source: {source} pg{page} - {doc.page_content[:50]}...")
                # Show more content with source info
                context_parts.append(f"[Source: {source}, Page: {page}, Relevance: {score:.2f}]\n{doc.page_content[:CONFIG['search_content_limit']]}")
            
            context = "\n\n---\n\n".join(context_parts)
            print(f"✓ Found {len(docs_with_scores)} document chunks")
            return f"🔍 Search results for '{query}':\n\n{context}"
        else:
            print(f"⚠️ No relevant documents found for query: {query}")
            return f"🔍 No relevant information found for '{query}' in your {len(pdf_files)} document(s). Try different keywords or check if your documents contain relevant content."
            
    except Exception as e:
        error_msg = f"🔍 Search error: {str(e)}. You have {len(pdf_files)} documents available."
        print(f"❌ Search error: {str(e)}")
        import traceback
        traceback.print_exc()
        return error_msg

@tool
def search_conversation_history(query: str) -> str:
    """Search through previous conversation history to find past discussions. Use this tool when the user asks about something they discussed before, wants to recall previous answers, or references past conversations."""
    return search_memory(query)

@tool
def get_recent_conversation(turns: int = 5) -> str:
    """Get the most recent conversation turns. Use this tool when the user asks what they were discussing, wants a summary of recent chat, or needs context from earlier in the conversation."""
    return get_memory_context(last_n=turns)

# List of all available tools
AGENT_TOOLS = [list_documents, count_documents, search_documents, search_conversation_history, get_recent_conversation]

# Sample question texts - Enhanced for agent capabilities
SAMPLE_Q1 = "How many documents are loaded? List their names and types."
SAMPLE_Q2 = "Summarize the key points of each document in 5 bullet points."
SAMPLE_Q3 = "What is the attention mechanism? list the main topics."
SAMPLE_Q4 = "How can I cook chicken breast with Phillips air fryer recipes?"
SAMPLE_Q5 = "Summarize each document in max 10 bullet points."
SAMPLE_Q6 = "What did we discuss earlier?"
SAMPLE_Q7 = "Summarize it in 50 words."