import os
import re
import numpy as np
import gc
import torch
import time
import shutil
import hashlib
import pickle
import traceback
from typing import List, Dict, Any, Tuple, Optional, Union, Generator
from dataclasses import dataclass
import gradio as gr

# Import dependencies (no need for pip install commands)
import fitz  # PyMuPDF
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from llama_cpp import Llama
from rank_bm25 import BM25Okapi
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from huggingface_hub import hf_hub_download

# Download nltk resources
try:
    nltk.download('punkt', quiet=True)
    nltk.download('stopwords', quiet=True)
except:
    print("Failed to download NLTK resources, continuing without them")

# Setup directories for Spaces
os.makedirs("pdfs", exist_ok=True)
os.makedirs("models", exist_ok=True)
os.makedirs("pdf_cache", exist_ok=True)

# Download nltk resources
try:
    nltk.download('punkt', quiet=True)
    nltk.download('stopwords', quiet=True)
except:
    print("Failed to download NLTK resources, continuing without them")

# Download model from Hugging Face Hub
model_path = hf_hub_download(
    repo_id="TheBloke/phi-2-GGUF",
    filename="phi-2.Q8_0.gguf",
    repo_type="model",
    local_dir="models"
)

# === MEMORY MANAGEMENT UTILITIES ===
def clear_memory():
    """Clear memory to prevent OOM errors"""
    gc.collect()
    torch.cuda.empty_cache() if torch.cuda.is_available() else None

# === PDF PROCESSING ===
@dataclass
class PDFChunk:
    """Class to represent a chunk of text extracted from a PDF"""
    text: str
    source: str
    page_num: int
    chunk_id: int

class PDFProcessor:
    def __init__(self, pdf_dir: str = "pdfs"):
        """Initialize PDF processor

        Args:
            pdf_dir: Directory containing PDF files
        """
        self.pdf_dir = pdf_dir
        # Smaller chunk size with more overlap for better retrieval
        self.text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=384,
            chunk_overlap=288,  # 75% overlap for better context preservation
            length_function=len,
            is_separator_regex=False,
        )
        
        # Create cache directory
        self.cache_dir = os.path.join(os.getcwd(), "pdf_cache")
        os.makedirs(self.cache_dir, exist_ok=True)

    def list_pdfs(self) -> List[str]:
        """List all PDF files in the directory"""
        if not os.path.exists(self.pdf_dir):
            return []
        return [f for f in os.listdir(self.pdf_dir) if f.lower().endswith('.pdf')]

    def _get_cache_path(self, pdf_path: str) -> str:
        """Get the cache file path for a PDF"""
        pdf_hash = hashlib.md5(open(pdf_path, 'rb').read(8192)).hexdigest()
        return os.path.join(self.cache_dir, f"{os.path.basename(pdf_path)}_{pdf_hash}.pkl")
    
    def _is_cached(self, pdf_path: str) -> bool:
        """Check if a PDF is cached"""
        cache_path = self._get_cache_path(pdf_path)
        return os.path.exists(cache_path)
    
    def _load_from_cache(self, pdf_path: str) -> List[PDFChunk]:
        """Load chunks from cache"""
        cache_path = self._get_cache_path(pdf_path)
        try:
            with open(cache_path, 'rb') as f:
                return pickle.load(f)
        except:
            return None
    
    def _save_to_cache(self, pdf_path: str, chunks: List[PDFChunk]) -> None:
        """Save chunks to cache"""
        cache_path = self._get_cache_path(pdf_path)
        try:
            with open(cache_path, 'wb') as f:
                pickle.dump(chunks, f)
        except Exception as e:
            print(f"Warning: Failed to cache PDF {pdf_path}: {str(e)}")

    def clean_text(self, text: str) -> str:
        """Clean extracted text"""
        # Remove excessive whitespace
        text = re.sub(r'\s+', ' ', text).strip()
        # Remove header/footer patterns (common in PDFs)
        text = re.sub(r'(?<!\w)page \d+(?!\w)', '', text, flags=re.IGNORECASE)
        return text

    def extract_text_from_pdf(self, pdf_path: str) -> List[PDFChunk]:
        """Extract text content from a PDF file with improved extraction

        Args:
            pdf_path: Path to the PDF file

        Returns:
            List of PDFChunk objects extracted from the PDF
        """
        # Check cache first
        if self._is_cached(pdf_path):
            cached_chunks = self._load_from_cache(pdf_path)
            if cached_chunks:
                print(f"Loaded {len(cached_chunks)} chunks from cache for {os.path.basename(pdf_path)}")
                return cached_chunks
        
        try:
            doc = fitz.open(pdf_path)
            pdf_chunks = []
            pdf_name = os.path.basename(pdf_path)
            
            for page_num in range(len(doc)):
                page = doc.load_page(page_num)
                
                # Extract text with more options for better quality
                page_text = page.get_text("text", sort=True)
                # Try to extract text with alternative layout analysis if the text is too short
                if len(page_text) < 100:
                    try:
                        page_text = page.get_text("dict", sort=True)
                        # Convert dict to text
                        if isinstance(page_text, dict) and "blocks" in page_text:
                            extracted_text = ""
                            for block in page_text["blocks"]:
                                if "lines" in block:
                                    for line in block["lines"]:
                                        if "spans" in line:
                                            for span in line["spans"]:
                                                if "text" in span:
                                                    extracted_text += span["text"] + " "
                            page_text = extracted_text
                    except:
                        # Fallback to default extraction
                        page_text = page.get_text("text")
                
                # Clean the text
                page_text = self.clean_text(page_text)
                
                # Extract tables
                try:
                    tables = page.find_tables()
                    if tables and hasattr(tables, "tables"):
                        for table in tables.tables:
                            table_text = ""
                            for i, row in enumerate(table.rows):
                                row_cells = []
                                for cell in row.cells:
                                    if hasattr(cell, "rect"):
                                        cell_text = page.get_text("text", clip=cell.rect)
                                        cell_text = self.clean_text(cell_text)
                                        row_cells.append(cell_text)
                                if row_cells:
                                    table_text += " | ".join(row_cells) + "\n"
                            
                            # Add table text to page text
                            if table_text.strip():
                                page_text += "\n\nTABLE:\n" + table_text
                except Exception as table_err:
                    print(f"Warning: Skipping table extraction for page {page_num}: {str(table_err)}")
                
                # Split the page text into chunks
                if page_text.strip():
                    page_chunks = self.text_splitter.split_text(page_text)
                    
                    # Create PDFChunk objects
                    for i, chunk_text in enumerate(page_chunks):
                        pdf_chunks.append(PDFChunk(
                            text=chunk_text,
                            source=pdf_name,
                            page_num=page_num + 1,  # 1-based page numbering for humans
                            chunk_id=i
                        ))
                
                # Clear memory periodically
                if page_num % 10 == 0:
                    clear_memory()
            
            doc.close()
            
            # Cache the results
            self._save_to_cache(pdf_path, pdf_chunks)
            
            return pdf_chunks
        except Exception as e:
            print(f"Error extracting text from {pdf_path}: {str(e)}")
            return []

    def process_pdf(self, pdf_name: str) -> List[PDFChunk]:
        """Process a single PDF file and extract chunks

        Args:
            pdf_name: Name of the PDF file in the pdf_dir

        Returns:
            List of PDFChunk objects from the PDF
        """
        pdf_path = os.path.join(self.pdf_dir, pdf_name)
        return self.extract_text_from_pdf(pdf_path)

    def process_all_pdfs(self, batch_size: int = 2) -> List[PDFChunk]:
        """Process all PDFs in batches to manage memory

        Args:
            batch_size: Number of PDFs to process in each batch

        Returns:
            List of all PDFChunk objects from all PDFs
        """
        all_chunks = []
        pdf_files = self.list_pdfs()

        if not pdf_files:
            print("No PDF files found in the directory.")
            return []

        # Process PDFs in batches
        for i in range(0, len(pdf_files), batch_size):
            batch = pdf_files[i:i+batch_size]
            print(f"Processing batch {i//batch_size + 1}/{(len(pdf_files)-1)//batch_size + 1}")

            for pdf_name in batch:
                print(f"Processing {pdf_name}")
                chunks = self.process_pdf(pdf_name)
                all_chunks.extend(chunks)
                print(f"Extracted {len(chunks)} chunks from {pdf_name}")

            # Clear memory after each batch
            clear_memory()

        return all_chunks

# === VECTOR DATABASE SETUP ===
class VectorDBManager:
    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
        """Initialize vector database manager

        Args:
            model_name: Name of the embedding model
        """
        # Initialize embedding model with normalization
        try:
            self.embedding_model = HuggingFaceEmbeddings(
                model_name=model_name,
                model_kwargs={"device": "cpu"},
                encode_kwargs={"normalize_embeddings": True}
            )
        except Exception as e:
            print(f"Error initializing embedding model {model_name}: {str(e)}")
            print("Falling back to all-MiniLM-L6-v2 model")
            self.embedding_model = HuggingFaceEmbeddings(
                model_name="sentence-transformers/all-MiniLM-L6-v2",
                model_kwargs={"device": "cpu"},
                encode_kwargs={"normalize_embeddings": True}
            )
        
        self.vectordb = None
        # BM25 index for hybrid search
        self.bm25_index = None
        self.chunks = []
        self.tokenized_chunks = []

    def _prepare_bm25(self, chunks: List[PDFChunk]):
        """Prepare BM25 index for hybrid search"""
        # Tokenize chunks for BM25
        try:
            tokenized_chunks = []
            for chunk in chunks:
                # Tokenize and remove stopwords
                tokens = word_tokenize(chunk.text.lower())
                stop_words = set(stopwords.words('english'))
                filtered_tokens = [w for w in tokens if w.isalnum() and w not in stop_words]
                tokenized_chunks.append(filtered_tokens)
            
            # Create BM25 index
            self.bm25_index = BM25Okapi(tokenized_chunks)
        except Exception as e:
            print(f"Error creating BM25 index: {str(e)}")
            print(traceback.format_exc())
            self.bm25_index = None
    
    def create_vector_db(self, chunks: List[PDFChunk]) -> None:
        """Create vector database from text chunks

        Args:
            chunks: List of PDFChunk objects
        """
        try:
            if not chunks or len(chunks) == 0:
                print("ERROR: No chunks provided to create vector database")
                return

            print(f"Creating vector DB with {len(chunks)} chunks")
            
            # Store chunks for hybrid search
            self.chunks = chunks
            
            # Prepare data for vector DB
            chunk_texts = [chunk.text for chunk in chunks]
            
            # Create BM25 index for hybrid search
            print("Creating BM25 index for hybrid search")
            self._prepare_bm25(chunks)
            
            # Process in smaller batches to manage memory
            batch_size = 16  # Reduced for Spaces
            all_embeddings = []

            for i in range(0, len(chunk_texts), batch_size):
                batch = chunk_texts[i:i+batch_size]
                print(f"Embedding batch {i//batch_size + 1}/{(len(chunk_texts)-1)//batch_size + 1}")

                # Generate embeddings for the batch
                batch_embeddings = self.embedding_model.embed_documents(batch)
                all_embeddings.extend(batch_embeddings)

                # Clear memory after each batch
                clear_memory()

            # Create FAISS index
            print(f"Creating FAISS index with {len(all_embeddings)} embeddings")
            self.vectordb = FAISS.from_embeddings(
                text_embeddings=list(zip(chunk_texts, all_embeddings)),
                embedding=self.embedding_model
            )
            
            print(f"Vector database created with {len(chunks)} documents")

        except Exception as e:
            print(f"Error creating vector database: {str(e)}")
            print(traceback.format_exc())
            raise

    def _format_chunk_with_metadata(self, chunk: PDFChunk) -> str:
        """Format a chunk with its metadata for better context"""
        return f"Source: {chunk.source} | Page: {chunk.page_num}\n\n{chunk.text}"

    def hybrid_search(self, query: str, k: int = 5, alpha: float = 0.7) -> List[str]:
        """Hybrid search combining vector search and BM25

        Args:
            query: Query text
            k: Number of results to return
            alpha: Weight for vector search (1-alpha for BM25)

        Returns:
            List of formatted documents
        """
        if self.vectordb is None:
            print("Vector database not initialized")
            return []

        try:
            # Get vector search results
            vector_results = self.vectordb.similarity_search(query, k=k*2)
            vector_texts = [doc.page_content for doc in vector_results]
            
            final_results = []
            
            # Combine with BM25 if available
            if self.bm25_index is not None:
                try:
                    # Tokenize query for BM25
                    query_tokens = word_tokenize(query.lower())
                    stop_words = set(stopwords.words('english'))
                    filtered_query = [w for w in query_tokens if w.isalnum() and w not in stop_words]
                    
                    # Get BM25 scores
                    bm25_scores = self.bm25_index.get_scores(filtered_query)
                    
                    # Combine scores (normalized)
                    combined_results = []
                    seen_texts = set()
                    
                    # First add vector results with their positions as scores
                    for i, text in enumerate(vector_texts):
                        if text not in seen_texts:
                            seen_texts.add(text)
                            # Find corresponding chunk
                            for j, chunk in enumerate(self.chunks):
                                if chunk.text == text:
                                    # Combine scores: alpha * vector_score + (1-alpha) * bm25_score
                                    # For vector, use inverse of position as score (normalized)
                                    vector_score = 1.0 - (i / len(vector_texts))
                                    # Normalize BM25 score
                                    bm25_score = bm25_scores[j] / max(bm25_scores) if max(bm25_scores) > 0 else 0
                                    combined_score = alpha * vector_score + (1-alpha) * bm25_score
                                    
                                    combined_results.append((chunk, combined_score))
                                    break
                    
                    # Sort by combined score
                    combined_results.sort(key=lambda x: x[1], reverse=True)
                    
                    # Get top k results
                    top_chunks = [item[0] for item in combined_results[:k]]
                    
                    # Format results with metadata
                    final_results = [self._format_chunk_with_metadata(chunk) for chunk in top_chunks]
                except Exception as e:
                    print(f"Error in BM25 scoring: {str(e)}")
                    # Fallback to vector search results
                    final_results = vector_texts[:k]
            else:
                # Just use vector search results if BM25 is not available
                final_results = vector_texts[:k]
            
            return final_results
        except Exception as e:
            print(f"Error during hybrid search: {str(e)}")
            return []

# === QUERY EXPANSION ===
class QueryExpander:
    def __init__(self, llm_model):
        """Initialize query expander

        Args:
            llm_model: LLM model for query expansion
        """
        self.llm = llm_model
    
    def expand_query(self, query: str) -> str:
        """Expand the query using the LLM to improve retrieval

        Args:
            query: Original query

        Returns:
            Expanded query
        """
        try:
            prompt = f"""I need to search for documents related to this question: "{query}"
            
Please help me expand this query by identifying key concepts, synonyms, and related terms that might be used in the documents.
Return only the expanded search query, without any explanations or additional text.

Expanded query:"""
            
            expanded = self.llm.generate(prompt, max_tokens=100, temperature=0.3)
            
            # Combine original and expanded
            combined = f"{query} {expanded}"
            
            # Limit length
            if len(combined) > 300:
                combined = combined[:300]
            
            return combined
        except:
            # Return original query if expansion fails
            return query

# === LLM SETUP ===
class Phi2Model:
    def __init__(self, model_path: str = model_path):
        """Initialize Phi-2 model

        Args:
            model_path: Path to the model file
        """
        try:
            # Initialize Phi-2 with llama.cpp - optimized for Spaces
            self.llm = Llama(
                model_path=model_path,
                n_ctx=1024,         # Reduced context window for Spaces
                n_batch=64,         # Reduced batch size 
                n_gpu_layers=0,     # Run on CPU for compatibility
                verbose=False
            )
        except Exception as e:
            print(f"Error initializing Phi-2 model: {str(e)}")
            raise

    def generate(self, prompt: str,
                 max_tokens: int = 512,
                 temperature: float = 0.7,
                 top_p: float = 0.9,
                 stream: bool = False) -> Union[str, Generator[str, None, None]]:
        """Generate text using Phi-2

        Args:
            prompt: Input prompt
            max_tokens: Maximum number of tokens to generate
            temperature: Sampling temperature
            top_p: Top-p sampling parameter
            stream: Whether to stream the output

        Returns:
            Generated text or generator if streaming
        """
        try:
            if stream:
                return self._generate_stream(prompt, max_tokens, temperature, top_p)
            else:
                output = self.llm(
                    prompt,
                    max_tokens=max_tokens,
                    temperature=temperature,
                    top_p=top_p,
                    echo=False
                )
                return output["choices"][0]["text"]
        except Exception as e:
            print(f"Error generating text: {str(e)}")
            return "Error: Could not generate response."

    def _generate_stream(self, prompt: str,
                         max_tokens: int = 512,
                         temperature: float = 0.7,
                         top_p: float = 0.9) -> Generator[str, None, None]:
        """Stream text generation using Phi-2

        Args:
            prompt: Input prompt
            max_tokens: Maximum number of tokens to generate
            temperature: Sampling temperature
            top_p: Top-p sampling parameter

        Yields:
            Generated text tokens
        """
        response = ""
        for output in self.llm(
            prompt,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            echo=False,
            stream=True
        ):
            token = output["choices"][0]["text"]
            response += token
            yield response

# === RAG SYSTEM ===
class RAGSystem:
    def __init__(self, pdf_processor: PDFProcessor,
                 vector_db: VectorDBManager,
                 model: Phi2Model):
        """Initialize RAG system

        Args:
            pdf_processor: PDF processor instance
            vector_db: Vector database manager instance
            model: LLM model instance
        """
        self.pdf_processor = pdf_processor
        self.vector_db = vector_db
        self.model = model
        self.query_expander = QueryExpander(model)
        self.is_initialized = False

    def process_documents(self) -> bool:
        """Process all documents and create vector database
        
        Returns:
            True if successful, False otherwise
        """
        try:
            # Process PDFs
            chunks = self.pdf_processor.process_all_pdfs()
            if not chunks:
                print("No chunks were extracted from PDFs")
                return False
                
            print(f"Total chunks extracted: {len(chunks)}")

            # Create vector database
            print("Creating vector database...")
            self.vector_db.create_vector_db(chunks)
            
            # Verify success
            if self.vector_db.vectordb is None:
                print("Failed to create vector database")
                return False
                
            # Set initialization flag
            self.is_initialized = True
            return True
            
        except Exception as e:
            print(f"Error processing documents: {str(e)}")
            print(traceback.format_exc())
            return False

    def generate_prompt(self, query: str, contexts: List[str]) -> str:
        """Generate prompt for the LLM with better instructions

        Args:
            query: User query
            contexts: Retrieved contexts

        Returns:
            Formatted prompt
        """
        # Format contexts with numbering for better reference
        formatted_contexts = ""
        for i, context in enumerate(contexts):
            formatted_contexts += f"[CONTEXT {i+1}]\n{context}\n\n"

        # Create prompt with better instructions
        prompt = f"""You are an AI assistant that answers questions based on the provided context information.

User Query: {query}

Below are relevant passages from documents that might help answer the query:

{formatted_contexts}

Using ONLY the information provided in the context above, provide a comprehensive answer to the user's query. 
If the provided context doesn't contain relevant information to answer the query, clearly state: "I don't have enough information in the provided context to answer this question."

Do not use any prior knowledge that is not contained in the provided context.
If quoting from the context, mention the source document and page number.
Organize your answer in a clear, coherent manner.

Answer:"""
        return prompt

    def answer_query(self, query: str, k: int = 5, max_tokens: int = 512,
                 temperature: float = 0.7, stream: bool = False) -> Union[str, Generator[str, None, None]]:
        """Answer a query using RAG with query expansion
        
        Args:
            query: User query
            k: Number of contexts to retrieve
            max_tokens: Maximum number of tokens to generate
            temperature: Temperature for generation
            stream: Whether to stream the output
            
        Returns:
            Answer text or generator if streaming
        """
        # Check if system is initialized
        if not self.is_initialized or self.vector_db.vectordb is None:
            return "Error: Documents have not been processed yet. Please process documents first."

        try:
            # Expand query for better retrieval
            expanded_query = self.query_expander.expand_query(query)
            print(f"Expanded query: {expanded_query}")
            
            # Retrieve relevant contexts using hybrid search
            contexts = self.vector_db.hybrid_search(expanded_query, k=k)

            if not contexts:
                return "No relevant information found in the documents. Please try a different query or check if documents were processed correctly."

            # Generate prompt with improved instructions
            prompt = self.generate_prompt(query, contexts)

            # Generate answer
            return self.model.generate(
                prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=stream
            )
        except Exception as e:
            print(f"Error answering query: {str(e)}")
            print(traceback.format_exc())
            return f"Error processing your query: {str(e)}"

# === GRADIO INTERFACE ===
class RAGInterface:
    def __init__(self, rag_system: RAGSystem):
        """Initialize Gradio interface

        Args:
            rag_system: RAG system instance
        """
        self.rag_system = rag_system
        self.interface = None
        self.is_processing = False

    def upload_file(self, files):
        """Upload PDF files"""
        try:
            os.makedirs("pdfs", exist_ok=True)
            uploaded_files = []

            for file in files:
                destination = os.path.join("pdfs", os.path.basename(file.name))
                shutil.copy(file.name, destination)
                uploaded_files.append(os.path.basename(file.name))

            # Verify files exist in the directory
            pdf_files = [f for f in os.listdir("pdfs") if f.lower().endswith('.pdf')]
            
            if not pdf_files:
                return "No PDF files were uploaded successfully."
                
            return f"Successfully uploaded {len(uploaded_files)} files: {', '.join(uploaded_files)}"
        except Exception as e:
            return f"Error uploading files: {str(e)}"

    def process_documents(self):
        """Process all documents

        Returns:
            Status message
        """
        if self.is_processing:
            return "Document processing is already in progress. Please wait."
            
        try:
            self.is_processing = True
            start_time = time.time()
            
            success = self.rag_system.process_documents()
            
            elapsed = time.time() - start_time
            self.is_processing = False
            
            if success:
                return f"Documents processed successfully in {elapsed:.2f} seconds."
            else:
                return "Failed to process documents. Check the logs for more information."
        except Exception as e:
            self.is_processing = False
            return f"Error processing documents: {str(e)}"

    def answer_query(self, query, k, max_tokens, temperature):
        """Answer a query

        Args:
            query: User query
            k: Number of contexts to retrieve
            max_tokens: Maximum number of tokens to generate
            temperature: Sampling temperature

        Returns:
            Answer
        """
        if not query.strip():
            return "Please enter a question."
            
        try:
            return self.rag_system.answer_query(
                query,
                k=k,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=False
            )
        except Exception as e:
            return f"Error answering query: {str(e)}"

    def answer_query_stream(self, query, k, max_tokens, temperature):
        """Stream answer to a query

        Args:
            query: User query
            k: Number of contexts to retrieve
            max_tokens: Maximum number of tokens to generate
            temperature: Sampling temperature

        Yields:
            Generated text
        """
        if not query.strip():
            yield "Please enter a question."
            return
            
        try:
            yield from self.rag_system.answer_query(
                query,
                k=k,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=True
            )
        except Exception as e:
            yield f"Error answering query: {str(e)}"

    def create_interface(self):
        """Create Gradio interface"""
        with gr.Blocks(title="PDF RAG System") as interface:
            gr.Markdown("# PDF RAG System with Phi-2")
            gr.Markdown("Upload your PDF documents, process them, and ask questions to get answers based on the content.")

            with gr.Tab("Upload & Process"):
                with gr.Row():
                    pdf_files = gr.File(
                        file_count="multiple",
                        label="Upload PDF Files",
                        file_types=[".pdf"]
                    )
                    upload_button = gr.Button("Upload", variant="primary")

                upload_output = gr.Textbox(label="Upload Status", lines=2)
                upload_button.click(self.upload_file, inputs=[pdf_files], outputs=upload_output)

                process_button = gr.Button("Process Documents", variant="primary")
                process_output = gr.Textbox(label="Processing Status", lines=2)
                process_button.click(self.process_documents, inputs=[], outputs=process_output)

            with gr.Tab("Query"):
                with gr.Row():
                    with gr.Column():
                        query_input = gr.Textbox(
                            label="Question", 
                            lines=3, 
                            placeholder="Ask a question about your documents..."
                        )
                        with gr.Row():
                            k_slider = gr.Slider(
                                minimum=1, 
                                maximum=10, 
                                value=3, 
                                step=1, 
                                label="Number of Contexts"
                            )
                            max_tokens_slider = gr.Slider(
                                minimum=100, 
                                maximum=800, 
                                value=400, 
                                step=50, 
                                label="Max Tokens"
                            )
                            temperature_slider = gr.Slider(
                                minimum=0.1, 
                                maximum=1.0,value=0.7, 
                                step=0.1, 
                                label="Temperature"
                            )
                        submit_button = gr.Button("Submit", variant="primary")
                        
                    answer_output = gr.Textbox(label="Answer", lines=10)
                
                submit_button.click(
                    self.answer_query,
                    inputs=[query_input, k_slider, max_tokens_slider, temperature_slider],
                    outputs=answer_output
                )
                
                # Add streaming capability
                stream_button = gr.Button("Submit (Streaming)", variant="secondary")
                stream_button.click(
                    self.answer_query_stream,
                    inputs=[query_input, k_slider, max_tokens_slider, temperature_slider],
                    outputs=answer_output
                )
                
            gr.Markdown("""
            ## Instructions
            1. Upload PDF files in the 'Upload & Process' tab.
            2. Click the 'Process Documents' button to extract and index content.
            3. Switch to the 'Query' tab to ask questions about your documents.
            4. Adjust parameters as needed:
               - Number of Contexts: More contexts provide more information but may be less focused.
               - Max Tokens: Controls the length of the response.
               - Temperature: Lower values (0.1-0.5) give more focused answers, higher values (0.6-1.0) give more creative answers.
            """)
            
        self.interface = interface
        return interface

    def launch(self, **kwargs):
        """Launch the Gradio interface"""
        if self.interface is None:
            self.create_interface()
        self.interface.launch(**kwargs)

# === MAIN APPLICATION ===
def main():
    """Main function to set up and launch the application"""
    try:
        # Initialize components
        pdf_processor = PDFProcessor(pdf_dir="pdfs")
        vector_db = VectorDBManager()
        phi2_model = Phi2Model()
        
        # Initialize RAG system
        rag_system = RAGSystem(pdf_processor, vector_db, phi2_model)
        
        # Create interface
        interface = RAGInterface(rag_system)
        
        # Launch application
        interface.launch(share=True)
        
    except Exception as e:
        print(f"Error initializing application: {str(e)}")
        print(traceback.format_exc())

if __name__ == "__main__":
    main()