import gradio as gr
import os
import pandas as pd
from typing import List, Dict, Union, Tuple
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    CharacterTextSplitter,
    TokenTextSplitter
)
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain_community.vectorstores import FAISS, Chroma, Qdrant
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import ConversationalRetrievalChain
from langchain_huggingface import HuggingFaceEndpoint
from langchain.docstore.document import Document

# Configuration
list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]
api_token = os.getenv("HF_TOKEN")

CHUNK_SIZES = {
    "small": {"recursive": 512, "fixed": 512, "token": 256},
    "medium": {"recursive": 1024, "fixed": 1024, "token": 512}
}

def is_valid_file(file_path: str) -> bool:
    """Validate if the file extension is supported"""
    allowed_extensions = {'.pdf', '.xlsx', '.xls'}
    file_extension = os.path.splitext(file_path.lower())[1]
    return file_extension in allowed_extensions

def process_excel_to_documents(excel_file: str) -> List[Document]:
    """Convert Excel data to Document objects for vector store with enhanced handling."""
    documents = []
    
    try:
        # Load all sheets from the Excel file
        excel_data = pd.read_excel(excel_file, sheet_name=None)  # Load all sheets into a dictionary
        
        for sheet_name, df in excel_data.items():
            # Ensure headers are flattened
            df.columns = ["_".join(map(str, col)) if isinstance(col, tuple) else str(col) for col in df.columns]
            
            # Fill missing data from merged cells
            df = df.fillna(method='ffill').fillna(method='bfill')  # Forward and backward fill
            
            # Iterate over rows to create Document objects
            for idx, row in df.iterrows():
                content = "\n".join([f"{col}: {val}" for col, val in row.items()])
                metadata = {
                    "source": "excel",
                    "row": idx,
                    "sheet_name": sheet_name,
                }
                doc = Document(page_content=content, metadata=metadata)
                documents.append(doc)
    except Exception as e:
        print(f"Error processing Excel file: {str(e)}")
    
    return documents


def get_text_splitter(strategy: str, chunk_size: int = 1024, chunk_overlap: int = 64):
    """Get the appropriate text splitter based on strategy"""
    splitters = {
        "recursive": RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        ),
        "fixed": CharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        ),
        "token": TokenTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=chunk_overlap
        )
    }
    return splitters.get(strategy)

def load_doc(list_file_path: List[str], splitting_strategy: str, chunk_size: str) -> List[Document]:
    """Load and process documents from various file types"""
    chunk_size_value = CHUNK_SIZES[chunk_size][splitting_strategy]
    all_documents = []
    
    for file_path in list_file_path:
        if not is_valid_file(file_path):
            continue
            
        try:
            if file_path.lower().endswith('.pdf'):
                loader = PyPDFLoader(file_path)
                pages = loader.load()
                all_documents.extend(pages)
            elif file_path.lower().endswith(('.xlsx', '.xls')):
                excel_docs = process_excel_to_documents(file_path)
                all_documents.extend(excel_docs)
        except Exception as e:
            print(f"Error processing file {file_path}: {str(e)}")
            continue
    
    if not all_documents:
        return []
        
    text_splitter = get_text_splitter(splitting_strategy, chunk_size_value)
    doc_splits = text_splitter.split_documents(all_documents)
    return doc_splits

def create_db(splits, db_choice: str = "faiss"):
    """Create vector database from document splits"""
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-mpnet-base-v2"  # Explicitly specify model
    )
    db_creators = {
        "faiss": lambda: FAISS.from_documents(splits, embeddings),
        "chroma": lambda: Chroma.from_documents(splits, embeddings),
        "qdrant": lambda: Qdrant.from_documents(
            splits,
            embeddings,
            location=":memory:",
            collection_name="docs"
        )
    }
    return db_creators[db_choice]()

def initialize_database(list_file_obj, splitting_strategy, chunk_size, db_choice, progress=gr.Progress()) -> Tuple[Union[FAISS, Chroma, Qdrant, None], str]:
    """Initialize vector database with error handling"""
    try:
        if not list_file_obj:
            return None, "No files uploaded. Please upload PDF or Excel documents."
            
        list_file_path = []
        for file_obj in list_file_obj:
            if file_obj is None:
                continue
            if not is_valid_file(file_obj.name):
                return None, f"Invalid file type for {file_obj.name}. Please upload only PDF or Excel files."
            list_file_path.append(file_obj.name)
            
        if not list_file_path:
            return None, "No valid files found. Please upload documents."
            
        doc_splits = load_doc(list_file_path, splitting_strategy, chunk_size)
        if not doc_splits:
            return None, "No content extracted from documents."
            
        vector_db = create_db(doc_splits, db_choice)
        return vector_db, f"Database created successfully using {splitting_strategy} splitting and {db_choice} vector database!"
        
    except Exception as e:
        return None, f"Error creating database: {str(e)}"

def initialize_llmchain(llm_choice, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    """Initialize LLM chain with error handling"""
    try:
        if vector_db is None:
            return None, "Please create vector database first."
            
        llm_model = list_llm[llm_choice]
        
        llm = HuggingFaceEndpoint(
            repo_id=llm_model,
            huggingfacehub_api_token=api_token,
            temperature=temperature,
            max_new_tokens=max_tokens,
            top_k=top_k
        )
        
        memory = ConversationBufferMemory(
            memory_key="chat_history",
            output_key='answer',
            return_messages=True
        )

        retriever = vector_db.as_retriever()
        qa_chain = ConversationalRetrievalChain.from_llm(
            llm,
            retriever=retriever,
            memory=memory,
            return_source_documents=True
        )
        return qa_chain, "LLM initialized successfully!"
        
    except Exception as e:
        return None, f"Error initializing LLM: {str(e)}"

def format_excel_source(source_doc):
    """Format excel source documents for display"""
    if source_doc.metadata.get('source') == 'excel':
        return f"Excel Row {source_doc.metadata['row']}: {source_doc.page_content}"
    return source_doc.page_content

def conversation(qa_chain, message, history):
    """Handle conversation with error handling and source formatting"""
    if qa_chain is None:
        return (
            None,
            gr.update(value=""),
            history + [("Error", "Please initialize the LLM first.")],
            "", 0, "", 0, "", 0
        )
    
    try:
        response = qa_chain.invoke({
            "question": message,
            "chat_history": [(hist[0], hist[1]) for hist in history]
        })
        
        response_answer = response["answer"]
        if "Helpful Answer:" in response_answer:
            response_answer = response_answer.split("Helpful Answer:")[-1]
        
        sources = response["source_documents"][:3]
        source_contents = []
        source_pages = []
        
        for source in sources:
            formatted_content = format_excel_source(source)
            source_contents.append(formatted_content.strip())
            source_pages.append(source.metadata.get("page", 0) + 1 if "page" in source.metadata else 0)
        
        while len(source_contents) < 3:
            source_contents.append("")
            source_pages.append(0)
        
        return (
            qa_chain,
            gr.update(value=""),
            history + [(message, response_answer)],
            source_contents[0],
            source_pages[0],
            source_contents[1],
            source_pages[1],
            source_contents[2],
            source_pages[2]
        )
    except Exception as e:
        return (
            qa_chain,
            gr.update(value=""),
            history + [(message, f"Error: {str(e)}")],
            "", 0, "", 0, "", 0
        )

def demo():
    """Create and launch the Gradio interface"""
    with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue="sky")) as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        
        gr.HTML("<center><h1>RAG PDF & Excel Chatbot</h1></center>")
        
        with gr.Column(scale=86):
            gr.Markdown("<b>Step 1 - Configure and Initialize RAG Pipeline</b>")
            with gr.Row():
                
                document = gr.Files(
                    height=300,
                    file_count="multiple",
                    file_types=[".pdf", ".xlsx", ".xls"],
                    interactive=True,
                    label="Upload PDF or Excel documents"
                )
            
            with gr.Row():
                splitting_strategy = gr.Radio(
                    ["recursive", "fixed", "token"],
                    label="Text Splitting Strategy",
                    value="recursive"
                )
                db_choice = gr.Radio(
                    ["faiss", "chroma", "qdrant"],
                    label="Vector Database",
                    value="faiss"
                )
                chunk_size = gr.Radio(
                    ["small", "medium"],
                    label="Chunk Size",
                    value="medium"
                )
            
            with gr.Row():
                db_btn = gr.Button("Create vector database")
                db_progress = gr.Textbox(
                    value="Not initialized",
                    show_label=False
                )
            
            gr.Markdown("<b>Step 2 - Configure LLM</b>")
            with gr.Row():
                llm_choice = gr.Radio(
                    list_llm_simple,
                    label="Available LLMs",
                    value=list_llm_simple[0],
                    type="index"
                )
            
            with gr.Row():
                with gr.Accordion("LLM Parameters", open=False):
                    temperature = gr.Slider(
                        minimum=0.01,
                        maximum=1.0,
                        value=0.5,
                        step=0.1,
                        label="Temperature"
                    )
                    max_tokens = gr.Slider(
                        minimum=128,
                        maximum=4096,
                        value=2048,
                        step=128,
                        label="Max Tokens"
                    )
                    top_k = gr.Slider(
                        minimum=1,
                        maximum=10,
                        value=3,
                        step=1,
                        label="Top K"
                    )
            
            with gr.Row():
                init_llm_btn = gr.Button("Initialize LLM")
                llm_progress = gr.Textbox(
                    value="Not initialized",
                    show_label=False
                )
        
        with gr.Column(scale=200):
            gr.Markdown("<b>Step 3 - Chat with Documents</b>")
            chatbot = gr.Chatbot(height=505)
            
            with gr.Accordion("Source References", open=False):
                with gr.Row():
                    source1 = gr.Textbox(label="Source 1", lines=2)
                    page1 = gr.Number(label="Page")
                with gr.Row():
                    source2 = gr.Textbox(label="Source 2", lines=2)
                    page2 = gr.Number(label="Page")
                with gr.Row():
                    source3 = gr.Textbox(label="Source 3", lines=2)
                    page3 = gr.Number(label="Page")
            
            with gr.Row():
                msg = gr.Textbox(
                    placeholder="Ask a question",
                    show_label=False,
                    interactive=False
                )
            with gr.Row():
                submit_btn = gr.Button("Submit")
                clear_btn = gr.ClearButton(
                    [msg, chatbot],
                    value="Clear Chat"
                )

        # Event handlers
        db_btn.click(
        initialize_database,
        inputs=[document, splitting_strategy, chunk_size, db_choice],
        outputs=[vector_db, db_progress]
        ).then(
            # Fix: Check if vector_db exists instead of trying to index it
            lambda x: gr.update(interactive=True) if x is not None else gr.update(interactive=False),
            inputs=[vector_db],
            outputs=[init_llm_btn]
        )
    
        init_llm_btn.click(
            initialize_llmchain,
            inputs=[llm_choice, temperature, max_tokens, top_k, vector_db],
            outputs=[qa_chain, llm_progress]
        ).then(
            # Fix: Check if qa_chain exists instead of trying to index it
            lambda x: gr.update(interactive=True) if x is not None else gr.update(interactive=False),
            inputs=[qa_chain],
            outputs=[msg]
        )
        
        msg.submit(
            conversation,
            inputs=[qa_chain, msg, chatbot],
            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
        )
        
        submit_btn.click(
            conversation,
            inputs=[qa_chain, msg, chatbot],
            outputs=[qa_chain, msg, chatbot, source1, page1, source2, page2, source3, page3]
        )

        clear_btn.click(
            lambda: [None, "", 0, "", 0, "", 0],
            outputs=[chatbot, source1, page1, source2, page2, source3, page3]
        )

    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()